Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A ‘master regulator’ transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This ‘master regulator’ transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option.We present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one.http://www.somnathdatta.org/software.In the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found in We have developed a screening method of identifying the ‘master regulator’ transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.The online version of this article (doi:10.1186/s12859-017-1499-x) contains supplementary material, which is available to authorized users. Through several scientific findings, it has been suggested that cancer is mainly caused by the mutations in certain genes. So, for effective treatment of cancer, identification of these mutated genes (oncogenes) is very essential. Detailed studies of different cancer datasets often lead to identification of several oncogenes which are directly or indirectly responsible for development and progression of cancer. It is a very challenging task to target and individually study all of these oncogenes as they are large in number. One way to overcome this approach is to group the proteins and genes belonging to the same pathway . These gIn the recent past, many studies have identified a transcription factor or a group of transcription factors as the driving force behind the development of a biological or disease process –12. In oIn current literature there is a lack of appropriate statistical methods which use a single data source for accurate identification of such a master regulator among a set of identified transcription factors. In this article, we develop a novel two-step statistical approach to test for the existence of a master regulator transcription factor and for subsequent identification of the master regulator, if it exists, from gene expression data alone. The rest of the article is organized as follows: In the Methods section, we develop our test statistic and describe its underlying motivations for identifying the master regulator transcription factor. In the Results section, we describe a set of simulation experiments to evaluate the performance of our method and also apply our proposed method to two real datasets. Finally, we conclude with a discussion on the utility of our proposed method.We first discuss the biological considerations that motivated the development of our test statistic. We then provide the methodology to formulate the test statistic and use it for the identification of the regulatory circuit of the transcription factors and genes. Finally, we identify the master transcription regulator at the top of the regulation hierarchy.Important biological processes can have multiple layers of regulation and control. A transcription factor is known to control not only genes but also other transcription factors. As discussed before in the Background section, usually there is a hierarchical structure in the regulation of the transcription factors so that the master regulator controls most of the regulatory activities of the other transcription factors and the associated genes. In this article, we aim to identify the master regulator transcription factor which is at the top of the hierarchy for better understanding of the associated disease process. A toy example is shown in Fig. TF1, TF2, TF3and TF4 denote the transcription factors and g1, g2, …, g12 denote the set of genes. Suppose TF1 directly regulates five of the genes, which are g1, g2, g3, g4 and g5, and also all the other three transcription factors, TF2, TF3 and TF4. The transcription factor TF2 regulates the genes g6, g7 and g8. Similarly, the transcription factor TF3 regulates the genes g9 and g10 and finally, the transcription factor TF4 regulates the genes g11 and g12. In this example, there exists a hierarchical structure with three layers. We have TF1 at the top of the hierarchical structure as it directly or indirectly regulates the other transcription factors and the genes. So, TF1 is considered to be the first layer of the hierarchy. Now, TF1 directly regulates the other transcription factors, TF2, TF3 and TF4 and the genes g1, g2, …, g5. So, TF2, TF3 and TF4 and g1, g2, …, g5 are considered to be at the second layer of the hierarchy. TF1 regulates the genes g6, g7, …, g12 indirectly through the transcription factors TF2, TF3 and TF4. Thus, the genes g6, g7, …, g12 form the third layer of the hierarchy. In this example, TF1 directly or indirectly regulates all the layers of the hierarchy and is not under the regulatory influence of any other gene or transcription factor. Therefore, according to the definition, TF1 can be considered as the master regulator transcription factor.In Fig. Here, in this article, we attempt to develop a test that can check if there exists any transcription factor that acts as a master regulator in a genome, and identify such a master regulator if present. The details of our proposed method are given in the next section.M denote the total number of transcription factors present in a genome. Let the transcription factors be denoted by TF1, TF2, …, TFM. Let the genes, which are not transcription factors, be denoted by g1, g2, …, gN, where N denotes the total number of such genes. So, in total, we have expression data on M + N genes. Let us assume that there are two groups of subjects, for example, the case group (the disease group) and the control group (non-disease group). Let there be r1 subjects in the case group and r2 subjects in the control group. So, in other words, we have two groups of subjects with expression levels for M + N features in each group. It is well known that the genes including the transcription factors are expressed differently in the two groups. Additionally, they are connected with one another differently in the networks of the two groups. There are methods to reverse engineer the networks of genes with association measures such as correlations, partial correlations and partial least squares regression scores [Let n scores , 37. Then scores .N. Here, TFi, i = 1, 2, …, M, and the gene g in the case and control groups, respectively. There are several choices for connectivity scores such as Pearson’s correlation scores, partial correlation scores, partial least square based association scores. In this article, we use the Pearson’s correlation scores as connectivity scores. So, here, the xi′s give us an idea about the magnitude of the differential regulation of the transcription factors on the genes between the case and control groups.Since, it is believed that the master regulator maximally controls the other transcription factors as well as the associated genes; it is important to find the regulatory network among the transcription factors and also the degree of regulation of all the transcription factors on the genes. We first measure the degree of regulation of the transcription factors on the genes. The degree of regulation of a transcription factor on the genes is measured by the change in connectivity of the genes it regulates in the two networks. In other words, we find how the connectivity of a transcription factor with the genes differs between the subjects in case and control groups. For this, we estimate the change in connectivity of a transcription factor with the genes in the two groups of samples using connectivity scores of the given transcription factor with all the genes in the case group with that in the control group. The difference in connectivity is measured using the following statistic :1\documej, k), let yjk denote the absolute value of the Pearson’s correlation coefficient score between the transcription factors TFj and TFk; j, k = 1, 2, …, M, where yjj = 1. Note that this calculation is done by pooling the data from both the groups.Next, we find the regulatory structure among the transcription factors. For this, we measure the association between each pair of transcription factors using the Pearson’s correlation coefficient scores between them. For each pair ; and a measure of the association of TFj with all the transcription factors .At this stage, for a transcription factor M transcription factors is the same with the differential connectivity of genes in the two groups that they control. In other words, the rank order of the amount of differential connectivity of a transcription factor with other genes it controls is in line  with its ordered connectivity with the master regulator. Therefore, we consider two ranked lists. One that ranks the transcription factors by the amount of differential connectivity of the genes it controls and another that puts the master regulator in the first position and ranks the remaining transcription factors by their correlation with the master regulator. We evaluate the concordance of these two sets of ranks using a statistical measure which is described in next paragraph. Since we do not know a priori the identity of the master regulator/s, we maximize this measure of concordance over the set of all transcription factors in candidacy for playing the role of the master regulator. In case the maximal concordance is statistically significant, we conclude that there is a master regulator. In addition, we declare the transcription factor for which this concordance measure is maximal amongst all transcription factors to be the master regulator.We argue that the degree of change in connectivity of the genes in the two networks is controlled by the transcription factors which are correlated amongst themselves in a hierarchical manner. That is, the hierarchical regulation pattern (as measured by the rank order) among the Kj for each transcription factor TFj that is in candidacy for the master regulator ; 1 ≤ j ≤ M, in the following way:x1, yj1), , …, , …, . Note that xi denotes the average difference in connectivity of transcription factor TFi between the two groups, and yji is the absolute correlation of transcription factor TFi with transcription factor TFj. This test statistic Kjbelow conveys whether the differential connectivity of the genes with the transcription factor TFj in the two experimental groups is concordant with the correlations of the transcription factor TFjwith all other transcription factors. In other words, Kj measures whether the differential connectivity is concordant with the hierarchical regulation of the transcription factors amongst themselves. The Kendall’s rank correlation coefficient test statistic for the transcription factor TFj is given as:nc,j = number of concordant pairs in the above paired list,We calculate the Kendall’s rank correlation coefficient test statistic given by  below band,j = number of discordant pairs in the above paired list,TFj.We construct a concordance statistic x and the correlations with TFj are non concordant versus the alternative hypothesis that they are concordant.2)We repeat step 1) for all such transcription factors, so that we have a concordance test statistic for each of the transcription factors which is a potential master regulator.This statistic can be used to test the null hypothesis that the two sets of ranks produced by differential connectivity Kj, over the set of all transcription factors. So, we define K as the maximum of the statistics given in  based procedure as the sampling distribution of K is not tractable. This will calculate the p-value or the observed level of significance of the value of test statistic K calculated in  in the case group, and N in the control group.We assume that (log-transformed) expression values for M independent random variables Vi from N; i = 1, 2, …, M, that are also independent of TF1.We also generate M − 1 transcription factors in such a way that Corr > Corr , where Corr denotes the correlation between the transcription factors TFj and TFk. One way of achieving this is to simulate the remaining M − 1 transcription factors TFi ; i ≠ 1 as follows:ρi′s are decreasing in i, i ≠ 1.We want to generate all the transcription factors in such a way that there exists a hierarchical regulatory pattern among them. In other words, we want to generate the remaining In this case, the correlation structures among all the transcription factors are given by:TFi ; i = 1, 2, …, M, regulates mi genes. Here, N = m1 + m2 + … + mM. The genes, TF1 alone, are generated as given below:ϵj and ϵj′ are independent and identically distributed (i.i.d) as N, and γ1 and γ2 are real numbers.The next step is to generate the genes. We assume that each of the transcription factors TF1 and the genes gk , k = 1, 2, …, m1 is given byHere, the correlation between the transcription factor M − 1 transcription factors TFi;  i ≠ 1, are generated as follows:The genes, regulated by the remaining ϵj and ϵj′ are i.i.d N and ri1 and ri2 are real numbers, i ≠ 1.where, TFi;  i ≠ 1 and the genes regulated by that transcription factor is given byIn this case, the correlation between a transcription factor TFi;  i ≠ 1 and the genes which are not regulated by that transcription factors are zero i.e. Corr = 0 for k ≠ mi − 1 + 1, …, mi. Furthermore, Corr = 0 ; k ≠ 1, 2, …, m1.Also, the correlations between a transcription factor We calculate the size and power of our test in the following sections.We consider Recall that, the null hypothesis of interest is that the rank order of the transcription factors based on their differential connectivity with the genes is not statistically concordant with their rank order based on their correlations with the master regulator. So, the null situation can be created by assuming that there exists a hierarchical regulatory pattern among the transcription factors but there is no differential regulation of the genes in the two experimental groups due to the transcription factors. Hence, there is no such master regulator.ρis to be decreasing in i , i = 2, 3, …, M and choose γ1 = γ2 and ri1 = ri2, i = 2, 3, …, M. The decreasing nature of ρis ensures that there exists a hierarchical regulatory pattern among the transcription factors. γ1 = γ2 and ri1 = ri2, i = 2, 3, …, M ensure that the associations of the transcription factors with the genes remain the same in the two groups i.e. there is no differential connectivities of the transcription factors with the genes between the two groups. We generate r1 samples for the case group and r2 samples for the control group using the above described scheme. We calculate the value of our test statistic, denoted by K, using Eq.  = γ1 = γ2 = 0.5r12 = r22 = 0.45 and ri1 = ri2 = ri − 1)1 of TF1 with the genes, regulated by it, are greater in case group than that in the control group. Similarly, ri1 > ri2 ensures that the connectivity of TFi with the genes, regulated by it, are greater in case group than that in the control group, i = 2, 3, …, 10. Also, we assume ρis to be decreasing in i , i = 2, 3, …, M, so that there is a hierarchical regulatory structure among the transcription factors, TF1 being at the top of the hierarchy. We follow the same steps in calculating the p-value as we did for size calculation in the previous section. We consider the same choices for M, N, r1, r2, B, μ, ϑ, ρ and mi ; i = 1, 2, …, 10 as we consider for size calculation. In particular, we choose γ2 = 0.5; r12 = 0.45 and ri1 = ri − 1)1ri1 , i = 2, 3, …, 10 where 0 ≤ δ ≤ 1. These choices of ri2 , i = 2, 3, …, 10; ensure that increase in the value of δ also increase the difference between ri1 and ri2 , i = 2, 3, …, 10. In other words, the differential regulations of the transcription factors on the genes between the two groups increase as δ increases.To calculate the power of our test, we generate a data under the alternative hypothesis γ1, we consider the following relation: γ1 = γ2 + δ(r12 − r22), which impliesFor the choice of γ1 = γ2 + δ2r12, 0 ≤ δ ≤ 1. This choice of γ1 ensures that increase in the value of δ also increase the difference between γ1 and γ2. We draw the power curve for different choices of δ, as shown in Fig. δ) of the genes with the transcription factors between the two groups increase. The power curve starts from 3.2% at δ = 0 (no difference in the connectivity of the genes with the transcription factors in the two groups) and reaches its maximum of 100% at δ = 1 (maximum difference in the connectivity of the genes with the transcription factors in the two groups). The power reaches over 80% with a moderate choice of δ = 0.6. Therefore, we can say that our proposed method is a valid test  that performs reasonably well (power reaching 100%) in identifying a significant concordance in the differential connectivity of the genes with the transcription factors and the connectivity of a transcription factor with master regulator, if one exists.From Fig. r1 = 100, r2 = 70; r1 = 50, r2 = 40; and r1 = r2 = 50, representing reduced sample sizes and unequal sample sizes in each treatment group. Overall, from our analyses with different choices of sample size, we find that the power of our test is increasing with increase in the sample size as well as an increase in the differential connectivity of the genes with the transcription factors in the two groups. Details of the variation of the power with sample size can be found in Additional file δ, 0 ≤ δ ≤ 1.We also consider several other choices of the sample sizes in each of the two groups (case and control), and calculate the size and draw the power curves for each of the following choices of the sample sizes: In order to check the performance of our test in case there are more than one master regulator transcription factors, we have also studied a simulated scenario where there are two independent master regulator transcription factors regulating two independent sets of genes through transcriptional regulatory networks. Additional file We apply our test statistic, proposed in Methods section, to a human Illumina expression array dataset GSE18684 of androgen regulated gene expression in the LNCaP prostate cancer cell line . It is bp-value of 0.006. Since the p-value is highly significant we conclude that there exists a master regulator transcription factor in the system which is controlling all the other transcription factors and the genes. In order to find the master regulator, we use Eq. . “NFKB” transcription factors are known to be the key regulators of innate immune responses, inflammation, and cell survival , 59. AlsFrom our data, we find that the master regulator “NFKB2” is maximally (negatively) correlated with the transcription factor “PPARGC1A ” with an overall correlation value of -0.76. The correlation of “NFKB2” and “PPARGC1A” is -0.72 in the patients group whereas it is -0.39 in the control group. It is known that “NFKB” directly repress the activity of “PPARGC1A” in cardiac cells. This leads to the increase in glucose oxidation which is observed during pro-inflammatory state .p-value of 0.08 .In this article, we present a novel approach to identify a master regulator transcription factor in a system using only the gene expression profiles of the patients. We consider a simulation setting which validates our approach with a reasonable power in detecting the existence of a master regulator. We have also checked the power of our test in the presence of two independent master regulator transcription factors in the simulation setup. We apply our approach to two human microarray datasets and detect the existence of master regulators in those. In order to check the robustness of our method in experiments not typically falling under the ‘case-control’ category, we have applied our method to an additional dataset, namely, Glioblastoma (GBM) TCGA RNA-seq data . Here weOur method is aimed to identify a single master regulator, as opposed to identifying a group of transcription factors associated with the disease process as in the case of other existing methods. The method can identify multiple master regulator transcription factors if they are individually at the top of hierarchy of the transcription regulation. This is advantageous in anti-cancer drug development processes which initially target the most potential transcription factor associated with the disease and can be used as a potential biomarker. However, there is a scope of further improvement of our proposed method by incorporating important platforms like ChIP-Seq data. From simulation settings, we see that the performance of our method gets better with the increase in the number of patients in each group. So, our method is expected to be more efficient when there is sufficiently large number (around 100) of patients in each group while it may not be very efficient in case the sample size is very small. Although both the data analyzed in this article have much lower number of subjects in each group, our test was still successful in identifying master-regulator transcription factors from the data. One important assumption of our method is that the ranking of the transcription factors on the basis of their differential connectivity of the genes between two experimental conditions is concordant with the hierarchical order of their own regulation. The fulfilment of the above mentioned condition is a key indicator to the existence of a master regulator transcription factor and its subsequent detection through our method. However, it may be possible that in certain situations, although there exists a master regulator transcription factor, there is no such clear cut concordance between it’s regulation on other transcription factors and differential connectivity with the other genes. In such a case, our method may not perform well.We have developed a method of identifying the ‘master regulator’ transcription factor using only the gene expression data. This is advantageous in terms of narrowing down the search space for potential candidate transcription factor biomarkers that can be targeted for drug development of complex diseases. Also, the fact that our method uses only a single data source, e.g. gene expression data, for accurately identifying the master regulator transcription factor makes it very useful in case there is limitation in data sources and data from multiple platforms are not available. In addition to identifying the master regulator our method provides an overview of how the transcription factors regulate the global gene expression profiles and consequently the cell functioning. Additionally, with our method, one can identify many other transcription factors involved in the regulatory roles by reporting the hierarchy amongst them using the rankings of the test statistics values. Overall, we believe that our method will give new insight for efficient identification of potential disease biomarker and therapeutic target in drug development processes.


Shifting embryos from a male-producing temperature (MPT) to a female-producing temperature (FPT) at the beginning of the temperature-sensitive period (TSP) resulted in an increase in aromatase mRNA expression while a shift from FPT to MPT resulted in decreased expression. DNA methylation levels at CpG sites in the promoter of the aromatase gene were high (70–90%) at the beginning of TSP, but decreased in embryos that were incubated at constant FPT and those shifted from MPT to the FPT. This decrease in methylation in the promoter inversely correlated with the expected increase in aromatase expression at the FPT. The active demethylation under the FPT was especially prominent at the CpG site upstream of the gonad-specific TATA box at the beginning of TSP and spread downstream of the gene including exon1 as the gonad development progressed. In embryos incubated at FPT, the promoter region was also labeled by canonical transcriptional activation markers, H3K4me3 and RNA polymerase II. A transcriptional repression marker, H3K27me3, was observed in temperature-shifted gonads of both temperature groups, but was not maintained throughout the development in either group. Our findings suggest that DNA hypomethylation and H3K4me3 modification at the aromatase promoter may be a primary mechanism that releases a transcriptional block of aromatase to initiate a cascade of ovarian differentiation.The environment surrounding the embryos has a profound impact on the developmental process and phenotypic outcomes of the organism. In species with temperature-dependent sex determination, gonadal sex is determined by the incubation temperature of the eggs. A mechanistic link between temperature and transcriptional regulation of developmental genes, however, remains elusive. In this study, we examine the changes in DNA methylation and histone modification patterns of the  Primordial gonads are initially bipotential; namely, having the capacity to develop into either testes or ovaries when they are generated from the coelomic surface of mesonephrous tissue during embryonic development. Differentiation of the embryonic gonads into one of the developmental trajectories  depends on either heritable genetic factors  or the physical and biotic environment . Although gonad determination in mammals and birds is governed by the presence or absence of sex chromosomes, gonadal differentiation in non-mammalian vertebrates is susceptible to various environmental factors, especially temperature –3. For icyp19a1) is an enzyme that irreversibly catalyzes androgens into estrogen in the steroidogenesis pathway. In mammals, the role of aromatase is essential to folliculogenesis and steroid hormone production in adult females but there is little evidence of aromatase involvement in gonad development and differentiation in the fetus . PC. PC21]. 2, and stored at -80°C until the ChIP assay.Freshly harvested gonad tissues in cold PBS were cross-linked by adding 37% formaldehyde to obtain a final working concentration of 1% and incubated at RT for 15 min. The cross-linking reaction was stopped by the addition of 2 M glycine, and the pooled samples were further incubated at RT for 5 min. Resulting tissues were washed three times with cold PBS containing 1 mM PMSF and 1% protease inhibitor and gently disaggregated using a dounce homogenizer into a single cell suspension. Cells were lysed with cell lysis buffer  and incubated on ice for 15 min. After centrifugation, the cell pellets were lysed with nuclear lysis buffer  and incubated on ice for 10 min. The lysates were sonicated 4 x 4 sec each with 4sec intervals at 60% power using a sonicator  on ice. After centrifugation at 14,000 rpm for 10 min at 4°C, the chromatin supernatants were aliquoted into 5 tubes (100 μL each), snap frozen in LN3, 1% SDS) and the remaining chromatin was used for the addition of rabbit IgG as a negative control. The antibody-protein complexes were bound to 50 μL Dynabeads by incubating at RT for 1 hr. The protein-beads complexes were then washed by gentle 15 min rotation at RT for each buffer listed in this order : RIPA , High-salt , LiCl , and TE buffer (x2). Antibody-protein complexes were eluted by gentle rotation with 500 μL elution buffer for 15 min at 65°C. This elution process was repeated twice . The chromatin samples and inputs were reverse cross-linked by incubating at 65°C overnight (< 15 hrs) with 5 M NaCl and 1 μg RNaseA (Thermo Fisher Scientific). The reaction was subsequently treated with buffer containing 20 μg Proteinase K, 5 mM EDTA, and 20 mM Tris-HCl for 1 hr at 45°C. DNA was purified with conventional phenol-chloroform methods and resuspended in 20 μL TE buffer.Dynabeads Protein A (Thermo Fisher Scientific) was prepared by removing the original solution and resuspending them in the original volume with dilution buffer  containing 1 mM PMSF. Previously-frozen chromatin was thawed on ice, suspended in three volumes of dilution buffer, and pre-cleared with the addition of 20 μL of prepared Dynabeads Protein A and gentle rotation for 2 hrs at 4°C. After separating the chromatin from the beads, they were incubated overnight at 4°C with 2–4 μg of one of the following antibodies: rabbit polyclonal H3K4me3 , rabbit polyclonal H3K27me3 , mouse monoclonal RNA Polymerase II CTD repeat , or rabbit polyclonal anti-mouse IgG  as a negative control. These antibodies were previously shown to be compatible with ChIP or ChIP-seq experiments –37 and tRelative enrichment of specific proteins was quantified using the KAPA SYBR FAST qPCR kit (KAPA biosystems) on the ABI ViiA7 real-time PCR System. Two μL of ChIP-isolated DNA was amplified using three sets of qPCR primers that recognize the following regions of the aromatase gene: A) a 5' upstream region including two putative heat shock factor (HSF) binding sites, B) a core promoter region including the TATA box and TSS, and C) a coding region within exon 1 . Primer p-value of less than 0.05 was considered to be statistically significant.All statistical analyses and graphic visualizations were carried out using R . The differences between the averages of the control (constant temperature) and the temperature-shifted groups for the qPCR data were analyzed with the Wilcoxon rank sum test. Differences in the averages of the pyrosequencing data within a stage and across the various stages at each CpG site were tested by the Kruskal-Wallis test by ranks, and by a post-hoc multiple comparison of a pairwise Wilcoxon rank sum test. Two variable measurements from the same individual—aromatase mRNA expression and percentage of DNA methylation at the aromatase gene—were assessed with the Spearman's rank correlation coefficient. The student t-test was performed for statistical differences in enrichment between the protein of interest and IgG background in the ChIP analysis. A aromatase at MPT and FPT respectively throughout embryonic development  as early as stage 17  a 5' upstream region including two putative heat shock factor (HSF) binding sites, B) a core promoter region including the TATA box and TSS, and C) a coding region within exon 1 .In eggs incubated at MPT, H3K4me3 modification was not detected in the aromatase promoter region at embryonic stage 16 and at hatching though a moderate enrichment was observed at the middle of and after TSP   in embryos at MPT; however, no enrichment was observed at the late developmental stages as expected  at the beginning of TSP; this mode of action spreads downstream of the TATA box (CpGII) and exon1 (CpGIII and IV) as the gonad development progresses. In contrast, the role of MPT, which seems to be responsible for maintaining the high levels of methylation throughout development, seems rather passive. This asymmetric change of DNA methylation between two temperatures may indicate a critical mechanistic aspect of the regulation of The temperature shift at the beginning of TSP had an effect on the levels of DNA methylation at CpGI in the early stages, but not the other CpG sites. Instead, alterations to methylation at CpGII, located downstream of the TATA box, and CpGIII and IV, both located in the exon 1, were influenced by the temperature shift much later in development . It suggSox9 exhibits decreased methylation at MPT in differentiating gonads [FoxL2, an ovarian differentiation marker, exhibited indistinguishable levels of DNA methylation in both MPT and FPT in the gonads of red-eared slider turtles during TSP, regardless of the observed temperature-dependent dimorphic mRNA expression of this gene . Chemically-induced ovaries by exogenous estrogen treatment do not seem to follow the female-specific methylation pattern at the aromatase promoter even though the mRNA expression exhibits an expected increase by this treatment in species such as chicken [Other studies in species with TSD demonstrate this temperature-specific change in DNA methylation in other, but not all, genes involved in the process of gonad determination. In the American alligator, a gene promoter of the actively transcribed testicular differentiation marker g gonads . In Eurog gonads . We prev chicken , Europea chicken  and red- chicken . Taken tAs predicted, the histone marker H3K4me3 enriches at the TSS and exon1 regions of the aromatase gene, which is where an active transcription typically occurs in the FPT phenotype groups . The oppH3K4me3 and DNA methylation is often in a mutually exclusive relationship ,44. We oIn the current study we used an antibody against an unmodified RNA polymerase II CTD, which mainly recognizes a paused RNAPII . We initIn chicken, neither differential H3K4me3/H3K27me3 ratio nor RNAPII recruitment between female and male embryonic gonads (day 19) was detected at the gonad-specific aromatase TSS regardless of the sexually dimorphic mRNA expression at this stage . They diS1 FigHSF = putative binding site for heat shock factors. TATA = TATA box. Black arrow = transcription start site. ATG = translation start site. Parenthesis indicates the base pair position relative to the transcription start site counted as +1. CpG pol = G/A polymorphism resulting in loss of a CpG site in some individuals see  section.(TIF)Click here for additional data file.S2 FigEach time point includes data from 8–12 embryos. Data shown with a median value (thick black line), 25th and 75th percentiles (lower and upper boundary of box), and maximum and minimum values (whiskers). Outliers are indicated with open circles. Asterisks indicate statistically significant difference between MPT and FPT groups within a stage . Small squares within the larger graphs are the magnified view of the results from stage 16 and 17. MPT = male-producing temperature, FPT = female-producing temperature. St = embryonic stage.(TIF)Click here for additional data file.S3 FigCorrelation coefficient (r) and p-values were examined by Spearman's rank correlation coefficient. A plot without an r-value was not statistically significant.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes.Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4+ T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C. By integrating promoter capture Hi-C data with genetic associations for five autoimmune diseases, we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach.Within 4 h, activation of CD4Our systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes.The online version of this article (doi:10.1186/s13059-017-1285-0) contains supplementary material, which is available to authorized users. The majority of associated variants lie outside genes  amplification rounds). Biotinylated 120-mer RNA baits targeting both ends of HindIII restriction fragments that overlap Ensembl-annotated promoters within the Ensembl categories of protein-coding, non-coding, antisense, snRNA, miRNA and snoRNA were used to capture targets  = 1.5-4). Equivalent analysis of the genome-wide significant GWAS signals prioritised a median of two genes/region (IQR = 1–3). This suggests that this algorithm might be expected to prioritise at least one gene in about half the genome-wide significant regions input when run on a relevant cell type.Because COGS aggregates over multiple signals, a gene may be prioritised because of many weak signals or few strong signals in interacting regions. To estimate the expected number of prioritised genes for a typical GWAS signal, we considered the subset of 76 input regions with genome-wide significant signals  and variants in PIRs (component 3) using all available tissue interactions for that gene. While components 1 and 2 are fixed for a given gene and trait, the contribution of variants overlapping PIRs varies depending on the tissue context being examined. We use the ratio of the genic score to PIRs score in a similar manner to a Bayes factor to decide whether causal variants contributing to the gene score are more likely to lie within the gene or within its associated PIRs. If a genic location is more likely (gene.score ratio > 3) we iterate and compare if the gene score due to coding variants (component 1) is more likely than for promoter variants (component 2). Similarly, if PIRs are more likely, we compare PIR gene scores for activated vs. non-activated cells. If at any stage no branch is substantially preferred over its competitor (ratio of gene scores < 3), we return the previous set as most likely, otherwise we continue until a single cell state/set is chosen. In this way, we can prioritise genes based on the overall score and label as to a likely mechanism for candidate causal variants.+ T cells were isolated from five healthy donors and activated as described above and were harvested after 0, 2 and 4 h in RLT Plus buffer. Selected donors were heterozygous for all eight group A SNPs and homozygous for group C and F SNPs. Two and three of the donors were homozygous for the group D and E SNP groups, respectively . Sorted cells were either activated for 4 h in culture as described above or resuspended directly in RLT plus buffer post-sort. Total RNA was extracted using Qiagen RNeasy Micro plus kit and cDNA was synthesised using Superscript III reverse transcriptase (Thermo Fisher) according to the manufacturer’s instructions. To perform allele-expression experiments we used a modified version of a previously described method for quantifying methylation in bisulphite sequence data [http://bioinfo.ut.ee/primer3-0.4.0/primer3/) and adaptor sequences were added to the primers (Sigma), shown as lowercase letters . The first-round PCR contains 10 μL of Qiagen multiplex PCR mastermix, 0.5 μL of 10 nmol forward primer, 0.5 μL of 10 nmol reverse primer, 4 μL of cDNA and made up to 20 μL with ultra-pure water. The PCR cycling conditions were 95 °C for 15 min hot start, followed by 30 cycles of the following steps: 95 °C for 30 s, 60 °C for 90 s and 72 °C for 60 s, finishing with a 72 °C for 10-min cycle. The first-round PCR product was cleaned using AmpureXP beads (Beckman Coulter) according to manufacturer’s instructions. To add Illumina sequence compatible ends to the individual first-round PCR amplicons, additional primers were designed to incorporate P1 and A sequences plus sample-specific index sequences in the A primer, through hybridisation to adapter sequence present on the first-round gene-specific primers. Index sequences are as published [Total CD4nce data . A two-sublished . The sechttps://github.com/ollyburren/Methpup) to extract counts of each allele at rs12722495 and rs12244380  to confirm which allele at each SNP was carried on the same chromosome as A2 = rs12722495:C or A1 = rs12722495:T. Allelic imbalance was quantified as the ratio A2/A1 and was averaged across replicates within individuals using a geometric mean. Allelic ratios in cDNA and gDNA were compared using Wilcoxon rank sum tests. p values are shown in Fig. https://github.com/chr1swallace/cd4-pchic/blob/master/ASE/IL2RA-ASE.R.Sequence data were processed using the Methpup package  are from RNA-seq data at the 4-h timepoint. (GZ 278 kb) Gene modules inferred from WGCNA analysis of microarray time-course. Expression fold changes and associated false discovery rates  are prioritised for T1D, CRO and UC. Figure S11. Histograms show the distribution of summed PIR length by gene in non-activated CD4+ T cells (top panel) and TAD length in naive CD4+ T cells. Figure S12.IRF8 and EMC8/COX4I1 on chromosome 16 are prioritised for RA and SLE. Figure S13.AHR on chromosome 7 is prioritised for RA in activated CD4+ T cells. Figure S14. Allelic imbalance in mRNA expression in individuals heterozygous for group A SNPs is confirmed with reporter SNP rs12244380 (IL2RA 3’ UTR). (PDF 4243 kb)Additional file 3: Table S2. Results of differential expression analysis on RNA-seq data. Features are defined in the GTF file in Additional file Additional file 4: Table S3. Baited HindIII fragments used for capture of Hi-C libraries, annotated with Ensembl annotated genes. (GZ 572 kb)Additional file 5: Table S4. PCHi-C interactions called with the CHiCAGO pipeline. Annotation for baited fragments is given in Additional file Additional file 6: Table S5. Summary of GWAS data used. ‘type’ indicates whether the trait was quantitative (QUANT) or case/control (CC). For CC, ‘cases’ and ‘controls’ columns represent the number of individuals included in the study, while for QUANT, the number of individuals is given in the cases column. ‘Category’ indicates broader classes of traits. (XSLX 10 kb)Additional file 7: Table S6a. Results of ImmunoChip fine-mapping by GUESSFM. (GZ 2833 kb)Additional file 8: Table S6b. Results of GWAS summary statistic fine-mapping. (GZ 2833 kb)Additional file 9: Table S7a.https://www.immunobase.org - accessed 06/06/2016), we annotate genes near (within 5 Mb) previously reported disease susceptibility regions, with contextual annotation ‘Closest_Disease_Susceptibility_Region’, ‘Closest_Min_P_Value_Susceptibility_SNP’, ‘Closest_Min_P_Value_Susceptibility_SNP_P_Value’, ‘PIR_Overlaps_Disease_DSR’ indicates that the PIR driving the prioritisation for a gene/disease overlaps an ImmunoBase known disease susceptibility region for that trait. Restricted to the subset of genes with scores > 0.5 that are analysed in this paper. (GZ 37 kb) Autoimmune disease COGS gene prioritisation. Overall COGS gene scores  for each gene and autoimmune disease are shown together with the prioritised category and score associated with that category (COGS_Category_Gene_Score) Fig. . The ‘anAdditional file 10: Table S7b. As above, complete results. (GZ 37 kb)Additional file 11: Table S8a. GTF file with definitions for all Ensembl 75 genomic features plus CD4-specific regulatory regions inferred from chromatin states. These regulatory regions have been named with identifiers containing a CD4R prefix, assigned a regulatory biotype and marked as pertaining to both genomic strands due to their bi-directional transcription potential. (GZ 39807 kb)Additional file 12: Table S8b.+ T cells. (GZ 1551 kb) Whole-genome segmentation of non-activated and activated CD4 T cells into 15 states obtained from a CHROMHMM analysis using ChIP-seq data for activated CD4Additional file 13: Table S8c.+ T cells. (GZ 1520 kb) Whole-genome segmentation of non-activated and activated CD4 T cells into 15 states obtained from a CHROMHMM analysis using ChIP-seq data for non-activated CD4Additional file 14: Table S9. Genotypes for donors in the IL2RA ASE experiment across SNP groups A, C, D, E, F. (XLSX 12 kb)Additional file 15: Table S10. Read counts for each allele at the IL2RA ASE experiment. The column Expt denotes sample id; time, the timepoint ; stim, the condition . (GZ 4 kb)


Many studies have shown that guanine-rich DNA sequences form quadruplex structures (G4) in vitro but there is scarce evidence of guanine quadruplexes in vivo. The majority of potential quadruplex-forming sequences (PQS) are located in transposable elements (TEs), especially close to promoters within long terminal repeats of plant LTR retrotransposons.In order to test the potential effect of G4s on retrotransposon expression, we cloned the long terminal repeats of selected maize LTR retrotransposons upstream of the lacZ reporter gene and measured its transcription and translation in yeast. We found that G4s had an inhibitory effect on translation in vivo since “mutants” (where guanines were replaced by adenines in PQS) showed higher expression levels than wild-types. In parallel, we confirmed by circular dichroism measurements that the selected sequences can indeed adopt G4 conformation in vitro. Analysis of RNA-Seq of polyA RNA in maize seedlings grown in the presence of a G4-stabilizing ligand (NMM) showed both inhibitory as well as stimulatory effects on the transcription of LTR retrotransposons.Our results demonstrate that quadruplex DNA located within long terminal repeats of LTR retrotransposons can be formed in vivo and that it plays a regulatory role in the LTR retrotransposon life-cycle, thus also affecting genome dynamics.The online version of this article (10.1186/s12864-018-4563-7) contains supplementary material, which is available to authorized users. Guanine-rich sequence motifs with four closely spaced runs of Gs are able to form a four-stranded structure known as a G-quadruplex . QuadruMany studies have shown that guanine-rich sequences form quadruplex DNA or RNA in vitro but solid experimental evidence of quadruplex formation in vivo has been gathered only recently  althWhile in general the biggest focus is on genic and telomeric G4 motifs, the majority of G4 motifs are however localized in the repetitive fraction of genomes. For example, in the maize genome, mostly composed of LTR retrotransposons, 71% of non-telomeric G4 motifs are located in repetitive genomic regions . Lexa etCurrently a range of tools exist for detection of potential quadruplex forming sites in genomes. While most look for clusters of G runs in DNA sequences with constrained spacing and use regular expressions or recursive searches  and searched for G4 motifs using the R/Bioconductor  * time [min]). Each plasmid was tested in triplicate. We averaged measurements for each colony and used ANOVA (p > 0.001) and post-hoc Tukey HSD to compare lacZ units in different construct pairs (wt vs mutant).We used the Yeast for RNA isolation were grown the same way as for lacZ assay but for the final day the whole volume was used. RNA was prepared by extraction with hot acidic phenol  and thenE. coli electrocompetent cells (Invitrogen), 8 colonies were sequenced.Transcriptional start sites (TSS) were predicted using TSSPlant . ExperimZea mays B73 seeds were obtained from the U.S. National Plant Germplasm System (https://npgsweb.ars-grin.gov). Seeds were sterilized and germinated in moisturized filter papers for 5 days at room temperature. 5th day seedlings were transferred to ¼ concentration of aerated Reid-York solution [solution  in a greIn total eight RNA samples (2 μg each) were provided to the Genomics Core Facility Center (EMBL Heidelberg) for the construction of cDNA libraries with poly(A) + selection and sequencing. Sequencing libraries were prepared using an ILMN truseq stranded mRNA Kit  according to manufacturer’s protocol. Sequencing libraries were pooled in equimolar concentration and sequenced on an Illumina NextSeq 500, producing 2 × 80-nucleotide paired-end reads.http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Reads with low-quality, containing adaptor sequences, unpaired reads, containing rRNA contamination  and reads containing poly-G runs, which are a typical error for NextSeq platform, were removed using Trimmomatic 0.36 [ftp://ftp.ensemblgenomes.org/pub/plants/release-31/fasta/zea_mays/dna/) using STAR aligner v2.5.2b [Raw RNA-Seq libraries contained 47–56 million paired-end reads for treated samples and 47–62 million paired-end reads for control samples. Reads were checked for quality using FastQC . Due to a differences in mapping reads onto transposable elements  compared to onto genes, we adjusted mapping settings to allow multimaps and a higher number of mismatches in mapped reads to reflect transposon variability: --winAnchorMultimapNmax 1000, −-outFilterMultimapNmax 1000, −-outFilterMismatchNmax 15, −-alignIntronMin 5 --alignIntronMax 20,000. The number of mapped reads with these settings varied from 234 to 360 thousand, corresponding to 0.68–1.05% of library sizes. Subsequently, to obtain raw counts of mapped reads per transposable element, the featureCounts [--fraction option was used to assign counts of multi-mapped reads to transposons correctly and to avoid multiple counts of the same sequence. These raw counts were used for differential expression analysis performed with the EdgeR package [p-value) were estimated using the exactTest function and adjusted p-values (FDR) with the p.adjust function. Transposons with LFC > | 1.5 | and FDR < 0.05 were considered as differentially expressed. Such transposons were annotated as described above in the TE reference sequence annotation section. Elements with inconsistecies in annotation, e.g. wrong order of protein domains, were excluded from the analysis. To correlate RNA-Seq coverage with position of quadruplexes in differentially expressed LTR retrotransposons, RNA-Seq coverage was estimated by bedtools genomecov [-d -split -scale $norm_factor, where $norm_factor represents normalization factor calculated for each library by the EdgeR package. RNA-Seq coverage for all control and treatment samples was aggregated to average coverage and plotted by using custom R script together with annotation of LTR retrotransposons.To estimate the expression of individual maize transposable elements, RNA-Seq reads were mapped using STAR aligner v2.5.2b  on the MreCounts  tool wit package , which i package . Poorly pqsfinder  using der Fig.  and 4. Wder Fig.  with a tder Fig. . This alSurprisingly, the majority (79%) of all high-scoring PQSs in maize elements were accumulated in the minus strand Fig. . The preAlthough LTR retrotransposons tend to harbour more than one PQS in their LTRs, for clarity and convenience we selected 10 elements possessing only one PQS within their LTRs. Since even sequences with very long central loop can form G4s, our selection included five elements with PQS having short loops (up to 8 nucleotides) and five elements with PQS possessing a central loop of 27–49 nucleotides  spectra using synthetic oligonucleotides Fig. . We perfThe ability of tested oligonucleotides to form quadruplexes was also confirmed by native PAGE providing information on molecularity Fig. . All oliWe tested the effect of mutations on G4s formation by substituting some guanines with adenines with the aim to disrupt G4 formation. The substitutions were carried out on two inner runs of guanines, since we had previously observed that this had a greater effect on G4 formation than in outer G runs , Additio. Our CD Saccharomyces cerevisiae (CM100). LTRs originated from four LTR retrotransposons: the Huck, Gyma, Dagaf and Tekay families belonging to a Gypsy superfamily and were 1.3–3.5 kb long  lacZ expression was not affected by G4 disruption while in the Huck element the lacZ protein level was more than twice the amount in G4 mutants than in the wild-type and control mutant LTRs (mutation in G4 motif loop) that both harbored stable G4s , we estimated transcription start sites (TSS) using the Strawberry TSSPlant prediction tool and then performed Rapid Amplification of cDNA Ends (RACE) on both yeast and maize total RNA. We found that the transcription start site of the Huck element is located within the LTR and upstream of the G4 sequence both in yeast and maize although the position of specific TSS differed slightly Fig. . NotablyIn yeast we used mutations of PQS and tested the effect of G4 formation on a very limited number of elements, however, the potential effect of G4 on gene expression in vivo can also be studied by using a G4-stabilizing ligand. Therefore, to know more about the genome-wide G4 stabilization effect on retrotransposons transcription, maize seedlings were grown in the presence of the NMM ligand and polyA RNA sequencing was performed using Illumina. The subsequent analysis of RNA-Seq data revealed that the elements studied above had low transcription and were not differentially expressed. On the other hand, several LTR retrotransposons showed high transcription and were differentially transcribed in the presence/absence of NMM. The Gypsy retrotransposons of Grande and Uvet showed lower transcription in the presence of NMM while in the Guhis and Maro families NMM had stimulatory effect on transcription Fig. .Fig. 5Efin cellulo systems [In this study we showed that the G4 motif, previously confirmed to adopt quadruplex conformation in vitro, located downstream of TSS within the long terminal repeat of LTR retrotransposons, affects the LTR driven expression of the lacZ reporter gene by regulating translation. The translation repression by G4s located in the 5’UTR of the firefly luciferase reporter gene has been well-documented in both cell-free and  systems , 36. OurOur work on prediction of G4 motifs, revealed that central loop length is an important determinant of in vivo G4 formation. Four out of five tested oligonucleotides with shorter loops readily formed G4s in vitro. Contrastingly, the motifs with longer central loops (27–49 nt) did not readily adopt quadruplex conformation under tested conditions and G4 formation was rather an exception here. Although our study was focused only on the maize LTR retrotransposons, our results are in agreement with previous analyses from 21 plant species that revealed enrichment of G4 motifs within the LTRs of retrotransposons . The difThe prevalence of PQS in the minus strand suggests that there is selection pressure against the presence of G4 in the plus strand where G4s inhibit the translation and subsequent amplification of retrotransposons. This is consistent with our results showing that the translation of the Huck retrotransposon (possessing G4 in the plus strand) was inhibited while the translation of the Gyma, Tekay and Dagas retrotransposons (possessing a G4 motif in the minus strand) was not affected. Strand specificity in G4-affected processes has also been observed in other systems and organisms. For example, Smestad and Maher  demonstrAlthough we demonstrated the effect of the G4-stabilizing drug NMM on the transcription of LTR retrotransposons, irrespective of their subsequent impact on translation, the elucidation of the role of G4s in transcription and other steps of the LTR retrotransposon life-cycle needs further research. It remains a question to what extent does the positive or negative effect of G4 on transcription depend on the LTR retrotransposon family and its mode of regulation. Moreover, when assessing the differences between the G4 effect on transcription and translation in yeast and maize, we should keep in mind that there are different cellular factors binding the G4s in each case.The inhibitory or stimulatory effect of G4s on LTR retrotransposons expression can also be explained by the formation of quadruplex structures within only a specific genomic context and/or in particular cellular (ionic and protein) environments. Such an explanation is consistent with the finding that quadruplexes are globally unfolded in eukaryotic cells . The abuWe have demonstrated the effect of G4s on the transcription of LTR retrotransposons in maize and on their translation in yeast but we cannot exclude that G4s affect also other steps of LTR retrotransposon life cycle. The effect of G4 on other life cycles has previously been shown in closely related retroviruses, e.g. in HIV-1 nucleocapsid proteins are bound to the G4 structure of the preintegration genome leading to the initiation of the virion assembly . In addiIt is also possible that in some cases G4s take part in retrotransposon stress activation. RNA quadruplexes are essential for cap-independent translation initiation  during wOur finding that all four tested G4s adopted intramolecular (monomolecular) quadruplex agrees with its regulatory role during translation or transcription where a single RNA/DNA molecule participates. Moreover, all our G4s show parallel strand orientation prevalence supporting their potential role during transcription since promoter-associated quadruplexes tend to be parallel-stranded .Our study provides, to our knowledge, the first experimental evidence that quadruplex DNA located within the long terminal repeat of LTR retrotransposons can affect the expression of plant LTR retrotransposons in vivo: (i) mutation disrupting G4 in the LTR resulted in a higher translation level of a downstream located reporter gene in yeast compared to the wild-type the G4 motif and (ii) the G4 stabilizing drug NMM affected transcription of LTR retrotransposons in maize. This demonstrates that quadruplex DNA plays a regulatory role in the maize LTR retrotransposon life-cycle. Therefore, stabilization of quadruplexes present in LTR retrotransposons under specific cellular conditions can, thanks to the multicopy character of LTR retrotransposons, influence whole genome dynamics as well as represent the abundant barriers for DNA replication.Additional file 1:Annotation of all 579 maize TEs included in this study. The presence and position of detectable LTRs, PBS and PPT sequences (LTR Finder), protein-coding domains (BLASTX) and potential quadruplex sequences . White rectangles represent LTRs, blue rectangles are common TE domains (labelled) or other domains detected in Uniprot (unlabelled). Small blue bars are PQS with score > 24 (> 64 larger bar). (PDF 927 kb)Additional file 2:Overview of oligonucleotides, BAC clones and primers used in study. Name is derived from Maize TE Database. BAC referes to ZMMBBc library coordinates of clones containing given element. G4 motif coresponds to oligonucleotides used for CD measurments and UV melting. Guanine tracks and mutations are highlighted by bold bigger font in wilde types and mutants respectively. Forward and reverse primers were used for element amplification from BAC clones, the product length is indicated as last colum. Mutagenic primers were used for generating constructs with mutatnt LTRs, mutations are highlighted as mentiond above. RACE and RACE nested primers were used for rapid amlification of cDNA ends in yeast and maize. (XLSX 10 kb)Additional file 3:pqsfinder where (a) score > 64 and (b) score > 25. Gypsy (RLG), Copia (RLC) and other (RLX) superfamilies are shown in separate columns. Frequency  represents the number of PQS present in a window covering 2% of TE length. 75% of LTRs fall within the black rectangles shown below the horizontal axis . (TIFF 3702 kb)Occurrence of high-scoring PQS along maize LTR retrotransposons. The distribution of PQS containing a minimum of four adequately spaced G runs in the sense strand  and antisense strand  as identified by Additional file 4:Table S1. Families possessing PQS score > 64. Table S2. Number of PQS score > 64 in superfamilies. Table S3. Average lengths of LTRs, non-LTR regios and whole elements. (XLSX 13 kb)Overview of families, PQS and average lengths. Additional file 5:CD spectra of oligonucleotides without G4-forming ability. CD spectra of oligonucleotides representing wild-type PQS from various LTR retrotransposons obtained at different concentrations of potassium ions . Debeh, Nobe, Hooni, Wuwe and Prem1 are oligonucleotides with long middle loop. Flip has short middle loop. (TIFF 883 kb)


The generation of induced pluripotent stem cells (iPSCs) has underdefined mechanisms. In addition, leukemia inhibitory factor (LIF) activated Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT3) pathway is the master regulator for naïve-state pluripotency achievement and maintenance. However, the regulatory process to attain naïve pluripotent iPSCs is not well understood./STAT3 activity. We describe JAK/STAT3 signaling-specific biological events such as gametogenesis, meiotic/mitotic cell cycle, and DNA repair, and JAK/STAT3-dependent expression of key transcription factors such as the naïve pluripotency-specific genes, developmental pluripotency associated (Dppa) family, along with histone modifiers and non-coding RNAs in reprogramming. We discover that JAK/STAT3 activity does not affect early phase mesenchymal to epithelial transition (MET) but is necessary for proper imprinting of the Dlk1-Dio3 region, an essential event for pluripotency achievement at late-reprogramming stage. This correlates with the JAK/STAT3-dependent stimulation of Dppa3 and Polycomb repressive complex 2 (PRC2) genes. We further demonstrate that JAK/STAT3 activity is essential for DNA demethylation of pluripotent loci including Oct4, Nanog, and the Dlk1-Dio3 regions. These findings correlate well with the previously identified STAT3 direct targets. We further propose a model of pluripotency achievement regulated by JAK/STAT3 signaling during the reprogramming process.We performed transcriptome analysis to dissect the genomic expression during mouse iPSC induction, with or without blocking the JAK/STAT3 promoted pluripotency establishment, which are valuable for further improving the naïve-pluripotent iPSC generation across different species including humans.Our study illustrates novel insights for JAKThe online version of this article (10.1186/s12864-018-4507-2) contains supplementary material, which is available to authorized users. Oct4, Klf4, Sox2, and c-Myc (OKSM) [Generation of induced pluripotent stem cells (iPSCs) represents a powerful way to establish embryonic stem cell (ESC)-like cells through ectopic expression of the four transcription factors, namely c (OKSM) . Howeverc (OKSM) . Large-sc (OKSM) –7. The ec (OKSM) , 8, 9. HGtl2-Rian-Mirg, localized in the Dlk1-Dio3 region at chromosome 12qF1  localized between Dlk1 and Gtl2 genes [Dppa3) gene antagonize the binding of de novo DNA methyltransferases 3 (Dnmt3s) to IG-DMR region, therefore prevent the IG-DMR hypermethylation in reprogramming [Dnmt3s for proper imprinting of Dlk1-Dio3 in mouse ESCs [Dppa3 or PRC2 activity is controlled in reprogramming to ensure proper imprinting of the Dlk1-Dio3 region is unclear.The transition of somatic to pluripotent state is also regulated by stage-specific expression of non-coding RNAs such as microRNAs (miRNAs) , 10, 11 l2 genes   pathway by inducing heterodimerization of LIF receptor and the signal transducer protein gp130 [/STAT3 by LIF ensures naïve-state mouse ESC pluripotency and self-renewal [/STAT3 activity regulates different biological events to ensure complete reprogramming. Characterization of JAK/STAT3 mediated reprogramming activities is needed to fully elucidate its downstream mechanism/effectors for naïve-state pluripotency generation. Such knowledge will also help to improve the LIF signal-dependent naïve-state iPSC generation across different species including humans [The cytokine leukemia inhibitory factor (LIF) activates Janus kinas/signal transducer and activator of transcription 3 , while blocking JAKre-iPSCs . These pnd Nanog , two halnd Nanog . To furtOct4 distal enhancer region (OG-MEFs), as described previously [Oct4 activation) in Ctl reprogrammed cells started to develop quickly between S1 and S2, while those colonies in Jaki-treatment remained GFP negative (GFP-) [/STAT3 activity significantly impacts global gene expression patterns at either stage.We performed transcriptome analysis of the RNA samples of reprogrammed MEFs that carry a GFP reporter controlled by the eviously   . Pearson/STAT3 signaling) and Jaki  conditions, or 2) between the Jaki- and DMSO-treatments at S1 or S2, to identify specific targets of JAK/STAT3 activity at these two reprogramming stages. Out of the 13,547 genes detected, Cuffdiff analysis revealed the largest numbers of significantly up−/down-regulated genes  happened in Ctl reprogramming between S2 and S1  either 1) between the S1 and S2 reprogrammed cells within each treatment, to compare the dynamic reprogramming differences in Ctl  and the CCT family  [Sumo1, Sumo2, and the E3 SUMO-protein ligase Pias2 [We then asked how JAKent Fig. –left. Thent Fig. –left. Th6A, − 7) , 40, or /STAT3 signaling happen during reprogramming. These include the upregulation of gametogenesis, meiotic/mitotic cell cycle, and protein modification including protein folding and sumoylation, and downregulation of immune responses and protein phosphorylation.For all significantly downregulated DEGs between S2 and S1 reprogrammed cells, 481 genes were commonly downregulated in both Ctl- and Jaki-treatments, while 1175 and 475 genes were specifically downregulated in Ctl- or Jaki-treatment, respectively Fig. . GO-anal/STAT3-specific, we compared the DEGs between Jaki- and Ctl-treatments at the same reprogramming stage. As there are limited numbers of significant DEGs identified at S1  both in drosophila and mammals [To further evaluate the biological events in reprogramming that are JAK S1 Fig. , we focu S1 Fig. , only on S1 Fig. . On the  S1 Fig. . Interes S1 Fig. –right. W S1 Fig. . For exa Syce1/2 –45) werend Ccne1 , 47), annd Brca2 –50), whi mammals .Fig. 3JA/STAT3-dependent genes in reprogramming are directly targeted by STAT3, by comparing our data with the processed STAT3 ChIP-seq data [/STAT3-dependent spermatogenesis/DNA repair genes upregulated in reprogramming are bound by STAT3, such as Brca2, Mael, Dmrt1, Chek2, etc., so is the case for nearly 1/3 of the upregulated mitotic cell cycle-associated genes such as Ccne1, Mybl2, Cdc6, etc.  has identified thousands of gene loci directly bound by STAT3 . We therseq data , and witseq data . We founOct4-GFP+ colonies [Snai1, Snai2, and Twist [Snai1, Snai2, Cdh2, Twist1, and drastic upregulation of epithelial marker E-cadherin/Cdh1 in Ctl reprogramming condition were further downregulated at S2 than at S1 , while some others  downregulated  and Mirg are among the 25 lincRNAs downregulated at S2 in Jaki-treatment compared with the Ctl  were significantly downregulated by Jaki-treatment  are naïve-state ESC-specific [Dppa3, Fbxo15, Fgf4, and Tbx3) being direct targets of STAT3  [Gtl2-Rian-Mirg lincRNAs is regulated by JAK/STAT3 at late-reprogramming stage, and this may be achieved through direct STAT3 binding to Gtl2/Meg3 and through JAK/STAT3-dependent Dppa3 activation in reprogramming. In the meanwhile, JAK/STAT3 promotes complete pluripotency establishment by stimulating the activation of key pluripotent genes, including the naïve-state and germ cell specific markers.The pluripotent factor y Dnmt3s . We wond106a-363 –61  , 63 is J Lefty2)   , 65. Thu/STAT3 activates the expression of de novo DNA methyltransferase Dnmt3a, − 3b, − 3 l, and suppresses the histone deacetylases (Hdacs) expression [Hdac10 expression in S2 in the presence of Jaki  including Hat1, Kat5, −6b, and − 8 [/STAT3 activity, however, downregulated Hats/Kats including Hat1, Kat6b, and Ncoa3 [Hdac10, Hat1, and Ncoa3 gene loci in ESCs by STAT3 was also shown before [/STAT3 also selectively stimulates Hats/Kats expression to promote histone acetylation in reprogramming.Epigenetic changes during reprogramming are essential to activate core pluripotent genes, and silence transgenes and lineage commitment genes , 6, 8, 9pression . Our RNAaki Fig. . In addi and − 8  from S1 nd Ncoa3 , 68 at Sn before . These dDlk1-Dio3 IG-DMR region by de novo Dnmt3s, and depletion of PRC2 components Eed, Jarid2, or the major methyltransferase Ezh2 suppressed maternal Gtl2-Rian-Mirg expression, due to hypermethylation of IG-DMR [/STAT3 promotes the expression of both Gtl2-Rian-Mirg lincRNAs and de novo Dnmts at S2  at S2 compared to the Ctl  and remained largely GFP-, thus further validating the specificity of Jaki on inhibiting LIF/STAT3 signaling  led to a gradual conversion of GFP- colonies to GFP+ in 3 weeks, while those colonies left in either Jaki or LIFAb treatment remained GFP-, as confirmed by fluorescence activated cell sorting (FACS)  Fig. . Previouromoters . Our stutivation , 71, and3 region , 69 . All constructs were verified by DNA sequencing.The Jak inhibitor I (Jaki) was from EMD . LIF antibody (LIFAb) was from Santa Cruz . Retroviral pMXs-mouse www.ncbi.nlm.nih.gov/geo) under the accession number GSE97261.Total RNA was isolated from reprogrammed cells with different treatments using RNeasy Mini kit  and reverse transcribed using the SuperScript III Reverse Transcription Kit . The quality of Total RNA was examined with the Aglient RNA 1000 Nano kit . rRNA was then removed by using Ribo-Zero-rRNA Removal kit . 500 ng of rRNA-depleted total RNA from each sample was used to prepare the RNA sequencing library following the manufacturer’s instructions by SOLiD Total RNA-seq Kit . Finally, sequencing libraries were quantified by using Agilent 2100 bioanalyzer and then barcoded, multiplexed, and sequenced on a 5500xl Genetic Analyzer at the Center for Applied Genetics and Technology, University of Connecticut. We obtained approximately 240 million sequencing reads with a read length of 75-bp from 8 samples. The raw FASTQ files and normalized gene expression levels are available at Gene Expression Omnibus (GEO)  of less than 0.05. Expression pattern clusters were generated by the K-means clustering algorithm using R.Sequencing reads were trimmed as previously described . Brieflyp-values are shown for the representative terms. Principle component analysis (PCA) was performed using the GeneXplain platform (www.genexplain.com).Functional annotation enrichment analysis for Gene Ontology (GO) and pathway analyses were conducted by Database for Annotation, Visualization and Integrated Discovery Bioinformatics Resource (DAVID) . We summt-test.Total RNA were extracted using a RNeasy Extraction kit , reverse transcribed using a iScript Reverse Transcription Kit  and PCR amplified with specific primers (Primer sequences available upon request). qRT-PCR was performed using SYBR Green PCR Master Mix (Bio-Rad) and the ABI 7500 Fast instrument, and data analyzed using the 7500 software version 2.0.2 provided with the instrument. All values were normalized with GAPDH as the internal control and relative mRNA expressions were quantified using either MEFs or R1-ESCs as the reference, which is specified in each figure legend. Data were analyzed with One Way ANOVA or the Student’s Oct4 and Nanog promoter regions were amplified using PCR primers described previously [TACCGGACTCAGATCT TGGTTTGTTATGGGTA AGTTTTATG (forward) and 3’-GTCGACTGCAGAATTC CTTCCCTCACTCCAAAAATTAAAA (reverse), with the bold letters indicate vector sequences for fusion cloning. PCR were performed with Taq 2× Master Mix  and cloned using an In-Fusion HD Cloning System (Clontech) into pIRES2-DsRed vector digested by BglII and EcoRI (New England Biolabs). Clones were picked, cultured in 5 ml LB medium with antibiotics overnight, and plasmid DNAs were extracted using a Qiaprep Mini Kit (Qiagen) and sequenced by regular Sanger DNA sequencing.For bisulfite sequencing, genomic DNAs were extracted and bisulfite converted using an EpiTeck Bilsulfite Kit (Qiagen). eviously . Nested-Generation of Jaki-treated pre-iPSCs using retroviral transduction and reprogramming medium was described previously . The repAdditional file 1:Dlk1-Dio3 region at mouse chromosome 12qF1. The Gtl2-Rian-Mirg lincRNAs are expressed from the maternally inherited chromosome, while the protein coding Dlk1, Rtl1, and Dio3 genes are expressed from the paternally inherited chromosome. IG-DMR is paternally methylated but demethylated in maternal chromosome to control expression of the Gtl2-Rian-Mirg lincRNAs. (PDF 61 kb)Schematic representation of the Additional file 2:Cuffdiff Analysis Results Table of DEGs Between Ctl S2 vs. S1 (DMSO_S2 vs. S1), Jaki S2 vs. S1 (Jaki_S2 vs. S1), Jaki vs. DMSO at S1 (S2_Jaki vs. DMSO), and Jaki vs. DMSO at S1 (S1_Jaki vs. DMSO). (XLSX 829 kb)Additional file 3:DAVID Analysis Table of Biological Processes (FDR < 0.05) for Common or Specific Up- or Down-regulated DEGs Between Ctl reprogramming (DMSO_S2 vs. S1) and Jaki-reprogramming (Jaki_S2 vs. S1). (XLSX 60 kb)Additional file 4:DAVID Analysis Table of Biological Processes (FDR < 0.05) for Up- or Down-regulated DEGs Between Jaki vs. DMSO at S2. (XLSX 28 kb)Additional file 5:Table for DEGs listed in Spermatogenesis/Meiotic, Mitotic, and DNA Repair GO-terms That Are Upregulated between Ctl S2 vs. S1 comparison but Downregulated at S2 in Jaki vs. Ctl comparison. (XLSX 60 kb)Additional file 6:Table for S1- or S2-specifically Expressed Genes under DMSO Ctl or Jaki-Treatment at S1 or S2. (XLSX 102 kb)Additional file 7:/STAT3 Activity Is Needed to Activate Pluripotent Loci in Reprogramming. DNA methylation of Nanog promoter region measured by bisulfite sequencing for samples described in Fig. JAK


Arabidopsis thaliana nuclei. We show that in various tissues, 10%–20% of the regions on the chromosome arms are anchored at the nuclear periphery, and these regions largely overlap between different tissues. Unlike LADs in animals, the identified domains in plants are not gene-poor or A/T-rich. These domains are enriched with silenced protein-coding genes, transposable element genes, and heterochromatic marks, which collectively define a repressed environment. In addition, these domains strongly correlate with our genome-wide chromatin interaction data set (Hi-C) by largely explaining the patterns of chromatin compartments, revealed on Hi-C maps. Moreover, our results reveal a spatial compartment of different DNA methylation pathways that regulate silencing of transposable elements, where the CHH methylation of transposable elements located at the nuclear periphery and in the interior are preferentially mediated by CMT2 and DRM methyltransferases, respectively. Taken together, the results demonstrate functional partitioning of the Arabidopsis genome in the nuclear space.The nuclear space is not a homogeneous biochemical environment. Many studies have demonstrated that the transcriptional activity of a gene is linked to its positioning within the nuclear space. Following the discovery of lamin-associated domains (LADs), which are transcriptionally repressed chromatin regions, the nonrandom positioning of chromatin at the nuclear periphery and its biological relevance have been studied extensively in animals. However, it remains unknown whether comparable chromatin organizations exist in plants. Here, using a strategy using restriction enzyme–mediated chromatin immunoprecipitation, we present genome-wide identification of nonrandom domain organization of chromatin at the peripheral zone of  Arabidopsis thaliana, CTs in interphase nuclei were unequivocally demonstrated with chromosome painting, which further revealed a predominantly random arrangement of CTs with respect to each other  is often tethered at the nuclear envelope, whereas telomeres often cluster and reside in the nuclear interior around the nucleolus  has been shown to specifically localize at the nuclear periphery . In contrast, a parallel ChIP experiment conducted on the same material, but with an antibody against RNA polymerase II, showed expected enrichment on a housekeeping gene , ruling out possible technical failures in our ChIP experiments. This negative result implied that NUP1:GFP did not directly interact with chromatin, or that such interactions, if they occurred, were not efficiently preserved by the crosslinking treatment in our ChIP experiment. To enhance the sensitivity of enriching chromatin loosely interacting with NUP1:GFP, we developed a restriction enzyme (RE)-mediated ChIP protocol, in which only mild sonication was applied to break the nuclei following digestion of chromatin with RE  and compared the results with those derived from a regular ChIP-seq . In addition, chromatin regions enriched by RE-ChIP-seq largely overlapped with those enriched by the regular ChIP-seq, by which >80% of chromatin regions enriched by ChIP-seq were found enriched in each RE-ChIP-seq replicate , indicating the feasibility of the RE-ChIP method in capturing chromatin features in plants. Furthermore, selected regions showing higher NUP1 RE-ChIP signals could be confirmed with fluorescence in situ hybridization (FISH) . Taken together, our results suggest that certain chromatin regions on the Arabidopsis chromosome arms are preferentially found near the nuclear periphery.We performed two RE-mediated ChIP-seq trial experiments with different sonication intensities and examined the sequence coverage with a 50-kb window setting to gain an overview of the distribution of sequencing reads. Interestingly, the RE-mediated ChIP with NUP1:GFP (hereafter referred to as NUP1 RE-ChIP-seq) showed that chromatin in pericentromeric regions was generally enriched, whereas chromatin on the distal chromosome arms tended to be depleted C,D. MoreChIP-seq . ConsistArabidopsis centromeres in almost all stages of the cell cycle . Apart from this, consistent with the fact that pericentromeric chromatin is mostly found at the nuclear periphery, the chromatin regions belonging to the remaining pericentromeric regions showed the highest NUP1:GFP RE-ChIP signals .It has been well demonstrated that chromocenters, which consist of the centromere and pericentromeric regions, are located preferentially at the nuclear periphery . It was ll cycle , and Gamll cycle ; these ill cycle . We founArabidopsis genome in the nuclear space has been recently revealed by several Hi-C experiments /knot-engaged elements (KEEs), which are regions showing strong intra- and inter-chromosomal interactions on Hi-C maps , suggesting that the clustering of IHIs/KEEs does not preferentially occur at the nuclear periphery.The nucleoli . In genenucleoli B. Becausi-C maps . HoweverSupplemental Table S1). The signal patterns, as well as enriched genes, between biological replicates were highly reproducible in each tissue . Interestingly, at a chromosomal level, NUP1:GFP RE-ChIP-seq data obtained from different tissues resembled each other . A common feature across these tissues was that chromatin close to the centromere was more frequently found at the nuclear periphery than was chromatin on the distal chromosome arms, which was reflected on a density plot showing the distribution of enriched chromatin segments . Notably, for each plant tissue used in this study, the RE-ChIP output signal was the average of a mixture of different cell types. For the inflorescence tissue, compared to roots and leaves, the lower RE-ChIP signals around the pericentromeric regions might also be attributed to a dilution effect among different cells with divergent chromatin positioning patterns. Across different tissues, the regions enriched at the nuclear periphery covered 10%–20% of the genome . A clustering analysis showed that roots and leaves from 7-d-old seedlings formed a subgroup, although from a tissue-identity point of view, leaf tissues with different ages would be expected to be clustered together . Nevertheless, due to the similar RE-ChIP-seq signal profiles on a chromosomal scale , there were substantial overlaps of enriched chromatin regions between any two given tissues . Of note, our analyses in this study were not sensitive to a cutoff that we arbitrarily set. We obtained the same conclusions when we changed the cutoffs to 2 or 3 Mb, in which more genomic regions flanking the pericentromeric regions were excluded.Supplemental Fig. 10). On the other hand, in contrast to LADs, NUP1-enriched domains were neither substantially depleted with protein-coding genes  nor enriched with A/T-rich sequences . We considered these control sets to be more appropriate than random permutations, as they maintained the distribution pattern of the enriched regions across the genome. In terms of transcriptional activity, the enriched protein-coding genes clearly had lower expression levels , suggesting a role of gene bodies in demarcating these chromatin domains. On the other hand, TE genes enriched at the nuclear periphery did not show lower expression levels than those that were not enriched . Additionally, TE genes enriched at the nuclear periphery showed a higher average level of heterochromatin marks, such as H3K9me2, H3K27me1, and DNA methylation . In terms of classes, two class-II TE genes (MuDR and CACTA-like) were more likely to be found at the nuclear periphery . Taken together, the Arabidopsis nuclear periphery defines a domain of transcriptional repression enriched for TE genes and transcriptionally inactive protein-coding genes.Based on gene annotations, we found that in all tissues, transposable element (TE) genes and pseudogenes were enriched compared to the control sets, which were simulated by shifting the coordinates of the enriched regions a certain distance upstream or downstream . CHH methylation over TE bodies is mediated by two partially overlapping pathways: RNA-directed DNA methylation (RdDM) and RdDM-independent  sequence context plays a crucial role in regulating expression and transposition of TEs. We noticed that TEs enriched at the nuclear periphery had a higher DNA methylation level in all sequence contexts . We nexte mutant . These pependent . Howeverependent . DOMAINSependent . Our resArabidopsis chromocenters are preferentially found at the nuclear periphery, but it was unclear whether such nonrandom localization was restricted to chromocenters. The present study demonstrates that the peripheral zone of the Arabidopsis nucleus is also enriched with interstitial regions on the chromosome arms, which are mainly heterochromatic. This is reflected by the fact that these regions have higher percentages of TE genes and silenced protein-coding genes , which could be at least attributed to following reasons. First, both the gene expression and RE-ChIP-seq experiments were conducted on tissues with a certain degree of cell-type heterogeneity; therefore, even if active transcription and positioning at the nuclear periphery are mutually exclusive, both events might be eventually captured at a gene locus in a mixed cell population. Second, the nuclear periphery does not absolutely inhibit transcription. This has been demonstrated in human cells by tracking the expression of a pool of genes after artificially anchoring them to the nuclear envelope; only a subset of the targeted genes showed reduced expression  PROTEIN locus showed that it is repositioned from the nuclear interior to the nuclear periphery together with robust transcriptional activation in response to light stimuli , respectively . By showbidopsis . From a bidopsis , as wellbidopsis ). It wouN6-adenine residue, has been applied to Arabidopsis to identify targets of LIKE HETEROCHROMATIN PROTEIN 1 (LHP1) as a complementary approach to the conventional ChIP method  background were grown at 23°C in long days (16 h light/8 h dark) on half-strength Murashige and Skoog (MS) medium supplemented with 1% sucrose and 0.3% Phytagel. The aerial and root tissues of 7-d-old seedlings were harvested at Zeitgeber time (ZT) 6 h. Other tissues, including 30-d-old leaf and inflorescence with flower bud up to stage 9  and sheared by sonication with a Covaris S220 instrument . The sonicated sample was centrifuged at 14,000 rpm for 5 min, and the supernatant was mixed with 100 µL 10% Triton X-100. Next, the sheared chromatin was mixed with an equal volume of IP buffer  and then equally divided and incubated with anti-GFP antibody  or normal rabbit IgG , respectively. After overnight incubation at 4°C, 10 µL Protein A/G magnetic beads (Pierce) were added and incubated for 2 h at 4°C. The beads were washed at 4°C as follows: 3× with IP buffer, 1× with IP buffer having 500 mM NaCl, and 1× with LiCl buffer , for 5 min each. Chromatin retained on beads was incubated in 200 µL elution buffer  for 6 h at 65°C, followed by Proteinase K treatment for 1 h at 37°C. DNA was extracted with a standard phenol-chloroform method. To increase sequence diversity at the ends of DNA, the immunoprecipitated DNA was incubated with dsDNA Fragmentase (NEB) for 25 min at 37°C, which randomly cut DNA into ∼100- to 200-bp fragments. The digested DNA was purified with AMPure XP beads (Beckman Coulter), and all subsequent end repairing, A-tailing, adaptor ligation, library amplification steps were done through following a standard protocol (Illumina). The final libraries were sequenced on an Illumina HiSeq 3000 instrument with 2×150-bp reads.Tissues were collected and fixed under vacuum for 30 min with 1% formaldehyde in MC buffer  at room temperature. After fixation, tissues were incubated at room temperature for 5 min under vacuum in MC buffer with 0.15 M glycine. Nuclei from 0.5 g fixed material were used for each round of ChIP. Nuclei were isolated as described . Nuclei Arabidopsis thaliana reference genome (TAIR10) using Bowtie 2 v2.2.4 (Arabidopsis gene annotation was retrieved from Ensembl Genomes (ftp://ftp.ensemblgenomes.org/) (release-24)  . A gene The FISH experiment was performed as previously published  with modZ-stack image was obtained by maximum projection of signals from five optical sections. Then, the nuclear edge was defined by adjusting the threshold of DAPI channel, and the distance between a FISH signal and the nuclear edge was determined as described  and fluorescent proteins (GFP and mCherry) was according to the default settings. Image processing was done with the Fiji software and final assembly in Photoshop. For the distance measurement of FISH signals, first, a escribed . Only nuData for gene expression in various tissues were from http://www.ncbi.nlm.nih.gov/sra) under accession number SRP079108.All sequence data from this study have been submitted to the NCBI Sequence Read Archive (SRA;


Slc2a4 gene, in L6 skeletal muscle cells.Obesity is strongly associated to insulin resistance, inflammation, and elevated plasma free fatty acids, but the mechanisms behind this association are not fully comprehended. Evidences suggest that endoplasmic reticulum (ER) stress may play a role in this complex pathophysiology. The aim of the present study was to investigate the involvement of inflammation and ER stress in the modulation of glucose transporter GLUT4, encoded by L6 cells were acutely (2 h) and chronically (6 and 12 h) exposed to palmitate, and the expression of several proteins involved in insulin resistance, ER stress and inflammation were analyzed.P < 0.01) and its mRNA  expression. Only acute palmitate treatment increased GRP78 , PERK , eIF-2A , IRE1a  and TRAF2  protein content, and PERK phosphorylation , but did not elicit eIF-2A, IKK phosphorylation or increased XBP1 nuclear content. Additionally, acute and chronic palmitate increased NFKB p65 nuclear content  and NFKB binding activity to Slc2a4 gene promoter .Chronic and acute palmitate exposure significantly reduced GLUT4 protein (~ 39%, Slc2a4/GLUT4 expression. This regulation involves activation of initial component of ER stress, such as the formation of a IRE1a-TRAF2-IKK complex, and converges to NFKB-induced repression of Slc2a4/GLUT4. These results link ER stress, inflammation and insulin resistance in L6 cells.Different pathways are activated in acute and chronic palmitate induced-repression of  Slc2a4 gene, which encodes GLUT4 protein, results in insulin resistance  until reach 70% of confluence. Cells were then differentiated for 6 days in differentiation medium . At seventh day, cells were incubated with FBS-free restriction/treatment medium  for 12 h before treatment. Palmitate was diluted in ethanol (vehicle) and added to a final concentration of 0.75 mM palmitate and 0.5% ethanol for 2, 6 and 12 h. In parallel, control plates were treated with a non-toxic final concentration of 0.5% ethanol [L6 cells were propagated and differentiated as previously reported . Briefly ethanol , 20.L6 cells were plated in 12-well plate and treated with 0.5% ethanol alone for 12 h or with 0.75 mM palmitate/0.5% ethanol for 2, 6 or 12 h. Cell viability was assessed using MTT reagent according to the manufacturer’s recommendations with some modifications. Briefly, after the incubation with the tested compounds, the medium was changed to a medium with 10% MTT (5 mg/ml) and incubated in dark for 4 h at 37 °C. The medium was removed and replaced with a solvent solution (0.04 N HCl in absolute isopropanol), and the plates were placed on a plate shaker for 15 min to solubilize the formazan crystals derived from MTT reduction. The absorbance of the converted dye was measured at 570 nm with a background subtraction at 690 nm using a microplate reader .Total membrane protein fraction for GLUT4 protein quantification was performed as previously reported . For RE Gapdh (#Rn99999916_s1) was used for normalization. The primer sequences for rat Slc2a4 were custom designed: 5 ′ GGCTGTGCCATCTTGATGAC-3′ (fw), 5′-CACGATGGACACATAACTCATGGA-3′ (rv) and FAM AACCCGCTCCAGCAGC MGB, as previously described [One microgram of total RNA was extracted with Trizol. cDNA, obtained by reverse transcription, was then amplified with Taqman system. Commercially available Taqman primers for rat escribed .Slc2a4 promoter (Slc2a4-NFKB) was 5`-GGGTTGGGGGCGTGGCCTTTTGG-3` [Nuclear proteins were extracted from L6 cells and subjected to EMSA as earlier described . The seqTTTGG-3` , 19.Data are expressed as means ± S.E.M. One-way analysis of variance (ANOVA), with Student–Newman–Keuls as a post hoc test was used for analysis of comparison, after comparison of the variances by the Bartelett’s test.The viability of L6 muscle cells incubated with 0.5% ethanol (vehicle) alone or with 0.75 mM palmitate was assessed with MTT assay. As shown in Fig. Slc2a4 gene expression was investigated over a time course of 2, 6 and 12 h in L6 myotubes. We have previously demonstrated that 0.75 mM palmitate for 20 h reduces GLUT4 protein content and glucose uptake in L6 cells [Slc2a4 gene transcript content , also known as growth arrest and DNA damage-inducible protein 34 (GADD34) was analyzed. Surprisingly, palmitate did not exert any effect in PR15A content in L6 cells Fig. .P < 0.01 versus C; Fig. The activation of PERK regulates numerous genes involved in apoptosis such as DDIT-3 (DNA damage-inducible transcript 3 protein), also known as growth arrest and DNA damage-inducible protein (GADD153) ; thus, tP < 0.05 versus C, Fig. Next, another pathway of UPR signaling was investigated. In parallel to the increased GRP78 protein content after 2 h-palmitate incubation, the content of protein kinase IRE1a also increased  and form a complex with inhibitor of nuclear factor kappa-B kinase (IKK) promoting activation of NFKB, independently of IKK activation . Here, iSlc2a4 gene by palmitate was investigated.Finally, the participation of NFKB in the modulation of Slc2a4 gene; in L6 muscle cells, the binding of NFKB dimers to a double-stranded oligonucleotide containing a NFKB site sequence inside the Slc2a4 promoter results in two complexes named a and b [a, P < 0.05 versus C; and, 68% for complex b; P < 0.01 versus C), and after 12 h . Additionally, palmitate increased the nuclear content of NFKB p65 in L6 cells (30, 23 and 32% after 2, 6 and 12 h, respectively; P < 0.05 versus C, Fig. Recently, we have reported that NFKB downregulates the  a and b . Accordi a and b , we confPalmitate is known to induce insulin resistance by imparing glucose uptake  and reduSlc2a4 gene. Actually, it has been reported that activation of UPR signaling represses the Slc2a4 gene in 3 T3-L1 adipocytes [Here we reported that palmitate reduced GLUT4 expression in L6 muscle cells; still, the correlation of mRNA and protein content was not consistent. Since post-transcriptional modulation has been reported to cause discrepancies between GLUT4 protein and its transcript abundancies , it was ipocytes . Also, iipocytes , pancreaipocytes , and C2Cipocytes . Up to dipocytes . The preipocytes . Here, wipocytes . Here weAcute palmitate exposure resulted in increased expression of PERK and eIF-2A; and surprisingly, while PERK activation was increased, eIF-2A phosphorylation was reduced. Some phosphatases such as PR15A can dephosphorylate eIF-2A, attenuating response to stress and promoting recovery of protein synthesis . In our We also demonstrated that acute palmitate even though was able to increase IRE1a expression, it did not result in its activation since phosphorylation of IRE1a and XBP1 nuclear content was not altered. However, our results strongly suggest that IRE1a can contribute to NFKB activation after acute palmitate exposure. It has been reported that basal IKK activity is sufficient to activate NFKB by IRE1a, and this occurs by physical interaction among IRE1a, TRAF2 and IKK . The forSlc2a4 gene promoter has a functional kB site to which NFKB binds and downregulates its transcription [Slc2a4 promoter, and consequently reduces Slc2a/GLUT4 expression. Consistent with these results, we have previously demonstrated that the same concentration of palmitate for 20 h reduces GLUT4 expression and glucose uptake; moreover, it induces tumor necrosis factor gene expression and NFKB accumulation in the nucleus in L6 cells [Slc2a4/GLUT4 in L6 cells, but did not increase NFKB binding in Slc2a4 gene promoter. Of note, a different pathway should be involved.Recently, it has been demonstrated the rat/mouse cription . Additiocription . Thus, NL6 cells . Our datSlc2a4/GLUT4 expression, in a ER- and inflammatory-stress mediated way; thus, contributing to induce insulin resistance (Fig. In summary, the data reveal that short-term exposition of muscle cells to palmitate can repress nce Fig. . ConsideSlc2a4/GLUT4 expression in L6 muscle cells. That is related to formation of a IRE1a-TRAF2-IKK complex, activation of NFKB and repression of Slc2a4 gene transcription. These findings indicate that ER stress may connect palmitate-induced inflammation and insulin resistance in L6 muscle cells.Palmitate reduces