We identified approximately 300 direct NRL target genes. Of these, 22 NRL targets are associated with human retinal dystrophies, whereas 95 mapped to regions of as yet uncloned retinal disease loci. In silico analysis of NRL ChIP–Seq peak sequences revealed an enrichment of distinct sets of transcription factor binding sites. Specifically, we discovered that genes involved in photoreceptor function include binding sites for both NRL and homeodomain protein CRX. Evaluation of 26 ChIP–Seq regions validated their enhancer functions in reporter assays. In vivo knockdown of 16 NRL target genes resulted in death or abnormal morphology of rod photoreceptors, suggesting their importance in maintaining retinal function. We also identified histone demethylase Kdm5b as a novel secondary node in NRL transcriptional hierarchy. Exon array analysis of flow-sorted photoreceptors in which Kdm5b was knocked down by shRNA indicated its role in regulating rod-expressed genes. Our studies identify candidate genes for retinal dystrophies, define cis-regulatory module(s) for photoreceptor-expressed genes and provide a framework for decoding transcriptional regulatory networks that dictate rod homeostasis.A stringent control of homeostasis is critical for functional maintenance and survival of neurons. In the mammalian retina, the basic motif leucine zipper transcription factor NRL determines rod versus cone photoreceptor cell fate and activates the expression of many rod-specific genes. Here, we report an integrated analysis of NRL-centered gene regulatory network by coupling chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–Seq) data from Illumina and ABI platforms with global expression profiling and  In vivo loss-of-function analysis of 16 NRL target genes in the mouse retina resulted in death or abnormal morphology of photoreceptor cells. Furthermore, we identified histone demethylase Kdm5b as a secondary node in the NRL-centered gene regulatory network. Our studies identify NRL target genes as excellent candidates for mutation screening of patients with retinal degenerative diseases, and they provide the foundation for elucidating regulation of rod homeostasis and targets for therapeutic intervention in diseases involving photoreceptor dysfunction.The rod and cone photoreceptors in the retina are highly specialized neurons that capture photons under dim and bright light, respectively. Loss of rod photoreceptors is an early clinical manifestation in most retinal neurodegenerative diseases that eventually result in cone cell death and blindness. The transcription factor NRL is a key regulator of rod photoreceptor cell fate and gene expression. Here, we report an integrated analysis of the global transcriptional targets of NRL. We have discovered that both NRL and CRX binding sites are present in genes involved in photoreceptor function, implying their close synergistic relationship.  Molecular mechanisms underlying neuronal differentiation and generation of complex sensory and behavioral circuits in the mammalian central nervous system are still poorly elucidated. Gene regulatory networks (GRNs) integrate key control elements that guide the development of distinct cell types The rod and cone photoreceptors in mammalian retina are highly specialized neurons that transduce visual signals under dim and bright light conditions, respectively −/−Nrl mice, all post-mitotic cells originally fated to become rods instead generate a cone-only photoreceptor layer Nrl expression in photoreceptor precursors produces a rod-only retina Nrl is replaced by thyroid hormone receptor β2 (Trb2) have an M-cone dominant retina, but the presence of both NRL and TRb2 yields a normal contingent of rods −/−Crx mice do not develop outer segments and eventually die During development, rod and cone photoreceptors are produced from common pools of retinal progenitors under the control of multiple transcription factors and regulatory signaling pathways in vivo occupancy in adult mouse retina by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–Seq) using Illumina and ABI sequencing platforms. We perform an integrated analysis by coupling the NRL ChIP–Seq data with published photoreceptor-specific transcriptional profiles and CRX ChIP–Seq results. We use in vivo knockdown assays to examine the physiological relevance of NRL target genes and identify secondary regulatory nodes downstream of NRL in rod transcriptional hierarchy. Our studies establish NRL and CRX as the key regulatory nodes for rod-expressed genes, identify NRL targets as candidate genes for retinal diseases, and provide a framework for GRN that controls homeostasis in rod photoreceptors.We hypothesize that detailed mapping of a rod-specific GRN would lead to the development of better therapeutic interventions in blinding diseases involving photoreceptor degeneration. Here we report the genomewide NRL in vivo in adult mouse retina. The ChIP DNA was subjected to direct high-throughput sequencing using either Illumina 1G genome analyzer or ABI/SOLiD system (ABI). The workflow for the analysis of two datasets is shown in www.nei.nih.gov/intramural/nnrldataresource.asp for raw sequence reads). Illumina and ABI datasets contained a total of 8 million 25-bp reads and 18.0 million 35-bp reads, respectively. Of these, respectively 5.3 million (66.3%) and 6.3 million (35%) reads were uniquely mapped to the mouse genome , with overlapping sequence reads forming the NRL ChIP–Seq peaks  to pull down the genomic fragments bound by NRL eq peaks . We usedeq peaks , Table 2eq peaks , with aleq peaks .A large number of NRL ChIP–Seq peaks were mapped within 1 kb of the transcription start sites . FurtherNrl−/− mouse retina −/−Nrl photoreceptors (−/−Nrl photoreceptors (−/−Nrl photoreceptors. A high correlation was detected between NRL ChIP–Seq peaks (from both Illumina and ABI datasets) and promoters of genes that are differentially expressed in rod photoreceptors of WT −/−versus Nrl retina  and eceptors . Of 4085eceptors . A combi− retina . For conNrl (As transcription factor interactions determine the specificity of gene expression patterns Nrl .−/−Nrl photoreceptors (except for MEF2C in ABI data)  BI data) . The motBI data) . The comeceptors , suggesteceptors .−/−Nrl mice exhibit reduced expression in −/−Crx retina as well As CRX is an established transcriptional activator of photoreceptor genes versus genes associated with non CRX-overlapping NRL peaks (basic cellular processes)  ocesses) .Rho and −3.5 kb for Nr2e3) Rho at −3 kb and −1.5 kb and in Nr2e3 at −1 kb and −100 bp. We also detected NRL binding in rod-specific genes  and Esrrb, an important regulator of rod gene expression Nrl−/− mice. In Esrrb, we identified strong NRL binding to the second intron. Interestingly, a strong NRL ChIP–Seq peak was observed within an intron of the Nrl gene in addition to a peak in the promoter region. Kdm5b and Wisp1 are among additional genes that are regulated by NRL and play a role in rod homeostasis (see later). NRL also binds to cone-specific genes and may contribute to their down-regulation to maintain a rod phenotype, as proposed previously We first checked Illumina and ABI ChIP–Seq data for a few established NRL target genes that are involved in rod development or function . In addivs 6.3 million) (Kdm5b and Nrl), Illumina data detected unique peaks that were not present in ABI   . Even th, Esrrb) . We thenNrl−/− mice as a tissue control. We compared ChIP-qPCR signals between anti-NRL antibody and normal IgG using WT mouse retina, and performed additional NRL ChIP analysis using WT and Nrl−/− mouse retina  were negative for CRX peaks. Co-transfection of mouse NRL expression plasmid in HEK293T cells increased the luciferase reporter expression from all 26 enhancer constructs containing NRL ChIP–Seq peaks, but not from the 5 constructs containing non-peak fragments  and 5 non-peak genomic fragments of comparable sizes upstream of an SV40 basal promoter and a luciferase reporter gene. Of 26 NRL ChIP–Seq regions, at least 19 included CRX ChIP–Seq peaks. Five non-peak genomic fragments  using the same reporter assay . We identified 21 NRL target genes that are known to be associated with retinal diseases involving photoreceptor degeneration  after electroporation  containing silent mutations that conferred resistance to shRNA mediated mRNA degradation. Co-transfection of Gapdh shRNA with dcDNA for Lman1 did not manifest a retinal phenotype, and more importantly, Lman1 dcDNA co-transfection rescued all of the Lman1 shRNA phenotypes in the retina   constructs for two of the target genes ( length) . Co-tranP+ cells , indicatKdm5b  are mediated through decreased Kdm5b expression. Some of the genes  altered by Kdm5b knockdown are associated with the visual transduction, while a few others  are associated with chromatin organization We were particularly intrigued by one of the NRL targets – dm5b see , which en arrays . Kdm5b k− retina , suggestcis-regulatory module, which also includes the binding sites for NR2E3, ESRRβ, RORβ and in some cases MEF2C. Equally important is the finding that non-CRX containing NRL cis-regulatory modules fine-tune the expression of additional photoreceptor-expressed genes, which may contribute to high metabolic demand in rod photoreceptors.Visual impairment in a vast majority of retinal and macular degenerative diseases can be attributed to dysfunction or death of photoreceptors in vivo locations for chromatin modifications and transcription factor binding in vivo NRL binding events reported here. In addition to enrichment in promoter regions, a number of NRL ChIP–Seq peaks are detected in intronic regions of annotated genes; some of these might reflect alternative promoter usage in photoreceptors as reported recently for Mef2cChIP–Seq has emerged as a cost effective, high-throughput technology for high-resolution genome-wide mapping of versus cone cell type −/−Nrl retina suggests specific and discrete cis-regulatory modules for rod versus cone photoreceptor expressed genes. CRX strongly activates the expression of both rod and cone genes −/−Crx retina, suggesting a fundamental role of NRL in regulating rod genes. CRX likely enhances rod gene expression by altering the chromatin conformation via recruitment of histone acetylases −/−Nrl retina), binding of both CRX and NRL is consistent with the common photoreceptor precursor hypothesis versus cone photoreceptors.We previously proposed that photoreceptor precursors have a “default” S-cone fate and a “tug-of-war” among a selected few transcription factors specifies rod Nrl promoter and intronic regions. While the key role of NR2E3 as a secondary node downstream of NRL is to repress cone-specific genes Like many key transcription factor nodes in GRNs http://www.sph.uth.tmc.edu/Retnet/). A catalog of genome-wide NRL targets with overlapping CRX binding sites, reported here, provides excellent candidate genes for mutation screening in patients with inherited retinal neurodegenerative diseases. We have listed almost 100 genes , which is characteristic of cone photoreceptors or late-born rods, whereas the early-born rods locate towards the vitreous side. LMAN1 participates in transport between the endoplasmic reticulum and Golgi in vivo electroporation could be due to rod to cone transformation in the absence of NRL, or delayed rod birth as a result of abnormal signaling for rod fate determination.A key aspect of photoreceptor homeostasis is the daily renewal of almost 10% of outer segment membrane discs, which requires a stringent control of the synthesis of specific phototransduction proteins and lipid molecules. Therefore, the target gene, Wisp1, another interesting target of NRL, encodes the Wnt1-inducible signaling pathway protein 1 that exerts cytoprotective and/or growth promoting effects Gene regulatory networks (GRN) control multiple pathways during development and homeostasis and provide conceptual framework for elucidating disease mechanisms All animal work must have been conducted according to relevant national and international guidelines. Animal Care and Use Committee of the National Eye Institute approved all mouse protocols. See also Retina from postnatal day (P) 28 C57Bl/6J mice was used for ChIP experiments with NRL antibody or normal IgG, as previously described Raw sequencing reads from Illumina or ABI platforms were mapped to the mouse genome (NCBI build 37) using Genomatix Mining Station (GMS).ChIP–Seq peaks were called using MACS The NRL ChIP–Seq peaks were compared to the CRX ChIP–Seq regions Nrl−/− retina Affymetrix microarray data from flow-sorted photoreceptors of WT and Transcription factor binding site enrichment analyses of sequences in ChIP–Seq peak regions were performed using RegionMiner and MatInspector .Lman1 cDNA and Wisp1 cDNA using Quikchange kit (Stratagene).To generate shRNA-resistant dcDNA constructs, silent mutations that confer resistance to shRNA were introduced into To generate enhancer constructs, ChIP–Seq peak regions were amplified and cloned into pGL3-promoter vector (Promega). HEK293T cells were transfected with these enhancer constructs, a transfection control plasmid expressing Renilla luciferase (Promega), and NRL expression plasmid or empty vector. The luciferase activities were measured 48 hr after transfection. The experiments were performed three times.Gapdh shRNA, For each target gene, three shRNA constructs were evaluated for efficacy indicated by a decrease in GFP. The most efficient one was chosen for in vivo knockdown experiments.To generate shRNA-sensor constructs for efficiency test, we cloned the shRNA target sequences into the 3′UTR of a GFP vector. The shRNA-sensor construct and CAG-HcRed (transfection control) were co-transfected with either shRNA against target or in vivo electroporation, as previously described shRNA alone or together with shRNA-resistant dcDNA was introduced in the retina of CD-1 P0 mouse pups by sub-retinal injection followed by Gapdh shRNA or Kdm5b shRNA and dissected at P20. GFP+ retinal cells were isolated from dissociated retina by FACS . RNA was extracted and cDNA was synthesized followed by sense transcript cDNA (ST-cDNA) generation using WT-Ovation Exon module (NuGEN Technologies). The ST-cDNA was fragmented and labeled with Encore Biotin Module (NuGen) and used for hybridization with GeneChip Mouse Exon 1.0 ST array (Affymetrix). The microarray data has been deposited in the Gene Expression Omnibus Database (accession #: will be available soon).Mouse retina was electroporated at P0 with Ub-GFP and Figure S1Nrl-ko mouse photoreceptors . Up: ChIP–Seq peaks associated with genes up-regulated in Nrl-ko mouse photoreceptors .Genomic distribution of NRL ChIP–Seq peaks that are enriched for distinct TF families. NRL ChIP–Seq peak regions (Illumina), enriched for the top 8 TF families , were ma(TIF)Click here for additional data file.Figure S2Gnat2, m-Opsin, Gngt2 and Pik3ap1) were cloned into pGL3-promoter vector in front of SV40 basal promoter and a luciferase reporter. The enhancer constructs (200 ng) were transfected in HEK293T cells with increasing amount of mouse Nrl (mNrl) expression plasmid (in pC4C vector). Empty pC4C vector was included to make the total amount of DNA equal among different transfection groups. The y-axis shows fold change (FC) of normalized luciferase readings. Two non-peak regions served as additional negative controls. The experiments were performed three times, and the representative results are shown as mean ± SD. * P<0.01 by Student's t test.Enhancer function of NRL ChIP–Seq regions associated with cone genes in transfected HEK293T cells. NRL peak regions associated with cone genes ((TIF)Click here for additional data file.Figure S3Gapdh shRNA or target gene shRNA construct was co-transfected with the sensor construct and CAG-HcRed into HEK293T cells. Sensor knockdown was imaged at 48 h after transfection.Efficacy tests for shRNA constructs, using a reporter assay in HEK293T cells. Sensor constructs included shRNA target sequences in 3′UTR of GFP. The (TIF)Click here for additional data file.Figure S4in vivo knockdown of Kdm5b, Lman1 or Wisp1 in P0 retina evaluated at P7. Ub-GFP and shRNA against Gapdh, Kdm5b, Lman1 or Wisp1 were co-injected in the sub-retinal space of CD-1 mice at postnatal day 0 (P0), followed by electroporation. Retinas were harvested at P7 and examined for GFP (green) fluorescence and Rho (red) and DAPI (blue) staining. Scale bar: 20 µm.Effects of (TIF)Click here for additional data file.Figure S5In vivo knockdown of additional NRL target genes. Ub-GFP and shRNA against 13 NRL target genes  or Gapdh were injected in the sub-retinal space of CD-1 mice at P0 and electroporation was performed. Retinas were harvested at P20 and examined for GFP (green) fluorescence and Rho (red) and DAPI (blue) staining. Scale bar: 20 µm.(TIF)Click here for additional data file.Table S1Genomic distribution of NRL ChIP–Seq peaks relative to the nearest TSS. Genomic distribution of NRL ChIP–Seq peaks was categorized according to the distance to the nearest TSS. TSS: transcription start site. %: percentage.(DOCX)Click here for additional data file.Table S2Nrl-ko vs. wt photoreceptors). Genomatix software was used to perform an unbiased analysis of sequences +500 bp from the NRL ChIP–Seq peak center for over-represented TFs. The commonly enriched TFs in Illumina and ABI data are shown. The number of input sequences with match, matches in input from ABI data were shown. The enriched TFs are ranked by Z scores, and TFs in gray are not significantly enriched (Z score<2). Down in Nrl-ko: ChIP–Seq peaks associated with genes down-regulated in Nrl-ko mouse photoreceptors . Up in Nrl-ko: ChIP–Seq peaks associated with genes up-regulated in Nrl-ko mouse photoreceptors . Nrl-ko: Nrl knock out mice. wt: wild type mice.TF enrichment analysis of NRL ChIP–Seq peaks associated with altered genes ((XLSX)Click here for additional data file.Table S3Nrl-ko photoreceptors. An unbiased analysis of sequences that are +500 bp from the NRL ChIP–Seq peak center was performed for over-represented TFs using Genomatix software. The TFs identified in both Illumina and ABI data are shown. The enriched TFs are ranked by Z scores. TFs in gray are not significantly enriched (Z score<2). Down in Nrl-ko: ChIP–Seq peaks associated with down-regulated genes in Nrl-ko mouse photoreceptors . Overlap with CRX: NRL ChIP–Seq peaks that overlap with CRX ChIP–Seq peaks. Non overlap with CRX: NRL ChIP–Seq peaks that do not overlap with CRX ChIP–Seq peaks. Nrl-ko: Nrl knock out mice.TF enrichment analysis of CRX-overlapping or non CRX-overlapping NRL ChIP–Seq peaks associated with genes down-regulated in (DOC)Click here for additional data file.Table S4Top 40 biological processes associated with genes at or near the CRX-overlapping or non CRX-overlapping NRL ChIP–Seq peaks. Genomatix software was used to perform an unbiased analysis of biological processes that are associated with the genes at/near the CRX-overlapping or non CRX-overlapping NRL ChIP–Seq peaks. The top 40 biological processes in Illumina and ABI data are shown. Overlap with CRX: NRL ChIP–Seq peaks that overlap with CRX ChIP–Seq peaks. Non-overlap with CRX: NRL ChIP–Seq peaks that do not overlap with CRX ChIP–Seq peaks. Photoreceptor-related/specific biological processes are highlighted in green.(DOC)Click here for additional data file.Table S5www.sph.uth.tmc.edu/retnet/). The top part of the table lists the retinal disease genes that have been identified, whereas the bottom part lists the candidate genes in the region of uncloned but mapped disease locus. The fold enrichment value was produced by dividing the total number of genes (reported in RefSeq) within each locus by the number of NRL target genes at the same locus.Identification of candidate retinal disease genes. Candidate retinal disease genes are based on the chromosomal location of the human orthologs of the NRL target genes that map within a mapped retinal disease locus reported in the RetNet database ((DOC)Click here for additional data file.Text S1Supplemental extended experimental procedures.(DOC)Click here for additional data file.


MicroRNAs are endogenous non-coding RNAs which negatively regulate the expression of protein-coding genes in plants and animals. They are known to play an important role in several biological processes and, together with transcription factors, form a complex and highly interconnected regulatory network. Looking at the structure of this network, it is possible to recognize a few overrepresented motifs which are expected to perform important elementary regulatory functions. Among them, a special role is played by the microRNA-mediated feedforward loop in which a master transcription factor regulates a microRNA and, together with it, a set of target genes. In this paper we show analytically and through simulations that the incoherent version of this motif can couple the fine-tuning of a target protein level with an efficient noise control, thus conferring precision and stability to the overall gene expression program, especially in the presence of fluctuations in upstream regulators. Among the other results, a nontrivial prediction of our model is that the optimal attenuation of fluctuations coincides with a modest repression of the target expression. This feature is coherent with the expected fine-tuning function and in agreement with experimental observations of the actual impact of a wide class of microRNAs on the protein output of their targets. Finally, we describe the impact on noise-buffering efficiency of the cross-talk between microRNA targets that can naturally arise if the microRNA-mediated circuit is not considered as isolated, but embedded in a larger network of regulations. The expression of protein-coding genes is controlled by a complex network of regulatory interactions. It is becoming increasingly appreciated that the post-transcriptional repression by microRNAs, a class of small non-coding RNAs, is a key layer of regulation in several biological processes. Since gene expression is a fundamentally stochastic process, the mixed network  has to reliably perform its functions in the presence of noise. In this paper we investigate the function of one of the recurrent architectures of this network, the microRNA-mediated feedforward loops, using a detailed analytical model and simulations. With this approach we show that these regulatory circuits are appropriately designed so as to control noise, giving a rigorous mathematical proof of a previously proposed biological intuition. Moreover the theoretical framework introduced in this paper allows us to make nontrivial predictions that are presently in agreement with observed features of microRNA regulation and that could be more specifically tested experimentally in the future. MicroRNAs (miRNAs) are endogenous small non-coding RNAs which negatively regulate the protein production of their targets in metazoans and plants. They are expected to target a substantial portion of the human genome Drosophila. It was shown Recent work, reviewed in It is by now well understood that gene espression is inherently a stochastic process The main goal of our paper is to introduce and solve analytically a stochastic model describing these incoherent FFLs in order to give a proof to this conjecture. Our results show that with respect to the simple gene activation by a TF, the introduction of a miRNA-mediated repressing pathway can significantly dampen fluctuations in the target protein output for essentially all the choices of input parameters and initial conditions. As a test of our analysis we also performed extensive numerical simulations which nicely agree with our analytical results. It is important to stress  that this noise buffering function is actually a precise consequence of the peculiar topolgy of the FFL. In fact, in order to fine-tune the level of a target protein one would not necessarily need a FFL topology. The same result could well be obtained with an independent miRNA (not under the control of the master TF which activates the target), but this choice would lead to strong fluctuations in the target expression. In the same theoretical framework we can show that the construction of an optimal noise filter does not necessarily imply a strong repression, in agreement with the observation that the miRNA down-regulation of a target is often modest Here we focus on the incoherent FFL in The miRNA action can direct translational repression or destabilization of target mRNAs The parameter The alternative choice of a degradation rate of mRNAs as a function of miRNA concentration does not yield significantly different results, as reported in The probability of finding in our cell exactly where In order to solve the master equation for We would like to emphasize that linearizing the Hill functions does not mean to linearize the model. In fact, even with a linearized dependence on the miRNA copy number, our model keeps a nonlinear contribution in the term encoding the target translation (due to the fact that it depends on both the number of miRNAs and mRNAs). As we will see later, this nonlinearity leads to non trivial consequences.Despite this nonlinearity, the moment generating function approach We now use the following properties of the moment generating function: Noise in protein expression is originated by the combination of two types of fluctuations: intrinsic and extrinsic ones. Intrinsic fluctuations are essentially due to the stochasticity of the gene expression process. Extrinsic ones, instead, are due to the environment. In the latter case a prominent role is played by the noise transmitted by upstream genes To show the noise buffering role of the miRNA-mediated incoherent FFL  we firstThe same fine-tuning of the mean target concentration achieved with a FFL could be equally obtained with an open circuit like the one in circuits . Note thStochastic equations are the natural formalism to describe a set of biochemical reactions when the number of molecules involved is small and thus fluctuations are important. As the number of molecules increases, the stochastic description smoothly converges, at least for linear systems, toward a deterministic one and stochastic equations can be substituited by ordinary differential equations (ODE). It is usually expected that even in the regime in which fluctuations cannot be neglected one could use ODE if interested only in the time evolution of the mean values. This approximation can be thought as a sort of “mean field” approach . However, similarly to what happens in statistical mechanics in the proximity of a critical point, it may happen that, even at the level of mean values, the ODE description does not coincide with the (more rigorous) stochastic one. These breakdowns between the two descriptions are known as “deviant effects” In a FFL, fluctuations of rrelated , becauseIn the previous sections we compared different regulatory circuits keeping the same amount of input noise, i.e. the same amount of fluctuations in the copy number of master TFs. Since the topology of a regulatory motif can have stronger effects on extrinsic rather than intrinsic noise Setting the rates of degradation  of the mixed-motif can further explain why it can be better suited for noise buffering. Firstly, fluctuations in RNA polymerase and ribosome abundance are possible sources of extrinsic noise in gene expression Secondly, delays in the action of regulators (miRNA or proteins) in the indirect pathway from the master TF to the target can damage the noise buffering function  its fluctuations are expected to be completely uncorrelated with the miRNA ones, implying a random sequestration of miRNAs. To disentagle this contribution from the dilution effect, we studied the case of a secondary target transcribed at the same effective rate of the FFL target, but with different levels of fluctuations see . In the DrosophilaFew cases of incoherent miRNA-mediated FFLs have been experimentally verified until now: a case involving c-Myc/E2F1 regulation On the purely computational side, it is interesting to notice that in Finally, a significant enrichment in oncogenes within the components of the FFLs was also observed The type of regulatory action which a miRNA exerts on its targets can be rather well understood looking at the degree of coexpression with the targets Our paper presents the first model explicitly built on the mixed version of the FFL. From a theoretical point of view, we addressed the detailed master equation describing the system (without neglecting the dynamics of mRNA), instead of the approximate Langevin description, and we were able to apply the moment generating function approach despite the presence of nonlinear terms that can give rise to deviant effects. This approach allowed us to take into account extrinsic fluctuations as the noise propagating from upstream genes, without an arbitrary definition of the extrinsic noise distribution. This strategy can be naturally extended to other circuits in the mixed network to test their potential role in the control of stochasticity.Furthermore, we compared, in terms of noise buffering ability, miRNA-mediated FFLs with their purely transcriptional counterparts, where the miRNA is replaced by a protein that inhibits transcription rather than translation. This comparison shows that a miRNA regulator can be better suited for the noise buffering purpose.Finally, we tryed to overcome the limitations in the analysis that can arise from considering a genetic circuit as operating in isolation. In this perspective, we evaluated the impact that the recently discovered dilution effect The hypothesis of a role of miRNAs in noise buffering can shed new light on peculiar characteristics of miRNA regulation. As discussed in Simulations were implemented by using Gillespie's first reaction algorithm Text S1Details on the theoretical model, supplementary analysis, and simulations.(0.75 MB PDF)Click here for additional data file.


Drosophila.A systematic analysis reveals a regulatory network controlling selective odorant receptor expression and neuronal diversity in  Drosophila. A systematic, RNAi-mediated knock down of most of the predicted transcription factors identified an essential function of acj6, E93, Fer1, onecut, sim, xbp1, and zf30c in the regulation of more than 30 ORs. These regulatory factors are differentially expressed in antennal sensory neuron classes and specifically required for the adult expression of ORs. A systematic analysis reveals not only that combinations of these seven factors are necessary for receptor gene expression but also a prominent role for transcriptional repression in preventing ectopic receptor expression. Such regulation is supported by bioinformatics and OR promoter analyses, which uncovered a common promoter structure with distal repressive and proximal activating regions. Thus, our data provide insight into how combinatorial activation and repression can allow a small number of transcription factors to specify a large repertoire of neuron classes in the olfactory system.The mechanism that specifies olfactory sensory neurons to express only one odorant receptor (OR) from a large repertoire is critical for odor discrimination but poorly understood. Here, we describe the first comprehensive analysis of OR expression regulation in  Drosophila olfactory system located in the antenna. This system contains 34 neuron classes with different functions and connections, each defined by the unique odorant receptor they express. We set out to identify the transcription factors (regulatory genes) that are required for each class to express the correct odorant receptor. We find that seven transcription factors are continuously required in different combinations for the expression of all tested 32 odorant receptors. We also show that these transcription factors can both turn on and turn off odorant receptor genes, making the expression regulation more economical. We conclude that dual use of a small set of factors, which are always on in the neuron, can define its functional class and thereby produce diversity in the nervous system.Our nervous system has a daunting diversity; it contains 100 billion neurons that all have defined functions and connections. To address how neuronal diversity is produced, we have turned to a complex but defined set of neurons, the  The external world is perceived by peripheral neurons that each expresses only one or a stereotyped set of receptors from a large genomic repertoire In the mouse olfactory system, each olfactory sensory neuron (OSN) chooses to express one odorant receptor (OR) out of approximately 1,200 OR genes Drosophila ORs are expressed in a salt and pepper pattern within domains of the antenna OSNs  screen for direct regulators of Drosophila we used the transgenic UAS-driven inverted repeats (IRs) from the Vienna Drosophila RNAi Center (VDRC) Drosophila (www.flytf.org) pebbled-Gal4 CD8::GFP -, Fer1-, onecut-, sim-, xbp1-, and zf30-IRs , National Institute of Genetics , and Transgenic RNAi Project  that corresponded to each of the seven genes were analyzed. All constructs gave rise to identical phenotypes  gave similar phenotypes compared to the RNAi lines , which encodes a protein with ten C2H2 zinc finger domains and Fer1 (Forty eight related 1), encoding a bHLH factor, had not previously been characterized.All identified TFs belonged to different protein families : Acj6   expressed Or92a and Or47b at the correct antennal location  . At the all OSNs . Flies mlocation . In contwas lost .acj6, E93, Fer1, sim, onecut, and xbp1 did not affect adult OR expression . Seven TFs can in theory separate 27 = 128 combinations, and ten TFs designate more than 1,000 combinations, suggesting a large number of unused combinations. This surplus of combinations may be due to the inherent randomness of evolution and the impossibility of creating a streamlined code by chance. Another possibility for this large number is the need for a high degree of fidelity, with little or no ectopic OR expression tolerable for proper functioning of the olfactory system. Extrapolation of our observations to the regulatory requirements of the mammalian olfactory system indicates that at least 200–300 TFs would be required to provide a regulatory system that controls >1,000 mammalian ORs, a daunting number. Therefore, it is reasonable to suspect that the stochastic OR selection mechanism found in vertebrates was added during evolution to accommodate the heavy increase in regulatory costs resulting from an expanded number of OR genes.How many OR selector genes are required to uniquely express one OR in each OSN class? We identified seven OR selector genes, but given the limitations of RNAi, it is likely that there are a total of at least ten critical TFs to specify all OSN classes. Even this probably low estimate generates a rather high number of TFs considering that To date very few TFs have been found to be restricted to small neuronal populations in neuroepithelia or in the developing brain in general Pebbled-GAL4, UAS-Dicer2, and the OR promoter fusions were mated with males obtained from the VDRC library. The crosses were set up at 25°C, and after 3 d the parental flies were removed and the vials shifted to 27°C. 2–3 d after eclosure, the GFP levels corresponding to OR expression were ranked 0–5, where 5 corresponded to the wild-type level. For all assays, for five females per line crosses were scored blind to the genotype and all lines with phenotypes scored below 2 were retested. A line was considered to have established phenotype if three consecutive crosses included flies that scored below 2. To further validate the established phenotypes, RNAi lines from the VDRC, NIG, and the Transgenic RNAi Project (TRiP) were used, all the different lines gave the same phenotype as in the screen , mouse anti-Acj6 , mouse anti-Neuroglian , and Rabbit anti-GFP .http://www.genomatix.de/) http://www.gene-regulation.com/) 1 kb upstream the translational start site of each OR was scanned with the motifs for HNF6 and BRN3 using weight matrices and programs provided by Genomatix . For the binding assay, single-stranded DNA oligonucleotides were end-labeled with T4 polynucleotide kinase (Roche) and G-32-P ATP (PerkinElmer) with T4 polynucleotide kinase according to the manufacturers' instructions, annealed with the complementary strand, and purified on a microspin column (Roche).Binding reactions were performed at room temperature for 20 min. The binding reaction included 3 µl recombinant glutathione S-transferase fusion proteins, 3 fmol labeled probe, 10 mM HEPES (pH 7.9), 70 mM KCl, 1 mM DTT, 1 mM EDTA, 2.5 mM MgCl2, 4% glycerol, and 1 µg poly (dI/dC) (VWR). Cold competition was performed by adding DNA oligonucleotides in molar excess 15 min before addition of labeled probe. The samples were separated on a 6% acrylamide TBE gel at 60 V for 90 min. Gels were dried and visualized by the FLA-5100 Multi Gauge system (FujiFilm).Pebbled-GAL4 and 6acj6mutant flies were kind gifts from L. Luo. H9sim was kindly provided by C. Klaembt. UAS-Dicer2 and the TF-IRs for the screen were provided by the VDRC. Additional TF-IR lines were obtained from NIG and TRiP. sim-lacZ flies were obtained from the Szeged Drosophila Stock Centre , and k13803xbp1, k02506zf30c, zf30c-lacZ, tstubP-Gal80 were obtained from the Drosophila Stock Center . For the promoter studies all DNA constructs were injected into 1118w flies, and six to 12 lines were analyzed.OR promoter fusion lines have previously been described Figure S1TF knockdown correspond to loss of TF mRNA. In situ labeling of each TF (red) and DAPI (blue) performed on TF-IR antenna, note the tight correlation of loss of xbp1 (red) and Or92a-CD8::GFP expression (green).(TIF)Click here for additional data file.Figure S2Olfactory sensory neuron layers and morphology are unperturbed in the TF knock downs. Antenna from TF-IR flies stained for neuronal markers in red (Neuroglian and Elav) and counterstained with DAPI.(TIF)Click here for additional data file.Figure S3None of the seven TFs were required for OR expression to one antenna domain or sensilla group. Regulatory matrix arranged after the five antenna domains (blue to red) and each sensilla group. Each domain is exemplified by one OR promoter fusion in green, counterstained with ELAV in red. Note that at least three of the seven TFs are required for expression in each sensilla group .(TIF)Click here for additional data file.Figure S4Predicted DNA motifs are bound by the identified TFs in vitro. (A) Electrophoretic mobility shift assay (EMSA) performed with radiolabeled probe containing the vertebrate Xbp1 core sequence with (+) or without (−) recombinant Xbp1. Increasing amounts  of nonlabeled probe were used as cold competitors; 900-fold excess of a probe carrying a mutated motif is shown in the lane labeled “m.” (B) Acj6, onecut, and Xbp1 motifs upstream of four ORs. (C) Expanded EMSA validation of the predicted Acj6, Xbp1, and onecut motifs from the four OR promoters. Radiolabeled motif probe (P) and competition with cold (C) motif probe at 900-fold excess are shown.(TIF)Click here for additional data file.Table S1All tested IRs and mutants for each TF gave rise to identical phenotypes. Statistics related to (DOC)Click here for additional data file.Table S2TF-IRs phenotypes for the 32 ORs in the regulatory matrix. Statistics related to (XLS)Click here for additional data file.Table S3Motifs upstream all 32 analyzed ORs. Statistics related to (DOC)Click here for additional data file.


Hrp1 binding sites are the major characteristic of terminators; these binding sites are often clustered in terminator regions and can terminate transcription bidirectionally. The UM predicts global transcript structure by modeling transcription of the genome using a hidden Markov model whose emissions are the outputs of the initiation and termination classifiers. We validated the novel predictions of the UM with available RNA-seq data and tested it further by directly comparing the transcript structure predicted by the model to the transcription generated by the cell for synthetic DNA segments of random design. We show that the UM identifies transcription start sites more accurately than the initiation classifier alone, indicating that the relative arrangement of promoter and terminator elements influences their function. Our model presents a concrete description of how the cell defines transcript units, explains the existence of nongenic transcripts, and provides insight into genome evolution.Identifying genes in the genomic context is central to a cell's ability to interpret the genome. Yet, in general, the signals used to define eukaryotic genes are poorly described. Here, we derived simple classifiers that identify where transcription will initiate and terminate using nucleic acid sequence features detectable by the yeast cell, which we integrate into a Unified Model (UM) that models transcription as a whole. The  Moreover, the existence of physical connections between the 5′ and 3′ ends of genes that depend on proper 3′ end formation , which recognizes the TATA box. Although there appear to be TATA-like elements bound by Spt15 in most promoters , which is loaded onto the promoter region via the general transcription factors (GTFs) . Howeverromoters , only a romoters , and TATromoters , but it romoters . Moreoveromoters . The exaromoters  and has romoters  which isromoters .cis by transcription over the promoter region, which results in transcriptional interference or histone modification  immediately upstream of the TSS . Yeast pfication .7G cap and becomes a substrate for 5′→3′ exonucleases that degrade the nascent RNA, leading to destabilization of the Pol II/RNA association and termination of transcription (Termination of coding transcripts in yeast (and other eukaryotes) occurs when a cleavage and polyadenylation (CPA) site is recognized, mRNA cleavage occurs, and a polyadenine tail is added, producing a mature mRNA. Cleavage tends to occur over a range of about 36 bases and prefers to occur at (C/G)AA motifs , which mcription . CPA sitcription , the A-rcription , and sevcription . In addicription . For exacription  and the cription . The CUTDespite this extensive literature describing the sequences and factors involved, there has been no global examination of which features are critical to yeast gene identity, and therefore, the problem of how yeast (or any other eukaryote) delineates transcription units remains an open question. Many programs to identify genes in genomic DNA exist e.g., , but thetrans-acting factors are most influential and which cis-elements help to define individual genes. Despite the fact that we did not incorporate CUTs into the training procedure, our model predicts bidirectional transcription from unidirectional promoters, indicating that the same sequence features generally drive transcript initiation for both mRNAs and CUTs and that CUTs are an inherent, and possibly unavoidable, feature of yeast promoters. Our model indicates that yeast CPA sites are also generally bidirectional, suggesting that convergent genes use the same termination elements. We combined these classifiers into a unified model that can predict where transcription will initiate more accurately than the initiation classifier alone, indicating that the relative arrangement of promoter and terminator elements in cis is a likely mechanism for orienting promoters. We go on to verify the predictions of the model using both existing data and experiments of our own design. This is the first model that describes how a eukaryotic cell defines transcript structure genome wide.Here, we have created a computer model that can explain genome-wide yeast transcript structure and is supported by multiple lines of experimental validation. The model is underpinned by classifiers that mimic the choices the cell makes when initiating and terminating transcripts. These classifiers indicate that the transcription of most yeast genes can be explained by relatively few features and reveal which We first sought to ask how well we could explain the specific recognition of yeast promoters and CPA sites by cellular factors, using the known sequence features associated with these elements. This question can be framed as a computational classification problem, in which algorithms seek to classify input sequences as positives  or negatives  on the basis of features in the sequences . We refer to the two resulting classifiers as the “initiation” and “termination” classifiers because they identify the regions where transcriptional units initiate (promoters) and terminate (CPA sites).Because we wanted the models to mimic cellular mechanisms, we restricted the input features to those that can be realistically sensed by nuclear factors, including predicted TF binding sites, nucleosome-excluding sequences, and DNA structural features, as well as the binding sites for RBPs and nucleotide content for genomic regions encompassing transcripts (since NTP concentrations can affect Pol II elongation rates) . For botWe created our classifiers using Random Forests , an apprtrans-acting factors tend to bind distinct sets of promoters , consistent with independent function.To minimize overfitting of the models, remove redundant features, and make the models easier to interpret, we sought to reduce the number of included features. We did this by iteratively rebuilding the model, adding one feature at a time, with features sorted by decreasing “importance” , and retaining only those that appreciably improved the performance of the model measured on the “refinement” data . This feature reduction procedure only slightly decreased the performance of each classifier on the “test” data, while greatly reducing the numbers of features (The initiation classifier was largely dependent (14/15 features) on six main types of features: binding sites for the GRFs Reb1, Abf1, and Rap1; binding sites for Rsc3 (a putative GRF and component of the RSC complex) ; DNA strThe termination classifier was reduced to only two feature types : G/C conWe next asked how well the classifiers identify the TSS and CPA sites on a per-base level. Despite the low intrinsic resolution of the predictors , the majority of bases used as TSSs/CPA sites in promoter/terminator regions  lie nearStrikingly, the key features for both the classifiers contain little strand specificity. The most important features of the initiation classifier (GRF sites and G/C content/poly[A] sequences) are thought to function through their role in NDR formation  and so sBoth the Hrp1 motif and G/C content are symmetric, and correspondingly, the termination classifier's predictions are also highly correlated between the forward and reverse DNA strands (Pearson R ≈ 0.8 when offset by 90 bp), suggesting that the same cleavage signals can operate in either orientation. In fact, 42% of convergent genes have only one optimal Hrp1 site between them. Further, RNA-seq reads containing poly(A) sequences  indicatetrans-acting factors: Abf1, Reb1, Rap1, Rsc3, and Spt15 (TBP)  . For a gcis-elements are sufficient to initiate gene expression in vivo using a combinatorial library of promoter constructs driving GFP, embedded in a context that otherwise has no promoter-like properties. These constructs encompassed the parts of the promoter most critical to the model (from −150 to +80 relative to the TSS). We designed the sequences in three different sections  (We obtained a strong and significant correlation between initiation score (a number between 0 and 1) and GFP expression level  , indicatd P ≈ 0) , and thed P ≈ 0) . The impd P ≈ 0) . Nonethed P ≈ 0) . OverallWe next created a “Unified Model” (UM) aimed at describing the full process of transcription. The UM uses a hidden Markov model (HMM), which is ideal for this purpose because it can probabilistically model the states of Pol II as it initiates, transcribes, and terminates transcription in a similar way to how it occurs in the cell. In essence, an HMM takes as inputs a model structure as in  and a seThe UM states correspond to TSSs, transcript bodies (“Gene”), and CPA sites on the forward and reverse strands, as well as a state corresponding to bidirectional transcript termination and a state for intergenic DNA . The modTo overcome these limitations, we defined a new transcript map that could be used in deriving the HMM parameters. In our initial analyses (data not shown), the model outputs  were surprisingly sensitive to the choice of parameters and the transcript maps from which they were derived. Ultimately, we derived the parameters using a transcript map that divided the genome into the eight HMM states based on multiple types of RNA-seq data  see Sup. We specGAL1-10 locus (and surrounding region), located on chromosome 2, is shown in P ≈ 0), and the precision is 76.9%  . For comparison, random guessing with a probability of 42.1% (the proportion of the genome encompassed by a transcript on a given strand) would yield precision and recall values of 42.1%. TSSs and CPAs are much more sparse than transcripts, yet the ROC and precision-recall analyses indicate that the UM is also adept at identifying these elements  , indicatP < 10−7). Remaining inconsistencies between the model and the annotated transcripts are split approximately evenly between predicting transcription on the wrong strand (15.3% of predicted transcribed bases) and predicting transcripts in intergenic regions . GAL10 . GAL10 , transcrP ≈ 0), and 39.0% of intergenic bases that are predicted to be transcribed are supported by the same RNA-seq data . Finally, examining complete transcript predictions that do not overlap known features , we find that available RNA-seq , into which we randomly inserted binding sites for Rap1, Abf1, Rsc3, Reb1, and Spt15, to reduce the amount of DNA to be synthesized . The 3-kb fragments we synthesized contained an average of 3.75 Spt15, 2.25 Abf1, 0.75 Reb1, 0.25 Rap1, and 3.25 Rsc3 consensus binding sites each, which is only slightly more than the number expected in completely random sequences of the same length . We did not intentionally add CPA sites because they appear very frequently in randomly generated DNA sequences. We integrated these constructs into the genome and assayed expression using a custom tiling array.A1-a/A1-b, B1-a/B1-b, B2-b/B2-c), as well as numerous instances of convergent transcripts that appear to terminate at the same bidirectional CPA site . Of the 12 easily distinguished transcripts, nine are robustly predicted by the UM . There are several examples where transcripts are generated on both DNA strands. These cases are difficult for the UM to predict because it has no states representing transcripts on both strands; nonetheless, in both instances of overlapping transcripts (A2-a/A2-b and kan and transcripts generated antisense to kan), the model is uncertain and partially predicts transcripts on both strands. In other cases, it is uncertain which strand is transcribed when the data support the transcription of only one strand under the condition tested . Of the easily distinguished transcripts, our model achieves a precision and a recall of 0.69, on a transcript-by-transcript basis, allowing a 200-base offset for TSS and CPA sites. Because the model is incapable of simultaneously predicting both of the overlapping transcripts A2-a and A2-b, the theoretical maximum is a precision and recall of 0.92. By simulating random guessing for the positions and strands of transcripts, but assuming the number and sizes of transcripts are known, the upper bound of what is expected by chance is a precision and recall of about 0.029. Altogether, these results demonstrate that transcripts can arise from a pseudorandom sequence at a predictable frequency.The expression data shown in Saccharomyces cerevisiae is relatively simple, and that transcription occurs as a probabilistic process that can be faithfully captured in a hidden Markov model. Both the models and the experimental data described here indicate that, rather than simply being a property of promoters, promoters are defined by the presence of an NDR, resulting from sequences that inherently inhibit nucleosome formation, such as G/C content and poly(A) sequences, or binding sites for a small number of chromatin-modifying TFs, mainly GRFs. Most of the features originally tested in the initiation classifier correspond to motifs for other TFs, and so the slight loss in the initiation classifier's performance when reducing the number of features . Such a mechanism could have a role in preventing transcriptional interference between adjacent genes by minimizing overlapping transcription and could also contribute to genome compaction.The termination classifier is both remarkably accurate  and verycis-regulatory sequence . Additional methodological descriptions are available in the Supplemental Methods and Analysis.Supporting data and model predictions are available on our website  , nucleosWe divided each chromosome into positive and negative examples for each classifier, where the initiation classifier compared examples of promoters to nonpromoter sequences, and the termination classifier compared the CPA sites of genes to gene bodies. We trained the classifiers on examples from half of the yeast chromosomes, and within these eight chromosomes, we left one chromosome out for evaluation of performance and optimization of the models. For each chromosome left out, we made four replicate forests, each with 50 trees. Thus, each classifier consisted of 1600 decision trees and was trained on half the genome.We designed several hundred promoter segments encompassing the most important feature bins of the initiation classifier. We generated random DNA sequences, embedded TFBSs within each, and scored them by the initiation classifier in every possible combination. We selected those sequences that had relatively high initiation scores in multiple contexts for synthesis, and designed corresponding control sequences which were identical, but with the added TFBS perturbed. We also designed high and low G/C content promoter segments that either score highly or lowly, but into which we did not specifically add TFBSs. TFBSs were generated by sampling from the position frequency matrix of the corresponding factor using the frequencies as weights. The segments had 43, 48, and 41 different sequences in the −150:−80, −80:TSS, and TSS:+80 positions, respectively, and these had complementary overhangs at either end that ensured the segments would combine in the desired order and would yield, in theory, 86,688 different promoters.E. coli. We then isolated the resulting library and transformed it into yeast cells. We sorted the pooled yeast library by flow cytometry into six fluorescence bins and grew the sorted cells overnight. We then isolated DNA and amplified, barcoded, and sequenced the promoter DNA to determine the proportion of each promoter in each bin. We calculated the expression level of each promoter by weighting the probability of each promoter ending up in each bin by the average fluorescence of the bin.We ordered the promoter parts as single-stranded oligonucleotides from Sigma-Aldrich. To generate double-stranded promoter parts, we phosphorylated and annealed the forward and reverse strands for each sequence. We then pooled and ligated these double-stranded promoter segments in bulk, purified and cloned the resulting promoters into an expression vector containing GFP, and transformed them into http://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE47004 and GSE48860. Supplemental and unprocessed data are also available at the author's website: http://hugheslab.ccbr.utoronto.ca/supplementary-data/transcription_model/.The data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO;


Saccharomyces cerevisiae, the conserved TREX complex couples transcription to mRNA export and mediates mRNP formation. Here, we show that TREX is recruited to the transcription machinery by direct interaction of its subcomplex THO with the serine 2-serine 5 (S2/S5) diphosphorylated CTD of RNA polymerase II. S2 and/or tyrosine 1 (Y1) phosphorylation of the CTD is required for TREX occupancy in vivo, establishing a second interaction platform necessary for TREX recruitment in addition to RNA. Genome-wide analyses show that the occupancy of THO and the TREX components Sub2 and Yra1 increases from the 5′ to the 3′ end of the gene in accordance with the CTD S2 phosphorylation pattern. Importantly, in a mutant strain, in which TREX is recruited to genes but does not increase towards the 3′ end, the expression of long transcripts is specifically impaired. Thus, we show for the first time that a 5′-3′ increase of a protein complex is essential for correct expression of the genome. In summary, we provide insight into how the phospho-code of the CTD directs mRNP formation and export through TREX recruitment.Messenger RNA (mRNA) synthesis and export are tightly linked, but the molecular mechanisms of this coupling are largely unknown. In  Gene expression is a fundamental cellular process that translates the information stored in the DNA into proteins, the workhorses of the cell. Eukaryotic cells contain a nucleus, where the genetic information is stored and transcribed by RNA polymerase II into messenger (m)RNAs. These copies of the blueprint of life need to be exported to the cytoplasm for protein production. Interestingly, mRNA synthesis is coupled to nuclear mRNA export. The protein complex TREX mediates this coupling of transcription to mRNA export. To assess the recruitment mechanism of TREX to genes we analyzed the presence of TREX over the whole genome in budding yeast. We found that there is more TREX at the end than at the beginning of genes. TREX binds to a subunit of RNA polymerase II, phosphorylation of which increases over the gene mediating the increase in TREX. Importantly, this increase in TREX over genes is important for normal levels of long transcripts. Thus, we show for the first time that a gradual increase of a protein complex is important for correct expression of the genome. We propose that TREX functions to keep the mRNA in the vicinity of the transcription machinery for correct processing and mRNP formation. Interestingly, these downstream processes occur co-transcriptionally and are intimately linked to each other and to transcription to ensure efficient mRNA biogenesis Gene expression is a fundamental process of every living cell. In eukaryotes, RNA polymerase II (RNAPII) transcribes protein-coding genes to synthesize messenger RNA (mRNA). In addition to RNAPII, a plethora of transcription factors is needed for efficient and regulated transcription in vivo. Transcription initiation factors recruit RNAPII to the promoter, whereas transcription elongation factors ensure efficient passage of RNAPII through the transcribed region Saccharomyces cerevisiae, the pattern of Y1 phosphorylation (Y1P) resembles that of S2 phosphorylation (S2P), with the exception of an earlier decrease at the poly(A) site The transcription cycle and the co-transcriptional processing of the mRNA are coordinated by the differential phosphorylation of the C-terminal domain (CTD) of Rpb1, the largest subunit of RNAPII S. cerevisiae, the conserved TREX complex couples transcription to mRNA export In addition to the CTD, a multitude of proteins and protein complexes link transcription to one or several downstream events. In The TREX components Sub2, Yra1, Gbp2 and Hrb1 are thought to be transferred to the mRNA during packaging of the mRNA into a ribonucleoparticle (mRNP) TREX is recruited to all protein-coding genes but seems to be especially important for the expression of long and GC-rich transcripts since these are less abundant in deletion mutants of TREX Here, we show that the occupancy of THO, Sub2 and Yra1 at genes increases from the 5′ to the 3′ end of the ORF and with gene length using ChIP-chip. A ChIP-based assay with a reporter construct containing a self-cleaving ribozyme shows that recruitment of TREX is at least partially RNA-dependent, but its 5′ to 3′ increase cannot be explained by RNA length. Instead, increasing TREX occupancy is most likely mediated by direct interaction of its subcomplex THO with the S2/S5 diphosphorylated CTD of RNAPII. Consistently, phosphorylation of the CTD on S2 is necessary for TREX recruitment in vivo. In contrast, THO recruitment is independent of Yra1's CTD-binding domain. Importantly, the 5′-3′ increase in TREX occupancy is crucial for the correct expression of long genes. This suggests that the CTD phospho-code dictates mRNP assembly and export through recruitment of TREX.S. cerevisiae, several proteins have been proposed to function as Mex67-Mtr2 adaptor proteins to the mRNA. This includes the TREX complex members Yra1 and Hpr1 as well as the mRNA-binding proteins Nab2 and Npl3. To assess whether these mRNA adaptors are differentially recruited to genes we assessed the genome-wide occupancy of individual TREX components, Nab2 and Npl3 by using high density tiling arrays for the analysis of chromatin immunoprecipitation (ChIP) experiments. TREX, Nab2 and Npl3 are recruited to all actively transcribed protein-coding genes with a slight preference of Npl3 for intron-containing genes and of Yra1 for intron-less genes (data not shown). This is consistent with previous data showing that Hpr1 and Sub2 are recruited to all ORFs The Mex67-Mtr2 heterodimer is recruited to mRNAs co-transcriptionally via association with multiple, distinct mRNA-binding proteins bona fide transcription elongation factors, such as Spt5, Spt6, Spt16, Bur1 and Paf1, does not increase over the length of the ORF  to correct for transcription activity, and the average normalized occupancy of TREX components was plotted for each length class . As expene class . This inne class . Taken tIn contrast, the occupancy of TREX components Gbp2 and Hrb1 decreases slightly from 5′ to 3′  and incrS. cerevisiae according to The 5′ to 3′ increase in TREX recruitment may be explained by interaction with the mRNA and/or the C-terminal domain (CTD) of Rpb1, the largest subunit of RNA polymerase II (RNAPII). First, we assessed whether the increased association of TREX towards the 3′ end of genes is caused by interaction with the growing mRNA. In order to assess the occupancy of a chosen protein dependent on the length of the RNA, it is necessary to cut the mRNA at a specific position to shorten the nascent RNA to a defined length in relation to which TREX occupancy can be measured. To do this, we established a hepatitis δ ribozyme based ChIP assay in i.e. at the same genomic position but with a longer nascent mRNA, it is unaffected 0.7 kb and further downstream of the cleavage site  is not necessary for initial recruitment of TREX to the 5′ end of genes, but rather ensures TREX occupancy along the gene unit Consistent with earlier observations Although highly speculative, the interaction of THO with the phospho-CTD could be conserved in metazoans. A direct interaction of TREX with the transcription machinery might be the basis for the association of TREX with naturally intronless mRNAs via a sequence element termed CAR-E i.e. via THO, with the phosphorylated CTD. During transcription elongation these mRNA-binding proteins are then transferred from the CTD to the mRNA packaging it into an mRNP.Although the major components of TREX, THO, Sub2 and Yra1, show a 3′ biased distribution, Gbp2 and Hrb1, two members of the TREX complex, do not. The mRNA-binding proteins, Nab2 and Npl3, also show equal occupancy across both long and short genes. We hypothesize that these mRNA-binding proteins are transferred to the mRNA during transcription elongation and thus leave the transcription site. Interestingly, Npl3 – which is not a TREX component – also binds to the S2 phosphorylated CTD THO2, in which TREX is recruited to the gene but does not increase towards the 3′ end of the gene (Tho2-TAP), we show that the 5′-3′ increase in TREX occupancy is important for the correct expression of long genes. THO, Sub2 and Yra1 might be needed at higher levels towards the 3′ end of genes, to keep the nascent mRNA in the vicinity of the CTD , accession number E-MTAB-1400.Chromatin immunoprecipitation (ChIP) assays were performed according to THO2-TAP strain was defined as a >1.7-fold change versus the average wild-type with a p-value of <0.05. The average length, GC-content, expression level, RNAPII occupancy, convergence, divergence, +1 and +2 nucleosome positioning of the up- and down-regulated genes was calculated and the statistical significance determined using the Wilcoxon rank sum test. SAGA and TFIID dominated genes were analyzed for expression changes versus all promoters using the Wilcoxon rank sum test. The microarray gene expression data has been deposited in ArrayExpress (www.ebi.ac.uk/arrayexpress/), accession number E-MTAB-1892.Tho2-TAP and wild type cells were grown in SDC media, RNA was isolated and hybridized in dye-swap biological replicate to dual-channel 70-mer oligonucleotide arrays to obtain four measurements as previously described Tandem affinity purifications (TAPs) were essentially done as described previously Pulldown assays were performed as described previously with following modifications www.ebi.ac.uk/arrayexpress/), accession numbers E-MTAB-1400 (ChIP-chip data) and E-MTAB-1892 (microarray gene expression data).Raw and normalized data are available at ArrayExpress  Peak occupancies of the indicated proteins relative to RNAPII (Rpb3) on protein coding genes. The lower and upper borders of the boxes reflect the 25% and 75% quantiles, respectively, the black lines are the median values and the whiskers extend to the 1.5-fold inter quartile range. The red line gives the ratio 0, corresponding to no recruitment, and the dashed red line represents a ratio of 2. (B) Peak occupancies as in (A) but for sn/snoRNA genes.(TIF)Click here for additional data file.Figure S2bona fide transcription elongation factors does not increase during transcription elongation. (A) Meta gene occupancy profiles and (B) peak occupancy according to length classes for RNAPII (Rpb3) (only A), the transcription elongation factors Spt5 (only B), Spt6, Spt16, Bur1 and Paf1 and the TREX component Hpr1. The peak occupancies in (B) were normalized to the peak occupancy of RNAPII to correct for the rate of transcription.The occupancy of (TIF)Click here for additional data file.Figure S3TREX occupancy increases with gene length. (A–Q) Meta gene occupancy profiles of RNAPII (Rpb3) (A), the CTD phosphomarks Y1P (B), S2P (C) and S5P (D), the transcription elongation factors Spt5 (E), Spt6 (F), Spt16 (G) and Paf1 (H), the THO components Tho2 (I), the mis-recruited allele Tho2-TAP (J), Hpr1 (K) and Mft1 (L), the TREX components Sub2 (M), Yra1 (N), Gbp2 (O) and Hrb1 (P) and the mRNA-binding proteins Nab2 (Q) and Npl3 (R) were calculated for different gene classes. The gene classes were defined as in (TIF)Click here for additional data file.Figure S4The 5′ to 3′ increase of TREX is independent of antisense transcription. The peak occupancy of TREX components Tho2, Hpr1, Mft1, Sub2 and Yra1 increases with gene length when genes containing a SUT or CUT 250 bp up- or downstream of the ORF were excluded from the calculation (TIF)Click here for additional data file.Figure S5th percentile of each profile for each gene) of each protein were correlated for all protein coding genes. As expected, the general elongation factors Spt5, Spt6 and Elf1 correlate very highly with each other. Also RNAPII (Rpb3) and the phospho-CTD marks Y1P, S2P and S5P correlate very well with each other and with the general elongation factors. S2P strongly correlates with THO subunits, Yra1 and Sub2, and more weakly with general elongation factors. As expected, the THO subunits, Sub2 and Yra1 correlate highly with each other. Due to their lack of length dependency Gbp2 and Hrb1 correlate less with THO subunits, Sub2 and Yra1, but highly with Nab2 and Npl3.Pearson correlation coefficients between TREX components, Nab2, Npl3 and general elongation factors. The peak occupancies (90(TIF)Click here for additional data file.Figure S6S2 phosphorylation is essential for recruitment of Sub2 and Yra1. The occupancies of Sub2 and Yra1 in the S2A mutant strain (white bars) were calculated relative to the occupancy in a strain with 14 wild-type CTD repeats (black bars). Results of at least 3 independent experiments are shown .(TIF)Click here for additional data file.Figure S7Tandem affinity purification (TAP) of the THO complex used in the CTD pulldown experiments. A strain expressing C-terminally TAP-tagged Hpr1 and C-terminally His6-tagged Mft1 was purified by two steps using IgG and Ni affinity purification under low salt (100 mM NaCl) and high salt (1000 mM NaCl) conditions yielding the whole TREX complex or the THO complex consisting of Tho2, Hpr1, Mft1, Thp2 and Tex1, respectively. (A) Coomassie stain of eluates after Ni affinity purification. The identity of each protein was verified by mass spectrometry and is indicated to the right. (B) Yra1 is absent from high salt purified THO complex. Western blot against Yra1 using an antibody directed against Yra1.(TIF)Click here for additional data file.Figure S8THO2-TAP cells.  TREX is recruited to the PMA1 gene but its occupancy does not increase in the THO2-TAP mutant. To assess the occupancy of Hpr1 and Yra1 in the presence of the TAP-tag on Tho2, they were tagged with the Avi-tag. Occupancy of Hpr1 (A) and Yra1 (B) at the PMA1 gene in a wt and the THO2-TAP strain. Results of 3 independent experiments are shown . (C) TAP-Tho2 and Tho2-TAP assemble into the TREX complex. TAP-Tho2 and Tho2-TAP were purified by tandem affinity purification. A Coomassie stain and Western blots against Yra1 and Sub2 of the whole cell extract  and the calmodulin eluate  are shown.TREX is intact and recruited to genes at the 5′ end, but its occupancy does not increase towards the 3′ end of the gene in (TIF)Click here for additional data file.Figure S9THO2-TAP strain. The lines indicate the average of all genes, the bars represent the average of up- or down-regulated genes, respectively, the error bars indicate the SEM and the p-value was calculated using the Wilcox rank sum test. (H) Changes in expression of all genes were compared to SAGA or TFIID promoter dominated genes The expression of gene classes other than length does not change in the THO2-TAP strain compared to a wt strain. Up- and down-regulated genes were analysed for their (A) expression level, (B) RNAPII levels, (C) GC-content, (D) convergent and (E) divergent gene spacing and for the positioning of the (F) +1 and (G) +2 nucleosome. No statistically significant effects were present in the (TIF)Click here for additional data file.Figure S10THO2-TAP is synthetically lethal with yra1-ΔPCID. Growth of strains expressing Yra1 or yra1-ΔPCID and either no tagged protein, Hpr1-TAP or Tho2-TAP and carrying the plasmid pRS316-YRA1 on SDC(-leu) and 5-FOA, which counterselects against the URA3-encoding pRS316 plasmid. yra1-ΔPCID is synthetically lethal with THO2-TAP, which causes an aberrant TREX occupancy profile, but not with HPR1-TAP.(TIF)Click here for additional data file.Figure S11The occupancy of transcription elongation factors binding to phosphorylated serine 2 or serine 5 does not increase during transcription elongation. (A) Meta gene occupancy profiles and (B) peak occupancy according to length classes for Spt6, Rtt103, Rna15, Pcf11 and Npl3 (S2P binders), Bur1 (S5P binder) and the TREX component Hpr1.(TIF)Click here for additional data file.Table S1Yeast strains used in this study.(DOCX)Click here for additional data file.Table S2Plasmids used in this study.(DOCX)Click here for additional data file.Table S3Sequences of the CTD peptides used in the pulldown experiments.(DOCX)Click here for additional data file.Text S1Supporting Materials and Methods and References.(DOCX)Click here for additional data file.


Our data show STAT5A and STAT5B play redundant roles in cell proliferation and apoptosis via SGK1 interaction. Interestingly, we found a novel, unique role for STAT5A in binding to genes involved in neural development and function , while STAT5B appears to play a distinct role in T cell development and function via DOCK8, SNX9, FOXP3 and IL2RA binding. Our results also suggest that one or more co-activators for STAT5A and/or STAT5B may play important roles in establishing different binding abilities and gene regulation behaviors. The new identification of these genes regulated by STAT5A and/or STAT5B has major implications for understanding the pathophysiology of cancer progression, neural disorders, and immune abnormalities.Signal transducer and activator of transcription (STAT) comprises a family of universal transcription factors that help cells sense and respond to environmental signals. STAT5 refers to two highly related proteins, STAT5A and STAT5B, with critical function: their complete deficiency is lethal in mice; in humans, STAT5B deficiency alone leads to endocrine and immunological problems, while STAT5A deficiency has not been reported. STAT5A and STAT5B show peptide sequence similarities greater than 90%, but subtle structural differences suggest possible non-redundant roles in gene regulation. However, these roles remain unclear in humans. We applied chromatin immunoprecipitation followed by DNA sequencing using human CD4 Signal transducer and activator of transcription (STAT) comprises a family of universal transcription factors, playing important roles in regulating gene expression in multiple cell types. STAT1 through 6 are essential for transduction of extracellular signals into the cells. STAT5, in particular, plays critical roles in the cellular response to various cytokines and hormones and therefore is crucial to regulation of immune and nervous system functions, as well as cell proliferation and growth, in both humans and rodents STAT5 encompasses two highly related proteins, STAT5A and STAT5B in humans (Stat5a and Stat5b in rodents). STAT5A and STAT5B show peptide sequence similarities of more than 90%, differing only by 6 amino acids in their DNA binding domains, 20 amino acids in their C-termini + T cell homeostasis −/−Stat5b mice). Moreover, deficiency in both Stat5a and Stat5b murine proteins is required to generate the growth defect observed in human −/−STAT5B patients IL4RA between STAT5A and STAT5B, with chromatin immunoprecipitation (ChIP) followed by sequencing (ChIP-seq), although no such difference was observed between Stat5a and Stat5b in mice −/−STAT5B do not completely recapitulate the immune data reported in −/−Stat5b mouse models, and suggest there are unique roles for STAT5A and STAT5B in human immune modulation. It is therefore crucial to analyze human samples to elucidate the redundant and non-redundant roles of STAT5A and STAT5B in human gene regulation Previous studies in mice have demonstrated both redundant and non-redundant roles for Stat5a and Stat5b in immune regulation and development. Both Stat5a and Stat5b were essential for normal lymphoid development, and function as critical signal mediators for CD8STAT5A expression. Additionally, we have reported that in humans, the anti-apoptotic factor BCL2L1 is specifically regulated by STAT5A, whereas FOXP3 and IL-2RA expression are specifically regulated by STAT5B We reported that STAT5B deficient patients show severe growth hormone-resistant growth failure despite the presence of normal growth hormone receptor + T cells, which are known to express STAT5 upon activation and can be easily expanded STAT5A or STAT5B KD human CD4+ T cells.To identify STAT5A and STAT5B target genes, we performed genome-wide ChIP-seq in human CD4+ T cells.To the best of our knowledge, this is the first report to show redundant and non-redundant roles of STAT5A and STAT5B in gene regulation in human CD4This study was approved by the Stanford Administrative Panel on Human Subjects in Medical Research Institutional Review Board.+ T cells from healthy adults were isolated using RosetteSep® Human CD4+ T Cell Enrichment Cocktail  according to the manufacturer's instructions. The purity of the sorted cells, as assessed by flow cytometry, was found to be greater than 92%.Whole blood samples from healthy adults were obtained from the Stanford Blood Center. CD42 atmosphere for 3 days. Recombinant human IL-2  was then added to cultures for either 30 min or 3 days.Cells were cultured in RPMI 1640 medium  supplemented with 10% fetal bovine serum and 100 µL/mL of antimicrobial agent  with PHA-P (5 µg/mL) in a 75 mL flask, and incubated at 37°C in a humidified 5% CO+ T cells were fixed on a glass slide with 100 µL cold Phosflow Fix Buffer 1 . The cells were permeabilized with 100 µL of Perm/Wash Buffer (Becton Dickinson), blocked with 10% normal donkey serum and 0.3 M glycine in phosphate buffer saline with 0.1% Tween 20, and incubated with anti-STAT5A Ab  and anti-STAT5B Ab  at a dilution of 1∶1,000. Alexa Fluor® 488 donkey anti-rabbit IgG (Invitrogen) or Alexa Fluor® 594 donkey anti-mouse IgG (Invitrogen) was used at a dilution of 1∶1,000 as secondary Ab for STAT5A or STAT5B respectively. The specificities of each Ab were shown in Fig. S1. Hoechst 33342, trihydrochloride, trihydrate (Invitrogen) was used at dilution of 1∶10,000 for the nuclear stain.CD4+ T cells were extracted using NE-PER Nuclear and Cytoplasmic Extraction Reagents . Lysates were run on a 10% polyacrylamide gel  for 30 min at 200 V, transferred to a nitrocellulose membrane (Bio-Rad) for 1 h at 100 V, blocked in 5% milk for 30 min. Proteins were detected by a 1∶1,000 dilution of anti-STAT5A Ab, anti-STAT5B Ab or anti-Phospho STAT5 Ab  administered for 18 hours, followed by incubation with anti-rabbit or mouse IgG, HRP-linked Ab  at 1∶5,000 for 30 min at room temperature. An enhanced chemiluminescent substrate for detection of HRP (Thermo Scientific) was used for visualization.Cytoplasmic or nuclear proteins from CD4+ T cells were also used for immunoprecipitation using anti-STAT5A Ab or anti-STAT5B Ab with the Protein A/G PLUS-Agarose  according to manufacturer's instructions.Nuclear proteins separated from CD4+ T cells (2.0×107 cells) stimulated by PHA-P for 3 days followed by incubation with rhIL-2 for either 30 min or 3 days were cross-linked with formaldehyde  for 10 min. The reaction was quenched with glycine and cell lysates were sonicated  and immunoprecipitated with anti-STAT5A Ab (12 µg), anti-STAT5B Ab (12 µg) or anti-IgG Ab  for 18 hours. After washing with tris-ethylene diamine tetra acetic acid including of 1% of sulfuric acid dodecyl buffer, to reverse the cross-link, the immunoprecipitated DNA and control DNA samples were prepared using previously published methods CD4http://genome.uscs.edu/) using the DNAnexus program (https://dnanexus.com/). Peak-calling was based on the QuEST algorithm. We set a strict threshold for the analysis to improve data quality. Peaks with more than a 3-fold increase in signal intensity versus input DNA and with more than 30-fold increase in read count in sample compared to a uniform distribution of the same sample reads across the genome were detected. Within these, peaks with a q-value<10−10 (i.e. the q-score in −log10(q-score) is greater than 10) were accepted as statistically significant.Short-read sequences were aligned to human genome sequences .Genes were identified as candidate genes for regulation by STAT5A and/or STAT5B if the gene existed within 10,000 bp from each detected binding site. Candidate genes were defined as “specifically detected genes” if they were detected by only anti-STAT5A Ab ChIP-seq (STAT5A ChIP-seq) or only anti-STAT5B Ab ChIP-seq (STAT5B ChIP-seq). Genes were defined as “dominantly detected genes” if there was more than a 2-fold difference in q-score between STAT5A ChIP-seq results and STAT5B ChIP-seq results. Genes were regarded as “equally detected genes” if q-scores differed by less than 2-fold between STAT5A ChIP-seq results and STAT5B ChIP-seq results, or were equivalent.5 CD4+ T cells transfected with STAT5A, STAT5B, or control siRNA via RNeasy kits  according to the manufacturer's protocol. KD cells were prepared using previously published methods STAT5A KD or STAT5B KD CD4+ T cells compared to the control CD4+ T cells.RNA was isolated from 2.0×10+ T cells at different time points after addition of rhIL-2. We found that nuclear translocation of both STAT5A and STAT5B was detectable as early as 30 min after addition of rhIL-2 , 273; genes detected only by STAT5A ChIP-seq (STAT5A-specific), 33; genes detected by STAT5A ChIP-seq, with a greater than 2-fold difference in q-score (STAT5A-dominant), 41; genes detected only by STAT5B ChIP-seq (STAT5B-specific), 187; genes detected by STAT5B ChIP-seq, with a greater than 2-fold difference in q-score (STAT5B-dominant), 146. The genes in each group are listed in To verify the binding sequences detected with ChIP-seq, we compared them to previously established consensus motif sequences for STAT5A and/or STAT5B. 30 of the 36 binding sequences detected by STAT5A and/or STAT5B ChIP-seq were in accordance with consensus motif sequences Fig. 2B.TTCCAGGAA” was detected equally by both STAT5A and STAT5B ChIP-seq in SGK1, as well as dominantly by STAT5A ChIP-seq in ST3GAL1 ; STAT5B siRNA-transfected human CD4+ T cells showed a 58% reduction in STAT5B gene expression as compared to control after 3 days (P<0.001) STAT5A and STAT5B (P = 0.20).To confirm whether the candidate genes were regulated by STAT5A and/or STAT5B, we measured expression levels of each gene  and ST3GAL1 ; GTF2H5 (decreased similarly in both KDs) and TNFSF10 ; BCL2L1 , FOXP3 and IL2RA ; CDKAL1 (no change in both KDs) and SAMD4A ; DNM2 (no change in both KDs) and NDRG1 ; MBP and LNPEP (no change in both KDs) and SSH2 ; and DNAJC6  and DOCK8 .As was seen with ChIP-seq, genes sharing binding sequences did not always display identical patterns of expression in Known roles of the genes are summarized in + T cells yielded a number of candidate genes, some of which appeared to be regulated by both STAT5A and STAT5B, while others were associated with regulation by either STAT5A or STAT5B. ChIP-seq results were further tested for validity by QT-PCR in STAT5A or STAT5B KD CD4+ T cells, which identified several genes whose expression was regulated by STAT5A and/or STAT5B. Both STAT5A and STAT5B regulated genes implicated in cell proliferation and apoptosis, gene transcription and DNA repair, and the active cellular uptake of carnitine. STAT5A was associated with non-redundant  roles in regulating genes relevant to neurite extension, synaptic vesicle recycling, anti-apoptosis, hypoxic stress, and androgen response; STAT5B was linked to non-redundant  roles in regulating genes important to IgE production, peripheral CD8+ T cell survival and function, Treg development and function, and T cell activation and proliferation. Overall, STAT5B was therefore highly associated with regulation of genes associated with T cell-specific functions, while STAT5A appeared to interact with genes known to play major roles in neural development and function.This study is the first to demonstrate, to our knowledge, redundant and non-redundant roles for STAT5A and STAT5B in human gene regulation. Genome-wide identification of STAT5A and STAT5B target genes in human CD4MAP3K5 , SNX9 , and UGCG .It has been suggested that structural differences between STAT5A and STAT5B DNA-binding domains, C-termini (which contain transactivation domains), and N-termini (which mediate oligomerization) may result in non-redundant roles in each gene's regulation. Our results supported this hypothesis, showing different binding behaviors between STAT5A and STAT5B to genes with differing binding sites: for example, SGK1 by both STAT5A and STAT5B ChIP-seq, but was dominantly detected by STAT5A ChIP-seq in ST3GAL1. After eliminating experiment-specific limitations such as differential affinity between anti-STAT5A and anti-STAT5B Abs, this discrepancy suggests that co-activators may also be important in establishing differential binding and transcriptional activities between STAT5A and STAT5B. This is supported by previous findings that Crkl, a STAT5B- and Stat5b-binding co-activator, significantly increases STAT5B and Stat5b binding to their target genes in humans and mice, respectively At the same time, we found that binding site sequences alone could not predict STAT5A versus STAT5B binding behavior, observing several gene-specific, but not sequence-specific, differences in STAT5A and STAT5B binding. For example, the same binding site sequence was detected within the gene Alternatively, the following mechanisms have been proposed for STAT protein regulation of target genes: (1) binding to DNA binding sites to directly drive transcription; (2) forming transcriptional complexes with non-STAT transcription factors to trigger transcription through a STAT; (3) interaction with non-STAT DNA binding sites; and (4) cooperation between STATs and non-STAT transcription factors to activate transcription via binding to clustered independent DNA binding sites It is also possible that the identified binding sites are not linked to the assigned candidate genes but are regulating further distant genes, as a binding site for STAT5 was found over 10,000 bp away from the target gene CDKAL1, DNM2, DUSP5, MBP, ARL4C, CBS, PPP2R2B, LNPEP, PTGER1 and DIDO1) showed no change in expression levels in QT-PCR performed in STAT5A or STAT5B KD CD4+ T cells when compared to control CD4+ T cells.These mechanisms may help explain the discrepancies seen between our ChIP-seq and QT-PCR results, in which several genes associated with STAT5A and/or STAT5B by ChIP-seq . These genes may provide explanations for the phenotype of STAT5B-deficient patients  encodes subunits of WASp (Wiskott-Aldrich syndrome protein)/SNX9/p85/CD28, a complex involved in signal transduction of CD28-mediated T cell co-stimulation SNX9 suppression may be a potential cause of impaired function and decreased number of T cells in STAT5B-deficient humans and mice.We found some genes to be specifically associated with regulation by STAT5B, and not STAT5A , and genes implicated in T cell development and function (STAT5B).The elucidation of the roles of STAT5A and STAT5B furthers our understanding of potential mechanisms in cancer pathophysiology, neural disorders, and abnormal T cell immune function providing potential new targets to study in these diseases.Figure S1Validation of anti-STAT5A Ab and anti-STAT5B Ab by Western blot.(TIF)Click here for additional data file.Table S1The list of candidate genes detected by STAT5A and/or STAT5B, equally, dominantly and specifically.(DOCX)Click here for additional data file.