Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria.Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion , numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation) and, conversely, orthologous regulators to control distinct pathways .To explore conservation and variations in the Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S. oneidensis MR-1. Analysis of correlations in gene expression patterns helps to interpret the reconstructed regulatory network. The inferred regulatory interactions will provide an additional regulatory constrains for an integrated model of metabolism and regulation in S. oneidensis MR-1.We tentatively defined the first reference collection of ~100 transcriptional regulons in 16  Genes and operons directly co-regulated by the same TF or by an RNA structure are considered to belong to a regulon. All regulons taken together form the transcriptional regulatory network (TRN) of the cell. TFs form more than 50 different protein families and constitute around 5-10% of all genes in an average bacterial genome, and their respective regulons cover a substantial fraction of bacterial TRNs . Al. AlShewaproaches ,20-22, opiration -29. In aneidensis.Shewanella genus . Among the remaining 14 families of TFs, mostly represented by single members in the genomes , the Fur, ArgR, BirA, LexA, MetJ, NrdR, RpiR, and TrpR families are universally conserved in the Shewanella  TFs from E. coli have orthologs in at least one Shewanella genome  that were possibly acquired together with the target metabolic pathway genes via lateral gene transfer events [Shewanella spp. belong to two protein families, LysR and AraC , that were likely expanded via gene duplication in course of ecological adaptation of individual species.The 3,228 predicted TFs in 16 r events . Near 1/Shewanella spp., we used the integrative comparative genomics approach that combines identification of TFs and candidate TFBSs with cross-genomic comparison of regulons and with the genomic and functional context analysis of candidate target genes. We analyzed 16 Shewanella genomes and inferred regulons for 82 orthologous groups of TFs that split into two groups: 41 regulators with experimentally characterized orthologs in S. oneidensis or other γ-proteobacteria  search for orthologous TFs, (ii) collecting known target genes and TFBSs in a model genome, (iii) identifying orthologous target genes in the analyzed genomes and extracting their upstream regions, iv) application of a pattern recognition program, then constructing positional weight matrices (PWMs) and comparison of the newly identified TFBS motifs with the previously known sites/motifs in a model genome, v) search for additional sites in the analyzed genomes and consistency check or cross-species comparison of the predicted regulons . For reShewanella genomes, we performed regulon reconstruction for 41 TFs that are orthologous to previously characterized regulators (Table E. coli (35 TFs) and/or Pseudomonas spp. (10 TFs), or had been previously studied in S. oneidensis (5 TFs)  and Pseudomonas spp. (for GlmR and HutC) (data not shown). Two novel TFBS motifs (for AgaR and GcvA) in the Shewanella spp. are completely different from the respective motifs in E. coli. Five other TFBS motifs  in the Shewanella spp. are moderately different (3-4 mismatches in the conserved positions) from the known motifs of orthologous TFs previously described in E. coli and/or Pseudomonas spp. The remaining 31 Shewanella TFs appear to have binding motifs that are well conserved or only slightly different (1-2 mismatches in the conserved positions) from the motifs of their previously characterized orthologs.For the E. coli 3 TFs and/ E. coli  TFs and/E. coli 3 TFs and/Shewanella genomes, and a collection of their upstream regions was used as an input for the motif-recognition program SignalX to predict a common DNA motif allowing a limited number of sequences to be ignored. At the next step, the Shewanella genomes were scanned with the constructed DNA motif to reveal the distribution of similar sites that were further verified by the consistency check procedure  genes that constitute functional metabolic pathways (subsystems); and ii) genes derived from conserved gene neighborhoods that include a putative TF gene. To analyze metabolic subsystems and conserved chromosomal gene clusters projected across bacterial genomes we used the SEED database . Each triewed in ). FinallShewanella spp. including: i) 18 regulons for metabolic subsystems; and ii) 23 regulons for conserved chromosomal gene clusters (Table http://theseed.uchicago.edu) and tentatively named using an abbreviation of the target metabolic pathway/genes. Hereinafter we mark the new names by asterisks.As a result, we inferred 41 novel regulons in Shewanella spp. it is predicted to be controlled by a GntR-family TF PrpR*. The proline utilization is controlled by the Lrp-family activator PutR in the Vibrio spp. [Pseudomonas spp. [Shewanella spp.. The homogentisate pathway of the tyrosine degradation is regulated by the IclR-type repressor HmgR in the Pseudomonas spp. [Shewanella spp.. Similar non-orthologous replacements of regulators have been detected for ten different carbohydrate catabolic pathways [Shewanella spp. [Shewanella instead of PurR regulon previously characterized in other γ-proteobacteria including E. coli[Shewanella spp.. Two novel regulators PflR* and XltR* were predicted to control metabolic pathways of pyruvate to formate fermentation and xylitol catabolism, whose regulation have not yet been previously described in any bacteria.Most of the novel metabolic TFs represent non-orthologous replacement of previously known TFs that control similar metabolic pathways in other lineages. For example, the propionate catabolism in the Enterobacteria is activated by the Fis-family regulator PrpR, whereas in the rio spp. , the Aranas spp. , and thenas spp. , which ipathways  and the lla spp. . A novelg E. coli and missazr-SO3586 operon, which encodes azoreductase and lactoylglutathione lyase that are likely involved in the superoxide stress protection. Novel regulator CalR* controls expression of the coniferyl aldehyde dehydrogenase calB that play a role in phytochemical aromatic compound utilization. Other inferred TF regulons appear to contain various hypothetical metabolite efflux transporters or flavocytochromes potentially involved in detoxification and undescribed respiratory processes, respectively.Functional annotations of novel TF regulons that were deduced from the analysis of conserved gene clusters are largely hypothetical and incomplete. Most of them are local regulators controlling one or two target operons  and biochemical conversion (nagK-nagBII-nagA), the reconstructed NagR regulon contains auxiliary components that are likely involved in chemotaxis and hydrolysis of chitin and/or chitooligosaccharides (mcpNag-hex and cbp). Experimental validation of the reconstructed NagR regulon in S. oneidensis MR-1 was performed by both in vitro and in vivo approaches. The nagR gene was cloned and overexpressed in E. coli, and the recombinant protein was purified by Ni2+-chelating chromatography. We used electrophoretic mobility shift assay to test specific DNA-binding of the purified NagR protein to its predicted operator sites in upstream regions of the nagP (SO3503), nagK (SO3507), mcpNag (SO3510), ompNag (SO3514) and cbp (SO1072) genes in S. oneidensis MR-1. The maximal shift of the nagK DNA fragment observed at 100 nM NagR was completely suppressed by the addition of 20 mM of N-acetylglucosamine, which was thus proven as a negative effector  “regulon expansion” in the Shewanella compared to other lineages that can be ranged from additions of several regulon members to larger-scale shifts in the regulated metabolic pathways ; (ii) “fuzzy regulons” when a regulon possess a conserved core and variable periphery within the Shewanella group ; (iii) “regulon loss or acquisition” when entire regulon  is present only in some of the Shewanella species . Of couE.coli and compared the gene contents of the regulons reconstructed in the Shewanella and with orthologous regulons in E.coli captured in the RegulonDB database , including Dnr, GlmR, HexR, HutC, and PsrA  have orthologous known targets in E. coli or Pseudomonas, 223 operons (~50%) lack orthologous operons, whereas the remaining 107 operons (~20%) have orthologous operons that are not under control of orthologous TFs in these species. Examples of impressive variations in the content of orthologous TF regulons in the Shewanella and E. coli are discussed below.To estimate the relative conservation of the predicted regulatory interactions in other lineages, we searched for orthologs of the putative regulon members in Shewanella spp. and the Enterobacteria. In E. coli, two global regulators, FruR (fructose repressor/activator) and Crp (cAMP-responsive activator), control the central carbohydrate metabolism, whereas HexR (phospho-keto-deoxy-gluconate-responsive repressor) and PdhR (pyruvate repressor) are local regulators of glucose-6P dehydrogenase and pyruvate dehydrogenase, respectively. By contrast, the Shewanella spp. are predicted to use the HexR and PdhR regulators for the global control of the central carbohydrate metabolism and fermentation , and lack global co-regulation by Crp. Thus, the Shewanella spp. seem to lack many “feed-forward loops” that are characteristic for the regulation of sugar catabolism pathways in E.coli  [The comparison of the inferred regulons revealed striking differences in the strategies for regulation of the central carbohydrate and amino acid metabolism between the lineages comprising the ion Fig. . The Fru by Crp) , thus maE. coli, the tyrosine- and phenylalanine-responsive regulator TyrR represses most aromatic amino acid biosynthetic enzymes and transporters encoded by multiple aro and tyr genes scattered on the chromosome, and activates the tyrosine transporter encoded by the mtr gene. In the Shewanella spp., we identified TyrR as a master regulator of the degradation pathways for various amino acids, including phenylalanine (phhAB operon), tyrosine (fahA-maiA operon), branched chain amino acids , proline (putA gene), and oligopeptides (various peptidase genes), as well as some other pathways such as the glyoxylate shunt (aceBA operon), and the chorismate biosynthesis (aroA gene). These findings are in accordance with the previously established role of PhhR, a TyrR ortholog in Pseudomonas spp., as an activator for phenylalanine and tyrosine degradation genes [Shewanella app. and many other γ-proteobacteria is controlled by PsrA, whereas in the Enterobacteria the analogous pathway is regulated by FadR [Shewanella spp. also have a significantly reduced in size FadR regulon, which retains only two operons shared with the orthologous regulon of E. coli, fadIJ and fadL[fabA, which has conserved regulation in both E. coli and the Shewanella spp., whereas the remaining target genes were identified as a lineage-specific regulon extension.Significant shifts in the regulon content were also identified for the TyrR, FadR, and FabR regulons Fig. . In E. con genes . The fat by FadR . The She and fadL. FinallyShewanella regulons contains at least 30 regulons (for 24 TFs and 6 regulatory RNAs) that have at least one operon under simultaneous control of at least two regulators . The glycine utilization operon gcvTHP was found to be controlled by the glycine-responsive regulator GcvA, the central carbohydrate regulator HexR, and the novel purine biosynthesis regulator PUR*. In the predicted regulons, 14 operons are under overlapping control of three regulons, whereas ~70 operons are co-regulated by two regulons. At least four regulatory cascades between various TFs were identified in the Shewanella spp.: LiuR for tyrR, NarP for crp, Crp for hmgR, and MetJ for metR, and only the latter cascade is conserved in E. coli.The collection of the inferred Shewanella spp., the predicted LiuR regulon was found to regulate Ile/Leu/Val operons  and was expanded by additional members involved in the biosynthesis of glutamate (gltBD) and threonine (thrABC), and the glyoxylate shunt (aceBA). Six out of nine LiuR-controlled operons are also regulated by the tyrosine/phenylalanine-responsive transcription factor TyrR [E. coli can act both as activator and repressor on its target genes, the mode of TyrR action on Shewanella targets is to be determined experimentally. Preliminary comparative analysis of relative positions of the TyrR- and LiuR-binding sites in Shewanella genomes  suggests that TyrR probably acts as an activator for the ldh, liu, ivd, and bkd operons (data not shown). This supposition suggests that integrative effect of the LiuR and TyrR mediated control can be activation of their target genes in the simultaneous presence of Ile/Leu/Val and Tyr/Phe. Indeed, the expression data confirm strong up-regulation of the Ile/Leu/Val utilization and glyoxylate shunt genes in the presence of casein-derived mixture of amino acids , and 258 RNA regulatory motifs from 14 families. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in the central metabolism, production of energy and biomass, metal ion homeostasis and stress responses. Although some diversity of the predicted regulons was observed within the Shewanella genus, the most significant diversification and adaptive evolution of TRNs were revealed by comparison with the established TRN in E. coli and related Enterobacteria. These differences are mostly attributed to: i) lineage specific regulon expansion and contraction for orthologous TFs that use conserved TFBS consensus motifs, and ii) involvement of non-orthologous TFs to control physiologically equivalent metabolic pathways in the two lineages of γ-proteobacteria.By applying the comparative genomics approach, we tentatively defined the first reference collection of transcriptional regulons in 16 S. oneidensis MR-1 are supported by available microarray expression data for the fur, crp, and etrA (fnr) knockout strains [S. oneidensis MR-1 is currently underway. Previously we have characterized in vitro the novel NAD metabolism regulon NrtR [in vivo and in vitro validation of N-acetylglucosamine utilization regulon NagR. Combined in vivo and in vitro experimental validation of the global carbohydrate metabolism regulon HexR and the assessment of its physiological role in Shewanella will be published elsewhere.The reconstructed regulons in  strains ,26,28,29 strains . Prelimilon NrtR  and in tShewanella regulons is the first taxonomy-wide collection of regulons obtained by this approach. It can be assessed in the RegPrecise database [S. oneidensis MR-1 [Shewanella spp. [This work demonstrates the power of the comparative genomics approach in application to the reconstruction of transcriptional regulons in poorly studied groups of related bacteria. The reference set of the database . We antisis MR-1 , allowinlla spp. Shewanella spp. genomes were downloaded from the Genbank [locus_tag gene identifiers are used throughout. Orthologous proteins in 16 Shewanella genomes were defined in the previous work by the best bidirectional hits criterion [Shewanella were named by either a common name of characterized protein, a novel name for proteins functionally annotated in this study, or by a locus_tag from S. oneidensis genome for uncharacterized proteins. Orthologs between proteins from different taxonomic groups (e.g. Shewanella and other γ-proteobacteria) were defined as bidirectional best hits with 30% of identity threshold using the Smith-Waterman algorithm implemented in the GenomeExplorer program [http://theseed.uchicago.edu/FIG/index.cgi, which combines protein similarity search, positional gene clustering, and phylogenetic profiling of genes [The  Genbank  Fig. 1)Shewanell Genbank . The locriterion . Ortholo program . In dubi program . Functioof genes . In addiof genes , and PFAof genes  databasede novo identification of a candidate regulatory motif in the training set of potential upstream regions of genes (intergenic regions up to 350 bp) we used a simple iterative procedure DNA motif detection procedure implemented in the program SignalX [http://regpredict.lbl.gov[http://regprecise.lbl.gov[For  SignalX . Weak pa SignalX . These p SignalX . Each ge SignalX  and gene SignalX . Analysit.lbl.gov. The dete.lbl.gov. For idee.lbl.gov. Calculae.lbl.gov.nagR (SO3516) gene cloned at a pET-derived vector containing the T7 promoter and His6 tag [The His6 tag  was kindProtein purification. Recombinant proteins of nagR (SO3516) from S. oneidensis MR-1 was overexpressed as N-terminal fusion with a His6 tag in E. coli strain BL21/DE3. Cells were grown on LB media to OD600 = 0.8 at 37°C, induced by 0.2mM IPTG, and harvested after 12 h shaking at 20°C. Protein purification was performed using rapid Ni-NTA agarose minicolumn protocol as described [escribed . BrieflyqPCR. In-frame deletion mutagenesis of or nagR (SO3516) was performed using previously published method [S. oneidensis MR-1 and ∆nagR cells grown in minimal medium supplied with lactate and collected at O.D.(600) of 0.52 using the RNA purification kit from Promega . Reverse transcription of total RNA was performed with random primers using iScript cDNA synthesis kit from BIO-RAD , following kits instructions. qPCR was performed using SYBR GreenER qPCR SupeMix Universal kit from Invitrogene . Transcript levels of the nagP (SO3503), nagK (SO3507), mcpNag (SO3510), ompNag (SO3514), cbp (SO1072), SO0854, and zwf  genes were measured and the results were normalized to the expression level of 16S mRNA. Fold change was calculated by the 2-∆CT method [nagR mutant and wild-type MR-1 strains.d method . GenomicT method  as a ratDNA-binding assay. Interaction of purified recombinant protein NagR (SO3516) from S. oneidensis MR-1 with their cognate DNA motifs was assessed by EMSA technique using the following dsDNA segments obtained by PCR amplification or by custom synthesis of both complementary oligonucleotides , annealing and purification. One of the primers was 5′-biotinylated (IDT). By using S. oneidensis MR-1 DNA as the template, we amplified DNA fragments from the following upstream gene regions: SO1072 (89 bp), SO3507 (69 bp), SO3510 (64 bp), SO3514 (69 bp), SO3503 (62 bp), SO0854 (67 bp). For EMSA, the biotin-labeled DNA (0.1 or 1 nM) was incubated with the increasing amount of purified NagR (0-100 nM) in a total volume of 20 μl. The binding buffer contains Tris-HCl 20mM, KCl 150mM, MgCl2 5mM, DTT 1mM, EDTA 1mM, 0.05% NP-40, 2.5% glycerol. The poly(dI-dC) (Sigma) was added as nonspecific competitor DNA at ~104-fold molar excess over labeled target DNA to reduce nonspecific binding. After 25 min incubation at room temperature, the reaction mixtures were separated by electrophoresis on a 5% native polyacrylamide gel in 0.5 × Tris-borate-EDTA for 90 min at 90V, at 4°C. The gel was transferred by electrophoresis  onto a nylon membrane  and fixed by UV cross-linking. Biotin-labeled DNA was detected with the LightShift Chemiluminescent EMSA kit , as recommended by the manufacturer. The effect of N-acetyl-glucosamine on NagR-DNA binding was tested by addition of 20 mM of N-acetylglucosamine to the incubation mixture.TF: transcription factor; TFBS: transcription factor-binding site; TRN: transcriptional regulatory network.Shewanella. XL carried out validation experiments for NagR regulon. JKF, ID and APA contributed to the development of the manuscript and design of the study. All authors read and approved the final manuscript.DARo, ALO and MSG conceived and supervised the research, and wrote the manuscript. DARo, IAR, DARa, AEK, EAP, AVG, ONL, GYK performed comparative genomic analysis to infer novel transcription factor regulons. MDK identified riboswitches. PSN developed RegPredict tool and RegPrecise database, performed propagation of regulons. DARo and PSN performed correlation analysis using expression data. EDS and RO performed computational similarity searches and gene annotation in the SEED database. MFR produced manually annotated table of orthologs and provided targeted gene knockout strains in The authors declare that they have no competing interests.Shewanella spp.Rodionov_AF1.xls - Repertoire of DNA-binding transcription factors identified in 16 genomes of Click here for fileShewanella spp.Rodionov_AF2.xls - Transcription factors universally conserved in Click here for fileE. coli and 16 Shewanella strains.Rodionov_AF3.xls - Functional classifiaction of 73 transcription factors conserved between Click here for fileShewanella genomes.Rodionov_AF4.xls - Functional content and conservation of reconstructed TF and RNA regulons in Click here for fileShewanella spp. to the previously characterized orthologous regulators in model species.Rodionov_AF5.pdf - Comparison of predicted TFBS motifs in Click here for fileS. oneidensis MR-1. (A) Electrophoretic mobility shift assays to assess NagR protein binding to their predicted DNA operators; (B) Differential regulation of NagR controlled genes determined by quantitative qPCR.Rodionov_AF6.pdf - Experimental validation of NagR regulon in Click here for file


GCN5-deficient NSC exhibit a 25% reduction in brain mass with a microcephaly phenotype similar to that observed in nestin-cre driven knockouts of c- or N-myc. In addition, the loss of GCN5 inhibits precursor cell proliferation and reduces their populations in vivo, as does loss of N-myc. Gene expression analysis indicates that about one-sixth of genes whose expression is affected by loss of GCN5 are also affected in the same manner by loss of N-myc. These findings strongly support the notion that GCN5 protein is a key N-Myc transcriptional cofactor in NSC, but are also consistent with recruitment of GCN5 by other transcription factors and the use by N-Myc of other histone acetyltransferases. Putative N-Myc/GCN5 coregulated transcriptional pathways include cell metabolism, cell cycle, chromatin, and neuron projection morphogenesis genes. GCN5 is also required for maintenance of histone acetylation both at its putative specific target genes and at Myc targets. Thus, we have defined an important role for GCN5 in NSC and provided evidence that GCN5 is an important Myc transcriptional cofactor in vivo.Here we examine the functions of the Myc cofactor and histone acetyltransferase, GCN5/KAT2A, in neural stem and precursor cells (NSC) using a conditional knockout approach driven by nestin-cre. Mice with  GCN5/KAT2A was one of the first histone acetyltransferase (HAT) linked to transcriptional activation GCN5 null embryos exhibit elevated apoptosis in the mesodermal lineage GCN5 allele were created as well as mice with a knock-in of a catalytically dead GCN5 (GCN5HAT/HAT), which exhibit a neuronal phenotype with no apoptosis GCN5 is also essential for normal embryonic stem cell pluripotency GCN5 has been shown to accelerate cerebellar degeneration in a mouse model of neurodegeneration GCN5 is one of the most thoroughly studied HATs as part of the SAGA complex in yeast GCN5 for Myc function in vivo, such as during normal brain development, remains unknown as all studies of GCN5 function as a Myc cofactor have been conducted in immortalized cell lines. While GCN5 substrates can include K9, K14 and K18 of histone H3 as well as all 4 amino-terminal K residues of histone H4 GCN5 as a Myc target gene in vivo remains unexamined in any developmental system.GCN5 also has a more global role in histone acetylation of unknown function myc family of proto-oncogenes encodes chromatin regulatory proteins belonging to the basic-helix-loop-helix zipper (bHLHZ) class of transcription factors  and the subventricular zone (SVZ). VZ and SVZ precursor cells can be identified based on expression of the intermediate filaments nestin and vimentin GCN5 gene or GCN5 protein are necessary for Myc’s chromatin, transcriptional, and biological functions in vivo during development is unknown. More specifically, the potential roles of Myc cofactors such as GCN5 in N-Myc function in NSC and brain growth remain important open questions. To address these issues, here we have used targeted gene disruption to knockout GCN5 specifically in NSC both in vivo and in vitro. We found that GCN5 is required in NSC for normal brain growth, producing a microcephaly phenotype similar to the nestin-cre driven myc KOs. In addition, loss of GCN5 decreases stem cell proliferation in the cerebral cortex. KO of GCN5 in NSC produces gene expression changes that strongly overlap with those observed in N-myc KO NSC. Together these findings provide the first evidence that GCN5 and a Myc protein family member regulate significantly overlapping transcriptional programs in vivo.The extent to which the GCN5 KO specifically in NSC we took advantage of the well-characterized nestin-cre transgene, which we have previously used to make c- and N-myc NSC-specific KO mice GCN5 floxed alleles in which the vast majority of the GCN5 coding region is flanked by LoxP sites GCN5 flox/flox mice with GCN5 flox/flox nestin-cre+ mice  evidence of lethality, as KO (nestin-cre+) mice were 15% underrepresented  brains  brains . The siz) brains . At E17.controls . ImportaGCN5 NSC KO mice indicated that the loss of GCN5 in NSC was inhibiting normal brain growth. Two potential causes of impaired brain growth are reduced cell cycling and increased apoptosis. To examine these possibilities we stained sections from the developing GCN5 NSC KO mice with an antibody against phosphorylated vimentin (4A4), a marker for dividing precursor cells (GCN5 KO ventricular zone (VZ) exhibited a marked decrease in the number of 4A4+ dividing precursor cells in all the developmental stages examined. We quantified the number of VZ 4A4+ cells present in a radial unit of dorsal cortex 100 µm wide spanning from the surface of the lateral ventricle to the pial surface. The number of 4A4+ cells in the null E12.5 VZ decreased by more than half ; the number of 4A4+ cells in the E14.5 VZ decreased almost by half, ; and the number of 4A4+ cells in the E17.5 VZ decreased by one third  compared to the VZ. The number of 4A4+ mitotic cells in the SVZ was quantified in a radial unit of dorsal cortex 100 µm wide spanning from the surface of the lateral ventricle to the pial surface. We found that the number of SVZ mitoses in the E17.5 cortex was decreased three-fold in the GCN5 KO , consistent with previous GCN5 loss of function studies Effects of the E17. 5 GCN5 KOs, we established E12.5 neurosphere cultures from control (flox/flox) and GCN5 KO (flox/flox nestin-cre) embryos. We isolated RNA from these neural stem and precursor cells and measured GCN5, Cre, and PPIA (loading control) levels by RT-PCR. As expected, we found that the GCN5 KO neurospheres exhibited undetectable GCN5 levels and N-myc null (N-myc floxed nestin-cre+) neurospheres did not express detectable N-myc  were also downregulated in the N-myc KO neurospheres  overlap between upregulated genes in the GCN5 and N-myc KO neurospheres, with a p value <10−32 , we analyzed the ontology of genes there were either down- or upregulated in N-myc and GCN5 KO neurospheres , SULF1, DBX2, GRIFIN, ITIH3, MYOM3, and EMP1. CLDN11 encodes OSP, Oligodendrocyte Specific Protein, which is specifically expressed in oligodendrocytes. The pronounced decrease in CLDN11/OSP expression in both KOs suggests that N-Myc and GCN5 are required for maintaining normal oligodendrocyte differentiation-associated gene expression. Many known Myc target genes were downregulated in the GCN5 KO, including for example EMP1/TMPGCN5 or N-myc included transcription factors such as MITF, which is important in retinal development and melanoma EPHA5, ephrin A5, was also strongly upregulated in both KOs and is important in axon guidance Prom1 (CD133), was separately downregulated in both the N-myc and GCN5 KOs. We chose six genes that exhibited downregulation in both KOs to analyze by RT-PCR, finding that the trends indicated in the array data were mostly confirmed, although changes in LCN2 in the GCN5 KO were at most mild  analysis  was the “Condensed Chromatin” class. This might suggest that N-myc indirect regulation of chromatin through transcriptional control of effectors in NSC is at least partially GCN5-independent. N-myc alone also was positively linked to expression of genes involved in tight junctions, mitochondrial function, and fatty acid metabolism. GCN5 was uniquely linked to positive expression of cytochrome P450, Cadherin, and DNA repair genes.Both N-unctions –2. InterGCN5 KO NSC had greatly decreased levels of both histone acetylation and RNA Pol II (GCN5 and ccd2 (cyclin D2) gene promoters. We found the same pattern using a second independent set of control and GCN5 KO NSC  and histone H4 as both AcK9 and AcH4 (pan-acetylated histone H4 antibody) were reduced at the Lcn2 gene promoter in GCN5 KOs  at any of the N-Myc or GCN5 target genes. However, focusing on a key GCN5 target for acetylation , we conducted ChIP assays for AcK9 as well as RNA Pol II at N-Myc target genes. We found that A Pol II  at the G5 KO NSC , findingGCN5 KOs . When westone H4 . These fmyc KO phenotype using the same nestin-cre. Parallel expression microarray studies indicated that approximately 1/6 of genes regulated by GCN5 are also regulated by N-Myc. The simplest model to explain this overlap is that N-Myc is recruiting GCN5 to these genes to control their expression, a model that fits for genes that are downregulated by KO of N-myc or GCN5. However, interestingly there is also strong overlap between genes upregulated in both N-myc and GCN5 KO NSC, representing putative targets of N-Myc and GCN5 repression. The striking overlap in genes activated by either N-Myc or GCN5 loss-of-function  suggests, not surprisingly, that at least some indirect effects on gene expression occur since histone acetylation driven by GCN5 is proposed to strictly activate genes. One possible model for derepression of genes in both knockouts is that N-Myc and GCN5 work together to activate expression of genes encoding repressor proteins that in turn suppress expression of genes in specific functional pathways.This study is the first to measure in parallel the potential cooperative functions of a transcription factor (N-Myc in this case) and its recruited HAT cofactor (GCN5) using mouse genetics to perform parallel target gene disruption. We found that the HAT GCN5 is required in NSC for brain growth and produces a phenotype with both some similarities and differences from the N-myc and GCN5 KOs exhibited a downregulated Wnt cluster, the genes within the clusters were entirely different as no specific Wnt pathway genes were downregulated in both KOs. This suggests that N-Myc and GCN5 have distinct effects on Wnt signaling. In addition, the Ras clusters downregulated in each KO only partially overlapped, indicating that GCN5 and N-Myc regulate both distinct and shared aspects of NSC signaling, likely explaining the differential effects of their loss on cell fate. Together these findings suggest that N-Myc and GCN5 may in part influence development via complex regulation of signaling pathways.Of downregulated genes, N-Myc and GCN5 null NSC shared many ontological clusters including, most prominently, those related to signaling including Ras and Wnt. If N-Myc and GCN5 function together, in part, to coordinately regulate Ras and Wnt signaling, then the disruptions in these pathways in the KOs may, at least in part, explain the changes in cell fate observed in both KOs. Importantly, while both N-myc and GCN5 are also required for maintained expression of peptidase inhibitors. These data strongly suggest N-myc and GCN5 have a role in delimiting peptidase activity in NSC.The strong downregulation of ubiquitin pathway genes (e.g. FBXO32 and FBXO36) in both KOs supports the model in which N-Myc and GCN5 together are positive regulators of the activity of the proteome, and suggests in their absence there are not only transcriptional changes but also widespread alterations in protein levels due to enhanced stability. These posttranscriptional events are also likely mechanisms underlying the brain growth and fate phenotypes observed in the KOs. N-Myc and GCN5 also both strongly inhibit phosphatase and peptidase gene expression (the expression of genes in these classes are upregulated in both KOs), while interestingly N-myc KO mice coupled with the absence of any disproportionate cerebellar phenotype in the nestin-cre driven GCN5 KO mice, indicates that N-Myc protein likely functions to direct cerebellar development independently of GCN5 through another HAT. Thus, an intriguing question is whether GCN5 has any role in the genesis of the cerebellar tumor medulloblastoma that is frequently associated with Myc family gene amplification. Future studies knocking out additional Myc cofactors such as TIP60, p300, and JARID factors in NSC employing the same nestin-cre are likely to shed further light on Myc function in brain development and brain tumors. In addition KOs of other transcription factors that recruit GCN5 using nestin-cre may provide mechanistic insight into not only their function, but also that of GCN5.While the overlap between genes transcriptionally regulated by GCN5 and N-Myc was highly significant statistically, the presence of a majority of gene expression changes that were not shared also suggests a significant divergence of many functions. More specifically, it argues that N-Myc is frequently recruiting other HATs, such as TIP60 and p300, to regulate NSC functions, and that GCN5 is often recruited by transcription factors besides N-Myc. The dramatic cerebellar phenotype in the nestin-cre driven N-myc KO in vivo and in NSC is reflected in the changes in the gene expression array studies, including upregulation of neural differentiation genes by ontological analysis and downregulation of a cluster of differentiation inhibitory genes. GCN5 KO also results in upregulation of some neural differentiation genes, including some that are shared with N-Myc such as BCL11A, BCL11B, ANK3, and SLITRK1. Interestingly, GCN5 KO leads to downregulation of neuronal differentiation genes, the gene expression changes that are perhaps dominant in the GCN5 KO given the impaired neuronal differentiation observed in vivo in the GCN5 KO.The enhanced neuronal differentiation phenotype in the N-Our studies of the parallel loss-of-function between N-Myc and GCN5, supporting the model of their cooperative function to regulate gene transcription and NSC biology, suggest that other parallel transcription factor/histone modifying enzyme cofactor knockout studies incorporating both phenotypic analysis and gene expression analysis may prove fruitful. The findings that N-Myc and GCN5 appear to work together to maintain a progenitor-like state of inhibited differentiation, high cellular metabolism, and rapid proliferation also support a model in which GCN5 may mediate aspects of nervous system tumor formation by excess N-Myc when such a progenitor-like state appears to be “locked in.” Further studies will provide additional insight into the functions of N-Myc and GCN5 in NSC and tumorigenesis.All E12.5 embryos were immersion-fixed overnight in fresh 4% paraformaldehyde (PFA) in Phosphate Buffered Saline (0.1 M PBS), paraffin embedded and 12 µm sagittal slices were cut in a microtome. Pregnant females were anesthetized with 150 mg/kg ketamine and 16 mg/kg xylazine, then E14.5 and E17.5 embryos were removed individually and perfused with PFA. Brains were removed and fixed overnight in fresh PFA, then cryopreserved with 30% sucrose and 12 µm coronal slices were cut in a cryostat. For immunostaining, E12.5 embryo sections were deparaffinized then rehydrated and 30 mM sodium citrate treated for 10 min at 95°C, while E14.5 and E17.5 frozen brain sections were post fixed at −20°C acetone for 10 min followed by three 5-minute PBS washes. All sections were blocked in 10% normal goat serum in PBS for 1 hr at room temperature then incubated in primary antibody overnight at 4°C. Antibodies used include anti-phosphorylated vimentin , Goat anti-Olig2 , Mouse anti-NeuN , Rabbit anti-S100 . The next day sections were washed in PBS three times for 10 min each, and then incubated in secondary antibody for 2 hrs at room temperature. Donkey anti-Rabbit, donkey anti-Goat, and donkey anti-Mouse were used . Sections were washed again in PBS three times for 10 minutes each, and then mounted in Vectashield mounting medium with DAPI . TUNEL staining was performed using the DeadEnd Fluorometric TUNEL System Kit  as directed.For quantitation of proliferation by 4A4 staining we used n = 3 for each group and the standard deviations are from different embryos. Changes in the sizes of brains were quantified by using microscopy and image analysis to measure sagittal (length) and coronal (width) slices.GCN5 control and KOs were substantially different, samples were re-run. Murine GCN5 ChIPs were unsuccessfully tried with the following antibodies: Abcam 18381, Cell Signaling 3305, Santa Cruz 20698, and Santa Cruz 6303. In those experiments IPs were conducted with antibodies for other factors including N-Myc, Pol II, and AcK9H3, which all worked indicating that the ChIP assays themselves worked, but that murine GCN5 is not ChIP’able using these antibodies.Chromatin samples were prepared as previously described ChIP primers. TTA AGA AGC ACC CCT TT.mCcd2 prom 1: GGC TAA CTT CGG AGG CTT GC.mCcd2 prom 2: CTCGCN5 L2 1: AGC ATC CAC AAA TCC TCC AG.mGCN5 L2 2: TAG TCT TCC TTT GCC GCT TC.m AAC AAC CCA GAA CCA AG.mApex1 1: ACG AGC CAG AGA CCC TCA CG.mApex1 2: CTA GGG TTC TGA GCA CTT AG.mPolII 82s: GAC CAT CAG CAT CAC TGA CC.mPolII 151a: CAAG CTT AGC TCC TAT ATG CAG C.Lcn2 1: GTCTG AGC AGG GCA TAG ATC CTT.Lcn2 2: GCN5 KO neurospheres Click here for additional data file.Table S2List of genes upregulated both in GCN5 KO NSC and in N-Myc KO NSC. Genes are listed by name and sorted in descending order by fold change in the GCN5 KO.(PDF)Click here for additional data file.


Furthermore, we find that PU.1 also regulates many of the same genes and pathways in other blood cells, leading us to propose that PU.1 is a multifaceted factor with overlapping, as well as distinct, functions in several hematopoietic lineages.PU.1 is a hematopoietic transcription factor that is required for the development of myeloid and B cells. PU.1 is also expressed in erythroid progenitors, where it blocks erythroid differentiation by binding to and inhibiting the main erythroid promoting factor, GATA-1. However, other mechanisms by which PU.1 affects the fate of erythroid progenitors have not been thoroughly explored. Here, we used ChIP-Seq analysis for PU.1 and gene expression profiling in erythroid cells to show that PU.1 regulates an extensive network of genes that constitute major pathways for controlling growth and survival of immature erythroid cells. By analyzing fetal liver erythroid progenitors from mice with low PU.1 expression, we also show that the earliest erythroid committed cells are dramatically reduced  Cellular identities are established by master regulatory transcription factors that promote cell type–specific gene expression. In some instances such factors also inhibit differentiation of alternative, closely related lineages. PU.1 is an Ets family transcription factor that is required for myeloid and B cell development. PU.1 is also expressed in red blood cell progenitors where it blocks erythroid terminal differentiation. One mechanism used by PU.1 to block red blood cell differentiation is by binding to and inhibiting the erythroid master regulator GATA-1. Here, we describe another mechanism utilized by PU.1 in erythroid progenitors. Using chromatin immunoprecipitation and high-throughput sequencing (ChIP-Seq) combined with gene expression profiling of erythroid progenitors, we show that PU.1 controls a large gene network in immature erythroid cells, including genes in pathways involved in regulating growth, survival, and differentiation of these cells. We also find that PU.1 controls many of the same genes and pathways in other blood cells. The results indicate that, in addition to activating lineage-specific genes, master regulatory transcription factors, like PU.1, also control numerous, widely expressed genes in multiple cell lineages. Cellular identities are established through the actions of master regulatory transcription factors. In addition to promoting their lineage-specific gene expression programs, these factors may also inhibit the transcriptional programs of alternative cell lineages PU.1 is an Ets family transcription factor that is required for the development of myeloid and B-cells Although PU.1 is highly expressed in myeloid and B-cells, it is also normally present in immature erythroid cells −16). This similarity is even visually evident from examination of the signal tracks of 500 kb windows, such as the one displayed in As a first step in identifying the transcriptional network controlled by PU.1 in immature erythroid cells, we performed ChIP-Seq in normal proliferating erythroid progenitors derived from embryonic stem cells (ES-EP) Since PU.1 occupies a surprisingly large number  of sites in ES-EP and MEL cells , it was To better understand the network of genes that are potentially regulated by PU.1 in immature erythroid cells, we sought to associate peaks with genes. We determined that 6,826 of the PU.1 occupied sites shared by ES-EP and MEL cells lie within 2 kb of the transcription start site (TSS) of known genes   right. MSince PU.1 was found to bind to many genes, it was important to understand the functional consequences of PU.1 occupancy in immature erythroid cells. Therefore, we correlated the PU.1 ChIP-Seq data with two types of mRNA profiling in these cells. First, we compared the transcriptomes of ES-EP and MEL cells. We found that the mRNA profiles of the two cell types are quite similar, despite the differences in their phenotypic properties . NeverthSfpi1 locus encoding PU.1, resulting in reduced PU.1 expression med TER119low) isolated by flow cytometry To determine how PU.1 affects the expression of its target genes, we combined the ChIP-Seq data with comparative gene expression analysis of fetal liver erythroid progenitors from wild-type E13.5–14.0 embryos and embryos that have a deletion in an Upstream Regulatory Element (URE) at the Besides PU.1, a number of other factors have been shown to be involved in regulating erythroid differentiation and erythroid-specific gene expression. For example, the Ets protein Fli-1 To further understand the categories of genes and biological pathways regulated by PU.1 in erythroid cells, we analyzed the ChIP-Seq PU.1 targets with Ingenuity Pathway Analysis (IPA) software. IPA analysis also revealed several important cellular pathways that are regulated by PU.1 in erythroid cells. For example, 58% (79/137) of genes involved in the PI3K/Akt signaling pathway are occupied by PU.1 in ES-EP and MEL cells . Gene exex vivo cultures of PU.1-depleted fetal liver erythroblasts in vivo and the precise stage of its effects, we analyzed the distribution of five distinct populations of erythroid cells in fetal livers of mutant PU.1 low embryos and wild-type littermate embryos med TER119low) to late orthochromatophilic erythroblasts and reticulocytes (R5 - CD71med TER119high), as described previously in vivo.The foregoing results indicate that PU.1 regulates several pathways that control the proliferation, survival and differentiation of erythroid cells. Previous studies reported that −5) association of PU.1 occupied sites with consensus binding-site sequences for a number of well-studied and less well-studied transcription factors  at 15 gene targets bound by PU.1 in erythroid cells. We found PU.1 bound 100% of these genes in myeloid cells , consistPU.1 is a master regulatory transcription factor that plays an essential role in the development of the myeloid and B-cell lineages Recent work demonstrates that PU.1 directly regulates certain genes that can themselves affect erythroid differentiation. For example, we reported that the CDK6 gene is directly upregulated by PU.1 in vivo. Thus, our studies further advance the concept of master regulatory transcription factors regulating both lineage-specific genes and more widely expressed genes.However, the results reported here also reveal that PU.1 has a much broader role in these cells than simply regulating several factors that control erythroid differentiation. Indeed, the ChIP-Seq and transcriptome analyses show that PU.1 regulates many genes involved in several signaling pathways that are critical for erythropoiesis. These pathways include the PI3K/Akt, ERK/MAPK, and Jak/Stat pathways  is consiSeveral recent papers reported the genome-wide binding patterns of PU.1 in macrophages Much evidence supports the view that PU.1 plays a major role in establishing cellular identity in B-cells and myeloid cells. Finding such an extensive overlap among the PU.1 gene targets in several related but quite distinct hematopoietic cell types raises the question as to how cellular identity is established by PU.1. We suggest that the answer lies, at least in part, in the differences in the levels of PU.1 in different hematopoietic cells. Myeloid cells have the highest level of PU.1, B-cells have an intermediate level MEL cells (clone DS19) stably expressing a GATA-1-estrogen receptor ligand-binding domain (ER) fusion protein (GATA-1/ER) was described previously 7 cells and 60 µg of anti-PU.1 antiserum. A list of antibodies used in this study, along with further information on processing of ChIP-Seq samples can be found in qChIP was performed as previously described Uniquely mapped reads from ChIP-Seq data were mapped to the mouse genome (mm9). Details on peak calling and subsequent statistical analysis can be found in −/− animals using the RNeasy Kit (Qiagen) following the manufacturer's instructions. Total RNA was further processed by the AECOM microarray facility using the standard Affymetrix pipeline and hybridized to GeneChip Mouse Gene 1.0 ST (Affymetrix). This expression data can be accessed from the GEO database under the accession number GSE21953. Subsequent statistical analysis is described in Total RNA was isolated from duplicate cultures of proliferating ES-EP and MEL cells or R1 cells sorted from two wild-type and UREde novo motifs with the MEME software For each PU.1 peak, we extracted a 500-bp sequence around the peak center  and used it for searching Dataset S1PU.1 ChIP-Seq Summary. Summary of PU.1 ChIP-Seq data. File includes all peaks identified in ES-EP and MEL cells. Peaks that are within 2 kb of TSS are assigned with a RefSeq gene.(2.05 MB XLS)Click here for additional data file.Figure S1Comparisons of ChIP-Seq and qChIP data for loci differentially occupied by PU.1 in MEL cells and ES-EP. (A) Comparisons for loci exhibiting enriched occupancy of PU.1 in MEL cells. Panels 1–3 display signal tracks corresponding to 1) a gene-poor region on chromosome 15, 2) the region near the Mxi1 gene, 3) the region near the Dnmt3a gene. Panel 4 shows qChIP analyses near the peaks denoted with arrows in panels 1–3. A HA antibody was used as an isotype control. Sequences of qChIP primers are shown in (0.41 MB PDF)Click here for additional data file.Figure S2Properties of PU.1 ChIP-Seq peaks enriched in MEL cells or ES-EP. (A) The percentage of PU.1 ChIP-Seq peaks lying within the indicated distance from the closest TSS was calculated separately for peaks shared by the two cell types (Overlap) or enriched in either MEL cells or ES-EP, as described in (0.27 MB PDF)Click here for additional data file.Figure S3High correlation of gene expression in MEL cells and ES-EP. Gene expression levels were obtained separately for MEL cells and ES-EP with the Affymetrix microarray platform. After data processing and normalization, the relative expression of genes in the two samples are shown here as a scatter plot with larger units representing higher expression. The Pearson's correlation coefficient and its statistical significance are shown at the top of the figure.(0.77 MB PDF)Click here for additional data file.Figure S4qChIP validation of PU.1 occupancy near genes involved in gene regulation and cell cycle regulation. (A) and (B) qChIP was performed as described in (0.16 MB PDF)Click here for additional data file.Figure S5−5) that were found in at least 10% of peaks and ≥2 fold enriched over the expected frequency are shown. The ratio between observed frequency and expected frequency is represented by a log2ratio. (B) and (C) qChIP analysis of E2F2 (B) and E2F4 (C) occupancy was performed as described in E2F factors occupy PU.1 target genes in MEL cells. (A) DNA sequences from PU.1 ChIP-Seq peaks within the proximal promoter in both ES-EP and MEL cells were analyzed using the TRANSFAC database as described in (0.16 MB PDF)Click here for additional data file.Figure S6−/− cells induced to differentiate into macrophages was used to generate heatmaps depicting the response of the indicated genes in the PI3K/Akt and ERK/MAPK signaling pathways to the activation of PU.1. The genes shown in this analysis are the same as the genes found to be regulated by PU.1 in MEL cells Click here for additional data file.Table S1Results of Ingenuity Pathway Analysis of PU.1 Target Genes. Ingenuity Pathway Analysis was performed on genes with one or more PU.1 ChIP-Seq peaks within 2 kb of TSS in both MEL and ES-EP. The ten most significant molecular and cellular functions identified are listed in descending order of significance. The number of genes associated with a given function and bound by PU.1 is shown. p-values have been corrected for multiple testing using the Holm-Bonferroni method.(0.01 MB DOCX)Click here for additional data file.Table S2Primers used in this study.(0.02 MB DOCX)Click here for additional data file.Text S1Supplemental (0.06 MB DOC)Click here for additional data file.


Genome-wide there are several thousand binding sites and hundreds of primary 1,25(OH)2D3 target genes, but their functional relation is largely elusive. In this study, we used ChIA-PET data of the transcription factor CTCF in combination with VDR ChIP-seq data, in order to map chromatin domains containing VDR binding sites. In total, we found 1,599 such VDR containing chromatin domains and studied in THP-1 human monocytic leukemia cells four representatives of them. Our combined ChIP-seq and FAIRE-seq time course data showed that each of these four domains contained a master VDR binding site, where an increase of VDR binding pairs with 1,25(OH)2D3-promoted chromatin opening and the presence of a highly significant DR3-type sequence below the peak summit. These sites differed in their relative VDR binding but not in their kinetics, while other loci either had a weaker and delayed VDR association or could not be confirmed at all. All studied chromatin domains contained at least one primary 1,25(OH)2D3 target gene demonstrating a characteristic slope of mRNA increase, while neighboring genes responded delayed, if at all. In conclusion, the observation of ligand-inducible VDR binding and chromatin opening combined with a DR3-type sequence highlighted genome-wide 160 VDR loci that have within their chromatin domain a more than 4-fold increased likelihood to identify a primary 1,25(OH)2D3 target gene than in the vicinity of other genomic VDR binding sites.The vitamin D receptor (VDR) is a transcription factor that mediates the genomic effects of 1α,25-dihydroxyvitamin D Primary VDR target genes are identified by short incubations (2 to 4 h) with 1,25(OH)2D3, but the physiological impact of the receptor and its ligand gets more obvious after stimulations for 24 h or longer. In THP-1 cells, transcriptome analyses indicated that already 408 genes are significantly up-regulated after 4 h stimulation with 1,25(OH)2D3The nuclear receptor VDR belongs to a unique transcription factor superfamily, whose members are directly activated by small lipophilic compounds 2D3 VDR associates with co-activator proteins and histone acetyltransferases VDR binds preferentially to sequences, which are direct repeats of two hexameric binding sites with three spacing nucleotides (DR3) + T-lymphocytes, which were obtained from nine healthy human volunteers, reported between a few hundred and more than 10,000 genome-wide VDR binding sites The method chromatin immunoprecipitation sequencing (ChIP-seq) allows the monitoring of all genome-wide binding sites of transcription factors 2D3 target genes. This should allow a segregation of master VDR loci from less important sites and a more efficient identification and characterization of 1,25(OH)2D3 target genes.Chromatin loops occur when genomic sequences from the same chromosome are in close physical proximity to each other 2 incubator. Prior to chromatin or mRNA extraction, cells were grown overnight in phenol red-free medium supplemented with charcoal-stripped fetal calf serum. Then, cells were treated with solvent (0.1% ethanol) or 100 nM 1,25(OH)2D3 (Sigma-Aldrich) for the indicated time periods.THP-1 cells −(ΔCt)*100, where ΔCt is Ct(input) – Ct(immunoprecipitated DNA) and Ct is the fractional cycle number.ChIP was performed exactly as reported before http://genome.ucsc.edu/ENCODE). Our own VDR ChIP-seq (GSE27437) and FAIRE-seq (GSE40075) datasets are available at GEO (www.ncbi.nlm.nih.gov/geo). ChIP-seq and FAIRE-seq data were visualized by using the Integrative Genomics Viewer (IGV) http://genome.ucsc.edu) Publically available CTCF ChIP-seq datasets of the ENCODE consortium B2M, GAPDH and HPRT1). The stability of the expression of the reference genes was determined using the geNorm algorithm ΔCt  =  2 . For the normalization, the relative quantities were divided by the normalization factor being the geometric mean of the three reference genes.Total RNA extraction and cDNA synthesis was done as reported before In order to describe genome-wide chromatin loops, we used ENCODE data of the 3-dimensional interactions of CTCF as determined by ChIA-PET assays performed in K562 cells ZFP36) gene (SFT2D1) gene (SEPT3) gene (SP100) gene  a short chromatin domain of 12.5 kb containing three VDR binding sites close to the ZFP36 ring finger protein (36) gene , ii) a dD1) gene , iii) a T3) gene  and iv) 00) gene . We haveIn summary, 3-dimensional CTCF-mediated looping data allowed a segregation of the human genome into chromatin loops, some 1,600 of which include at least one VDR binding site. The size of these chromatin domains varies significantly. Four genomic regions (17 to 590 kb) representing this size range indicated that the number of contained VDR sites does not depend upon the loop size.2D3-induced chromatin opening with the help of FAIRE-seq time course data ZFP36 gene, only the first  and the third  were associated with open chromatin. In contrast, at the site of the second VDR peak  no sign of chromatin accessibility could be detected . Moreover, below each of the peak summits a DR3-type binding sequence is located.For a more detailed characterization of the eight VDR binding sites within the four representative genome domains, we described the relation to chromatin accessibility and 1,25(OH)detected . Interesne’s TSS . In our 2D3The four exemplified master VDR binding sites belong to a group of 160 (6.8%) VDR peaks sharing these properties . Our cri2D3-promoted chromatin opening and the presence of a high confidence DR3-type binding sequence. In total, there are 160 chromatin loops with a single master VDR site in THP-1 cells. The presence of a master VDR site increases the likelihood to identify a primary 1,25(OH)2D3 target gene in the same chromatin domain by a factor of more than 4.Taken together, FAIRE-seq time course data were well suited for a detailed analysis of VDR binding sites in relation to chromatin accessibility. Each of the four investigated chromatin loops carried only one site, where an increase of VDR binding was associated with 1,25(OH)2D3 2D3  led to t2D3  gene, SFT2D1 and P2SFT2D1 agreed with our ChIP-seq data analysis For comparison, we investigated in the same way the time-dependent VDR binding at P1P2SFT2D1 . At P1ZFhan 0.5% . In cont%  gene and nearly 9-times higher mRNA amounts than the PLEKHG2  gene , PRR18 (proline rich 18) and the uncharacterized LOC100289495, respectively  1 alpha subcomplex 6), SMDT1 (single-pass membrane protein with aspartate-rich tail 1), NAGA  and CENPM (centromere protein M), respectively, but 10- and 23-times higher than those of the genes WBP2NL  and FAM109B   gene, nearly equal to that of the genes SP110 (SP110 nuclear body protein) and SP140L (SP140 nuclear body protein-like), 5- and 18- times higher than that of the genes SP140 (SP140 nuclear body protein) and SLC16A14   regions . In its  2) gene . The SFTectively . The basember B) . The basmber 14) .2D3 on their expression 2, 4, 6 and 24 h after onset of stimulation  or at least after 4 h (SEPT3) statistically significant. The maximal induction of their mRNA accumulation varied between 1.8- (SEPT3) and 3.8-fold (SP100). The ZPF36 flanking gene PLEKHG2 showed a 1.8-fold induction after 6 h ligand stimulation mulation . Only thn 2D3 2D3 consistently up-regulated, i.e. some 45 min delayed compared to the SFT2D1 gene within the same chromatin domain  180 min  right. I5(OH)2D3 , while t 2D3, when compared to the master gene.Taken together, every investigated chromatin domain contains at least one primary 1,25(OH)2D3. The chromatin domains around the genes ZFP36, SFT2D1, SEPT3 and SP100 are representative examples of small, mid-sized and large domains ranging from 17 to 590 kb and containing 3 to 7 genes.In this study, we investigated genomic binding sites of the transcription factor VDR in relation to the 3-dimensional organization of the human genome via chromatin domains. Such higher-order genome structures contribute to many nuclear functions, including the control of gene expression ZFP36 and SFT2D1 contain each three VDR binding sites. However, we found that, in both cases, one of the three VDR sites was dominant. These master VDR binding sites have very similar properties as those in the here examined domains that contain only one receptor locus, such as those of the genes SEPT3 and SP100. Their association with VDR is ligand-inducible, as indicated by ChIP-seq data and confirmed by ChIP-qPCR time course experiments. Moreover, the local chromatin at these sites further opens after stimulation with 1,25(OH)2D3, as monitored by FAIRE-seq time course assays. In addition, the sites carry a DR3-type sequence below their VDR peak summits. Although DR3-type sequences are known since more than 20 years to be the preferred binding sites for VDR The chromatin domains of the genes 2D3 target genes are indistinguishable in their time-dependent fashion of associating with VDR. This is in accordance with our previous comparison of six other master VDR binding sites, where we used a shorter time-scale for the ligand treatments ZFP36 and SFT2D1 are also occupied in the two lymphoblastoid cell lines. Moreover, the VDR site of the SP100 gene is even found in all published VDR ChIP-seq datasets SEPT3 gene seems to be specific to THP-1 cells. This suggests that master VDR binding sites may more likely be conserved between tissues and cell types than other VDR loci.VDR binding to its genomic loci is saturated after some 2 h. A comparable dynamic upload of a transcription factor to its binding sites has been shown before for RARγ 2D3 target gene within the investigated chromatin domain. The distances range from 12 kb upstream to 8.5 kb downstream of the respective TSS. This would have been considered fairly large in the pre-genome era 2D3. The kinetics of the up-regulation of the MPC1 gene within the chromatin domain around the SFT2D1 gene suggests that it is also a primary target of 1,25(OH)2D3, but it responds delayed compared to the master target gene. In contrast, the transient up-regulation 2D3) of the PLEKHG2 gene within the ZFP36 domain or the late responses (after 24 h) of the genes NDUFA6 and SP140L within the domain of SEPT3 and SP100, respectively, indicate that they are most likely secondary targets. For the overall physiological impact of vitamin D, a distinction between primary and secondary targets is less critical. However, for effective preventive intervention of healthy persons with vitamin D, or even a therapeutic application of 1,25(OH)2D3 analogs in the disease case, the mechanism and timing of responses to VDR ligands are important.In all four example cases, the TSS region closest to the master VDR binding site turned out to be that of the fastest responding and most up-regulated primary 1,25(OH)ZFP36, SFT2D1, MPC1, SEPT3 and SP100 have not yet been explicitly described as primary targets of 1,25(OH)2D3. The protein product of ZFP36 gene is the RNA binding protein tristetraprolin SFT2D1 gene encodes for an uncharacterized vesicle transport protein, while the product of its neighbor MPC1 is a mitochondrial pyruvate carrier protein SEPT3 gene encodes for a member of the septin family of GTPases, which are required for cytokinesis 2D3 targets represent rather different physiological functions ranging from the control of immune response and metabolism to cellular growth. This re-emphasizes the pleiotropic function of vitamin D and its impact on cells of the hematopoietic system.The genes 2D3 target gene within the same chromatin loop. This approach improves the identification and characterization of primary 1,25(OH)2D3 target genes and demonstrates the wide physiological impact of vitamin D.In conclusion, the observation of ligand-inducible VDR binding and chromatin opening in combination with a DR3-type sequence at the respective site is a very strong indication for an important VDR location with a high likelihood for at least one primary 1,25(OH)Figure S1Chromatin domains determined by CTCF binding sites. The IGV browser was used to display for the chromatin domains around the genes ZFP36 (A), SFT2D1 (B), SEPT3 (C) and SP100 (D) CTCF ChIP-seq data from the ENCODE cell lines K562 (red) and MCF-7 (blue) (PDF)Click here for additional data file.Figure S2Size range of VDR containing chromatin domains. The distribution of the 1,599 VDR containing chromatin domains Click here for additional data file.Figure S3Genomic view of VDR association and open chromatin at peaks 1 and 2 of the SFT2D1 locus. The IGV browser was used to visualize the genomic region of P1SFT2D1 and P2SFT2D1 (+/−15 kb of the center between both peaks). The peak tracks display data from a VDR ChIP-seq experiment in THP-1 cells 2D3  treatment 2D3 treatments for the indicated time periods (PDF)Click here for additional data file.Figure S4Definition of master VDR binding sites. Within the list of 2,340 VDR peaks (PDF)Click here for additional data file.Figure S5Dynamics of VDR association. ChIP-qPCR was performed to determine VDR association (blue) and unspecific IgG binding (grey) at P1ZFP36 (A), P2ZFP36 (B), P1SFT2D1 (C) and P2SFT2D1 (D) and the negative control region of the MB gene (E). THP-1 cells were stimulated for 1, 2, 3, 4, 5 and 24 h with 100 nM 1,25(OH)2D3 and chromatin was extracted. The data points represent the means of at least three independent experiments and the bars indicate standard deviations. The unspecific background binding at the negative control region (E) was subtracted from A–D. Two-tailed Student’s t-tests were performed to determine the significance of VDR association in reference to IgG background .(PDF)Click here for additional data file.Figure S6Basal mRNA expression of the genes within the four exemplary chromatin domains. qPCR was performed to determine the relative basal expression of all genes within the chromatin loop used in this study  in untreated THP-1 cells. The data points represent the means of three independent experiments (each performed in triplicate) and the bars indicate standard deviations.(PDF)Click here for additional data file.Figure S7Time course expression profiling of neighboring genes. qPCR was performed to determine the relative changes of mRNA expression of the genes that co-locate with the VDR target genes ZFP36 (A), SFT2D1 (B), SEPT3 (C) and SP100 (D) in the same chromatin loop normalized by the three reference genes B2M, GAPDH and HPRT1. THP-1 cells were incubated with 100 nM 1,25(OH)2D3 for 2, 4, 6 and 24 h. The columns represent the means of three independent experiments (each performed in triplicate) and the bars indicate standard deviations. Two-tailed Student’s t-tests were performed to determine the significance of the mRNA induction by 1,25(OH)2D3 in reference to solvent-treated cells .(PDF)Click here for additional data file.Figure S8Detailed time course expression profiling of the MPC1 gene. qPCR was performed to determine the relative changes of mRNA expression of the MPC1 gene normalized by the three reference genes B2M, GAPDH and HPRT1. THP-1 cells were incubated at 15 min intervals over a time period of 180 min. Data points represent the means of three independent experiments (each performed in triplicate) and the bars indicate standard deviations. Two-tailed Student’s t-tests were performed to determine the significance of the mRNA induction by 1,25(OH)2D3 in reference to solvent-treated cells .(PDF)Click here for additional data file.Table S1ChIP-qPCR primers.(PDF)Click here for additional data file.Table S2Reverse transcription qPCR primers.(PDF)Click here for additional data file.Table S3VDR ChIP-seq peaks associated with chromatin loops. The 2,340 VDR ChIP-seq peaks found in THP-1 cells 2D3 - and EtOH-treated FAIRE-seq signals ZFP36 gene, which therefore was not investigated in this study.(XLSX)Click here for additional data file.Table S42D3 target genes are enriched with VDR containing chromatin domains.Primary 1,25(OH) The 408 significantly up-regulated primary 1,25(OH)2D3 target genes in THP-1 cells (XLSX)Click here for additional data file.


Drosophila and identified ∼50 genes that strongly impact the ovarian somatic piRNA pathway. Many identified genes fall into functional categories that indicate essential roles for mitochondrial metabolism, RNA export, the nuclear pore, transcription elongation, and chromatin regulation in the pathway. Follow-up studies on two factors demonstrate that components acting at distinct hierarchical levels of the pathway were identified. Finally, we define CG2183/Gasz as an essential primary piRNA biogenesis factor in somatic and germline cells. Based on the similarities between insect and vertebrate piRNA pathways, our results have far-reaching implications for the understanding of this conserved genome defense system.The piRNA (PIWI-interacting RNA) pathway is a small RNA silencing system that acts in animal gonads and protects the genome against the deleterious influence of transposons. A major bottleneck in the field is the lack of comprehensive knowledge of the factors and molecular processes that constitute this pathway. We conducted an RNAi screen in  •DrosophilaSystematic identification of somatic piRNA pathway factors in •Identification of functional links between piRNA biology and major cellular processes•Characterization of CG2183/Gasz as an essential primary piRNA biogenesis factor Drosophila melanogaster genome contains ∼15%–20% of transposable elements (TEs) (The ts (TEs) . Uncontrts (TEs) . This ists (TEs) . Defectsts (TEs) .Similar to other small RNA pathways, the central players in the piRNA pathway are Argonaute proteins bound to small RNAs that provide target specificity . HoweverDrosophila ovary contains two major cell types: germline cells derived from primordial germ cells and somatic support cells of mesodermal origin. Both cell types silence TEs by the piRNA pathway, but the respective pathways differ considerably  that harbor different piRNA pathway versions  are complementary to this region and guide efficient reporter silencing in wild-type flies (flamenco restrictive). Flies harboring a functional piRNA pathway but lacking the ability to produce gypsy complementary piRNAs (flamenco permissive) exhibit strong reporter desilencing. Similarly, knockdown of somatic piRNA pathway components such as Armitage (Armi), Zucchini (Zuc), or Vreteno (Vret) in a flamenco restrictive background results in near loss of gypsy-derived piRNAs and reporter desilencing (gypsy-lacZ reporter assays the endpoint of the piRNA pathway (namely piRNA-mediated silencing in trans), the screen should capture factors involved at all steps of the piRNA pathway   under control of the ypsy LTR . The resilencing . As the lencing) C.To filter for genes with possible functions in the somatic piRNA pathway, we determined the gene expression profile of OSCs by RNA-seq. OSCs were derived from a somatic stem cell population of the germarium and express a functional piRNA pathway . At a coWe used the collection of transgenic RNAi lines from the Vienna RNAi Center (VDRC)  and scregypsy-lacZ reporter mirror derepression of endogenous TEs?For an overall evaluation of the screen, we considered three questions: (1) What is the false negative rate? (2) What is the false positive rate? (3) How well does the The number of known genes acting in the somatic piRNA pathway is limited (n = 8) and not well suited for a false negative estimation. To determine the efficacy of VDRC RNAi lines, we evaluated the ovarian morphology resulting from knockdowns of genes encoding for ribosomal proteins (n = 83). Defective ribosome biogenesis results in a “rudimentary ovary” phenotype, and based on this, ∼83% of VDRC lines elicit efficient target knockdown A. As we Eight genes have been assigned to the somatic piRNA pathway, and with the exception of the Tdrd12 member SoYb , all of these were identified among the top scoring candidates D Haase . We concgypsy-lacZ sensor being influenced only by the piRNA pathway and also on the extent of screen artifacts such as off-targets. Remarkably, only 2% (144 genes) of all tested genes showed any β-gal activity, and only 0.7% (49 genes) showed activity in the range of known pathway members  terms B. Surprificantly D. As manDrosophila follicle cells   factors The central RNA export factors small bristles (Nxf1) and Nxt1 strongly derepressed the sensor. These factors are implicated in general mRNA export, and ovarian morphology was distorted in the respective knockdowns. Nevertheless, the extent of sensor and TE derepression upon knockdown of these factors hints at a direct involvement of this protein complex, potentially at the level of piRNA cluster transcript export. Interestingly, Nxf2 and Nxf3 are specifically expressed in ovaries (but not in OSCs), and their knockdown in the germline results in TE silencing defects (unpublished observations), strongly substantiating the central importance of RNA export processes in the piRNA pathway.Four nuclear pore complex factors  and one accessory factor (CG14749) scored in the screen. The two FG repeat proteins Nup54 and Nup58 were among the strongest-scoring candidates, and given that ovarian morphology was normal in the respective knockdowns, we postulate that an intricate and specific connection exists between the nuclear pore complex and piRNA biology. All of these factors appear to be specifically involved in the soma piRNA pathway E. Our fiflamenco, for example, is a locus that spans at least 180 kb of repetitive sequence, and all evidence suggests that it is transcribed from a single promoter. Productive transcription through such a large locus might well require factors that reactivate backtracked polymerase. The soma-specific requirement of these factors suggests that transcription of germline piRNA clusters differs from that of flamenco. This might relate to their bidirectional transcription and the specific requirement for Rhino and Cutoff for their transcription/processing  while GFP-CG2183 localized to the cytoplasm (where piRNA biogenesis occurs) both in OSCs as well as in ovaries G and 5H.The subcellular localization of GFP-CG2183 was highly reminiscent of mitochondrial patterns and of the reported Zuc localization . In fact42AB collapsed nearly entirely in all three knockdowns  and I-element (collapsed piRNA profile) exemplify the differential impact of loss of type I biogenesis factors on piRNA populations  has been linked to piRNA biogenesis  with the upstream sorting of piRNA precursors.The mechanistic understanding of the piRNA pathway is very limited, and systematic studies are hampered due to its gonad-restricted activity. Therefore, our in vivo RNAi screen provides an invaluable entry point into the dissection of this silencing system. In the following, we critically assess two key aspects that are relevant to this work.gypsy-lacZ sensor faithfully recapitulates defects at all levels of the piRNA pathway, such as flamenco mutations, piRNA biogenesis defects, or defects in piRNA-mediated silencing.Transgenic RNAi screens based on the expression of long dsRNA hairpins must be rigorously evaluated in terms of their false negative and false positive rates. The false negative rate is influenced by the availability of VDRC lines (94% for this screen), by the number of genes that cannot be evaluated due to overall defects in ovarian development (10% for this screen), and by the knockdown efficiency of the VDRC lines toward their respective target genes (estimated to be ∼90% if two lines per gene were tested). Based on this, we conservatively estimate that our screen captured up to 80% of the key factors involved in the somatic piRNA pathway. We point out that the Our screen is relatively blind toward genes that are essential for cellular viability and that therefore could not be evaluated. It is clear that at some point piRNA-specific processes will connect to more general cellular biology. For example, the key heterochromatin protein Su(var)205 (HP1) is likely to be important for piRNA-mediated silencing , but it gypsy-lacZ sensor picks up even moderate defects in TE silencing, the number of factors with indirect impacts will be significant. For example, at least 38 factors can be clearly assigned to aspects of mitochondrial metabolism. As expected for such “indirect” factors, these scored weakly in the screen. We note that the identification of processes or factors that impact the piRNA pathway indirectly might still be valuable. For example, mitochondrial integrity and/or number likely influence TE silencing, as at least two piRNA biogenesis factors are mitochondrial transmembrane proteins. Mitochondrial function is diminished upon aging. As aging has been linked to increased mobility of TEs, it is tempting to speculate that aspects of this might be linked to modulations of piRNA pathway activity.We identified 144 genes with potential involvement in the somatic piRNA pathway. This set includes (1) true positives, (2) factors with indirect impact on the pathway, and (3) RNAi artifacts. Considering that the False positives due to screening artifacts are common for transgenic RNAi screens. They stem from off-target effects of generated siRNAs but can also arise from gain-of-function conditions of genes located in the vicinity of the VDRC transgene insertion. Both aspects, however, are expected to impact our screen only marginally. First, the set of core factors acting in the pathway is unlikely to exceed 30–40 genes, making off-target effects less likely. Second, the screen was based on manual evaluation of a transgenic reporter assay, a highly specific phenotype. And third, only very few genes (if any) will impair TE silencing if overexpressed. These arguments are strongly supported by an experimental validation of nearly all of the 30 tested candidates by an independent RNAi line.Taken together, our screen represents a highly enriched set of somatic piRNA pathway genes. We speculate that ∼30 factors are either core factors of the somatic piRNA pathway or have direct impact on its function. This testifies to the remarkable complexity of the piRNA pathway compared to the siRNA or miRNA pathways.Drosophila ovary. First, expression of Aub and AGO3 as well as the process of secondary piRNA biogenesis is germline specific  or that they are indeed required specifically in somatic cells.Drosophila piRNA pathway. Given that several of the key processes and involved factors are conserved in vertebrates, our data will also influence studies in the model systems zebrafish and mouse.All in all, the identified somatic piRNA pathway factors and the established genetic and cell-biological tools will advance investigations on the Fly stocks are listed in the Primer sequences and details are given in the shRNA lines were cloned into the Valium-20 vector  modifiedSmall RNA cloning and sequencing was performed as in α-Piwi, α-Aub, and α-AGO3 (rabbit) were described in IF staining of ovaries and OSCs was done as in Total RNA was isolated from respective knockdowns and separated on a 15% Urea-PAA gel. After transfer onto a membrane, radioactively labeled probes were hybridized over night. Probe sequences and details are given in the OSCs were cultured as described as in


Intriguingly, TRα2 is strongly expressed in the brain, where its mRNA levels exceed those of functional TRs. Ablation of TRα2 in mice results in over-expression of TRα1, and a complex phenotype with low levels of free T3 and T4, without elevated TSH levels, suggesting an alteration in the negative feedback at the hypothalamic-pituitary level. As the hypothesis of a potential TRH response defect has never been tested, we explored the functional role of TRα2 in negative feedback on transcription of hypothalamic thyrotropin, Trh. The in vivo transcriptional effects of TRα2 on hypothalamic Trh were analysed using an in vivo reporter gene approach. Effects on Trh-luc expression were examined to that of two, T3 positively regulated genes used as controls. Applying in vivo gene transfer showed that TRα2 over-expression in the mouse hypothαlamus abrogates T3-dependent repression of Trh and T3 activation of positively regulated promoters, blocking their physiological regulation. Surprisingly, loss of function studies carried out by introducing a shTRα2 construct in the hypothalamus also blocked physiological T3 dependent regulation. Thus, modulating hypothalamic TRα2 expression by either gain or loss of function abrogated T3 dependent regulation of Trh transcription, producing constant transcriptional levels insensitive to feedback. This loss of physiological regulation was reflected at the level of the endogenous Trh gene, were gain or loss of function held mRNA levels constant. These results reveal the as yet undescribed dominant negative role of TRα2 over TRα1 effect on hypothalamic Trh transcription.Mammalian thyroid hormone receptors (TRs) have multiple isoforms, including the  T3In vitro, TRα2 blocks the activity of other TRs by competing for TR binding to thyroid hormone response elements (TREs) on DNA via mechanisms that do not require TRE binding In mammals, TRα2 is identical to TRα1 in its N-terminus, but the C-terminus is entirely different rendering TRα2 unable to bind T3-dependent regulation of target genes in the brain -/-mice, TRα2 ablation results in TRα1 over-expression in brain tissue, and lower levels of free T3 and T4 but normal levels of TSH. This failure of TSH to adjust to the lower circulating T3 and T4 levels can be accounted for either by an effect at the level of the thyroid gland reducing hormone production, and/or an alteration in the negative feedback at the hypothalamic-pituitary level, which may also include a defect in TRH response. However, this latter hypothesis has never been tested. Previous studies on TRα2 function in brain have attributed a general dominant negative effect of TRα2 but never addressed its transcriptional effects on target genes in vivo, because of the technical challenge it represents. Thus we employed a synthetic gene transfer method in which our laboratory has a great expertise to follow the effects of TRα2 gain or loss of function on Trh gene transcription using positively regulated T3 genes  as controls. This in vivo transfection assay provides for tissue specific physiological regulation of transcription in integrated contexts Trh transcription in vivoGeneration of mutant mice lacking TRα2 has contributed to understand the roles of TRα2 on TIn vivo over-expression experiments show that in the hypothalamus, TRα2 acts as a dominant-negative receptor, blocking transcription of both positively and negatively T3 regulated target genes. Moreover, transient TRα2 knockdown seems to reveal TRα1 effect on Trh promoter, the regulation of which being equivalent to the one observed when TRα1 is over-expressed. This hypothesis was emphasised by a decrease in circulating T4 following TRα1 gain of function. Interestingly, both gain or loss of TRα2 function seems to block Trh transcription at an intermediate level between activated and repressed control levels. Indeed, an average TRH activity remains, whereas fine physiological T3 regulation is lost. Taken together, these results reveal the physiological importance of TRα2, naturally acting as dominant-negative receptor on hypothalamic Trh transcription in vivo.All aspects of animal care and experimentation were in accordance with the National institutes of Health Guidelines for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee of the Animal Protection and Health, Veterinary Services Direction, Paris, France.Swiss wild-type mice were purchased from Janvier . To induce foetal and neonatal hypothyroidism, dams were given iodine-deficient food containing 0.15% 6-n-propyl-2-thiouracil (PTU) (Harlan) and drinking water with 0.5 g/l PTU (Sigma-Aldrich) from day 14 of gestation through lactation.Trh-luciferase (Trh-luc) and rat (r) pSG5-TRα1 constructs have been described previously Tyrosine hydroxylase-luciferase reporter plasmid (TyrH-luc), containing 800 pb of the rat Tyrosine hydroxylase gene promoter firefly luciferase coding sequence in the pGL2 backbone, was a gift from Dr J Mallet . Human (h) TRα2 in pSG5 was kindly provided by Dr. Chassande and Dr Samarut . TRα2 is highly conserved in man, rat and mouse.To knockdown endogenous TRα2, shRNA-coding plasmids were designed against TRα2 (CMV H1- shTRα2), providing two shRNA sets (sh1TRα2 and sh2TRα2) as described in Decherf et al. , SI. The5′- AAGGACAGCAGCTTCTCGGATT-3′) and antisense (5′- AATCCGAGAAGCTGCTGTCCTT-3′)For sh1TRα2, siRNA sequences are: sense (5′- TGCAGAGTTCGATTCTGTACTT-3′) and antisense (5′- AAGTACAGAATCGAACTCTGCA-3′).For sh2TRα2, siRNA sequences are: sense  complexed with PEI. To assess the effect of TR overexpression, in addition of the reporter genes, we added pSG5-TRα1, pSG5-TRα2, or empty pSG5 expression vector in the complexes at 100 ng/pup. Luciferase activity was assayed 18 h after transfection. In shRNA experiments, we added small hairpin expression vector (see section plasmids) at a 100 ng/µL concentration (400 ng/pup). After 48 h, pups were decapitated, and hypothalami were dissected out for luciferase assays following the manufacturer's protocol (Promega). Luciferase activity was measured 48 h later to allow for shRNA expression.DNA/PEI (polyethylenimine) complexes, For qPCR analysis, pups were only transfected with either the overexpression vectors or small-hairpin RNA vectors. Transfections were performed in 2 days old pups and the hypothalami were dissected at 1, 3 and 5 days post-transfection for overexpression experiments, and at 36 h post transfection (3.5 days) for sh experiments.3 effects on reporter gene expression, pups were injected subcutaneously, with 2.5 µg/g of body weight (bw) of T3  in 0.9% saline, immediately after transfection. This quite high dose of T3 is necessary to observe Trh gene regulation in the hypothalamus of newborn mice, because global metabolic rate is high at this developmental stage and the brain is a resistant organ to excess of TH levels To assess T4 RIA Kit . Results are expressed as means ± SEM.Frozen plasma was thawed and processed according to the supplier's instructions, using the AMERLEX-M TWestern blot analysis was made on hypothalamus protein extract from brains transfected with CMV H1-shTRα2. Briefly, the hypothalami of two distinct mice were collected for each group under a stereo-microscope. The whole experiment was repeated twice. Tissues were lysed mechanically and proteins were extracted in RIPA buffer  according to the manufacturer's instructions. Protein content was determined by Qbit assays (Invitrogen). Total cell lysates (30 µg) were fractionated by SDS-PAGE 4–20% (Pierce) and transferred to nitrocellulose membranes (Biorad). Membranes were blocked with 5% non fat milk in Tris-buffer saline , followed by overnight incubation at 4°C with the indicated antibody diluted in TBS with 0.05% Tween-20 (TBS-T). After three washes with TBS-T, membranes were incubated with the appropriate secondary antibody coupled to peroxidase, and immunocomplexes visualized by enhanced chemiluminescence  according to manufacturer's instructions. Primary antibodies for Western-blotting, included rabbit polyclonal anti-TRα2 , rabbit polyclonal anti-βACTIN . Secondary antibody was anti-rabbit IgG Peroxidase Conjugate from Sigma. Chemiluminescence was revealed by film exposure.in vivo gene transfer) under stereo-microscope  and kept in “RNAlater”  until extraction. RNA extraction was performed using RNAble reagent following manufacturer's protocol . Concentration (A260) of the total RNA was determined and RNA was stored in Tris 10 mM/EDTA 0.1 mM (PH 7.4) at −80°C.Hypothalami were dissected from individual newborn mice  according to manufacturer's protocol. Control reactions without reverse-transcriptase were done in parallel.Trh primers were described in 18S primers and TaqMan probe were provided in the kit Eukaryotic 18S rRNA Endogenous Control  from Applied Biosystems, Warrington, UK. Trh). For Trh, 2 µl of cDNA were added to a mix containing Trh primers (300 nM), and 2x SYBR Green Master Mix (Applied Biosystems) to a final reactional volume of 20 µl. For 18S RNA (endogenous control), samples containing 2 µl of cDNA, 1 µl of 18S probe and 10 µl of 2x TaqManR universal PCR Master Mix (Applied Biosystems) were prepared in a final volume of 20 µl. The gene-specific PCR products were measured continuously by means of ABI PRISM 7300 Sequence Detection System (Applied Biosystems) during 40 cycles. All experiments were run in duplicate, and the same thermal cycling parameters were used (95°C for 10 min (1 cycle), 95°C for 15 sec and 60°C for 1 min (40 cycles)). Non-template controls and dissociation curves were used to detect primer-dimer conformation and non-specific amplification. According to the widely accepted MiQE . Given this efficiency, which is required to be able to use the ddCT relative quantification method, we can deduce that we can use these primers for standard quality qPCR studies. The threshold cycle (CT) of each target product was determined and ΔCT between target and endogenous control was calculated. The CT is the number of PCR cycles required for the fluorescence signal to exceed the detection threshold value. The detection threshold was set to the log linear range of the amplification curve and kept constant for all data analysis. The difference in ΔCT values of two genes (ΔΔCT) was used to calculate the fold difference (F = 2-ΔΔCT). The relative quantitative results were used to determine changes in Trh gene expression in groups where TRα1 or TRα2 was overexpressed as compared to control samples (empty pSG5 vector) at the ages shown. In the shRNA experiments, the GapdhDirect detection of the PCR product was monitored by measuring the increase in fluorescence generated by the TaqMan probe (18S) or by the binding of SYBR Green to dsDNA  was used to assess for statistical differences. For post-test comparisons, we took into account the multiple testing factor, using a non parametric solution. p<0.05 was considered significant . Each experiment was carried out with n ≥10, repeated at least two times providing the same results. For qPCR experiments, data were plotted as traditional Tukey whiskers . Statistical analysis compared the median of ΔCT values using nonparametric ANOVA, followed by a permutation test  to compare the control and treated groups.For in vivo, two reporter gene assays were carried out, using a positively T3 regulated promoter (ME-tk-luc) and a negatively regulated one (Trh-luc). In both cases we compared the effects of TRα2 over-expression to those of TRα1 overexpression, the action of which being already well characterised on both promoters in vivo.To test whether TRα2 acts as a dominant-negative receptor on hypothalamic gene transcription ME-tk-luc construct in the in vivo transfection paradigm, we found that in controls (3 significantly increased ME-tk-luc transcription by two fold (p<0.001). When TRα2 was co-transfected with ME-tk-luc, transcription was blocked at the basal level seen in controls in the presence of T3 . Thus, TRα2 does act as a dominant-negative receptor in vivo, blocking the regulation of transcription from a positively regulated TRE.When using the Trh gene, using the same in vivo gene transfer paradigm. In control mice, expression from the Trh-luc construct (co-transfected with an empty expression vector) was reduced by 37% in animals injected with T3 as compared with animals receiving saline  using iGT as described above. We found that in controls transfected with shCt (3 significantly increased TyrH-luc transcription (p<0.01). The same transcriptional profile is obtained when shTRα2 is co-transfected, with T3 significantly increasing TyrH-luc transcription (p<0.01)  when TRα2 was over-expressed as compared to Ct  was repressed significantly by 60.5% (p<0.001) in animals injected with T3 as compared with animals receiving saline . Similarly, no significant effects were seen on endogenous Trh levels when shTRα2 or shTRα1 was transfected as compared to controls   Trh transcription.It is intriguing to note that of all the four main products  of the two TR genes (NR1A1 and NR1A2), the mRNA of the non-hormone-binding variant TRα2 is by far the most highly expressed in the brain Trh transcription using an in vivo reporter gene approach. Three reasons, besides the phenotype of the TRα2-/- mouse, made Trh promoter of a particular interest in terms of function of this enigmatic TRα2 isoform. First, Trh gene regulation allows one to investigate TR isoforms specificity as TRβ and TRα have distinct roles in the negative transcriptional regulation by T3Trh is a critical component of the H-P-T axis and is thus a critical regulatory gene. Third, Trh is a T3 negatively regulated target gene and is of particular mechanistic interest from the transcriptional point of view. It is important to discuss here the fact that it is often considered that the HPT axis is immature in the postnatal mouse. This concept is largely based on the observations of the low levels of circulating T3 and T4 levels that increase steadily during the first two weeks of postnatal life peaking at p15 and then declining slightly to reach adult levels 3 and T4 levels by administrating PTU increase Trh expression. Thus even if the axis is not fully mature, the components of negative feedback are present . In both cases we compared the effects of TRα2 to those obtained with TRα1, because TRα1 action on both genes of interest has already been well characterised in vivo. We observed that in absence of T3, TRα1 fails to repress ME expression, suggesting that level of endogenous TRα1 was already sufficient to repress basal ME expression. In contrast, TRα2 was able to increase basal ME transcription, suggesting that when TRα2 is overexpressed it acts as a dominant-negative receptor, competing with endogenous TRα1 as to lead to an increase in ME transcription.We started our study by iGT experiments conducted on hypothyroid newborn mice, to reduce high variability in endogenous thyroid hormone levels, which could compromise transcriptional regulation study. The results on 3-regulated Trh gene, TRα1 prevents the T3-indepedent Trh activation, and increases the T3-dependent repression observed in the control group. Thus, TRα2 acts as a dominant-negative receptor on both positively and negatively regulated T3 target genes. Our in vivo result on ME-tk-luc transcription was in accordance with data conducted on transfected cells where TRα2 exerted a negative effect on T3-positive response element-mediated transcription in vitro. Two different mechanisms have been proposed: the first, described by Katz et al. Regarding the negatively T3 negatively regulated gene Trh, the gain of function of TRα2, when compared to the gain of function of TRα1, results in strong activation of transcription that is unmodified by presence or absence of T3. In contrast, TRα1 overexpression down-regulates Trh transcription and this regulation is equally T3 insensitive. Each of these regulations, contrast with the physiological T3-dependent repression of Trh in the presence of TRβ isoforms 3-independent activation of Trh transcription by TRα2 suggests a possible role of TRα2 in vivo, acting as a dominant-negative receptor on negative T3-regulated genes. In vitro experiments have not been able to reveal such a role. When transfected into JEG-3 cells, TRα2 isoform was inactive on positively and negatively regulated T3 response genes whereas TRα1 and TRβ stimulated transcription from TRE-tk-CAT (pTRE), and repressed TSHα-CAT (nTRE) reporter genes in T3-dependent manners. When coexpressed with TRα1 or TRβ at relatively high doses, TRα2 inhibited regulation of positive TREs but did not affect negative regulation For the TTrh regulation, we used transient knockdown of TRα2 using an shRNA approach. Effects were also followed on positively T3-regulated target genes (Tyrosine hydroxylase (TyrH) and ME enzymes). TRα2 overexpression abrogated T3-dependent transcription on both of these positively T3 regulated target genes, whereas TRα2 transient knockdown maintains T3-dependent TyrH-luc transcription. This result confirms the dominant negative action of TRα2 on positively regulated T3 target genes since its knockdown unmasks functional TRs transcriptional effects. Intriguingly, transient knockdown of TRα2 has the same effect as its overexpression on Trh gene transcription . Indeed, when TRα2 is overexpressed it leads to an imbalance in the transcriptional machinery, thus impairing the well-defined effect of TRβ on T3-dependent Trh transcriptional repression Trh-luc transcriptional levels as when TRα1 is overexpressed.To explore further the function of TRα2 in Trh mRNA levels, qPCR analyses were conducted on euthyroid mice so as to examine the dynamics of feedback in physiologically normal animals. We obtained no differential effects of the two overexpressed isoforms in the shorter frame at one day post-transfection (3 days old mice) nor at three days post-transfection. Similarly, no detectable effect was seen on endogenous Trh mRNA levels in the shorter frame (at 36 h post-transfection) when shTRα2 or shTRα1 was transfected. These data showing no detectable variations in Trh mRNA levels in the shorter term fit with those published by a number of authors Trh mRNA varied within longer time frames.In order to study the effects of TRα1 and TRα2 on endogenous Trh-luc transcriptional activity as TRα1 overexpression. We therefore suggest that in physiological conditions there is a balance between the effects of TRα2 and TRα1 allowing the overriding effects of the TRβ isoforms that provide physiological T3-dependent Trh regulation.To propose a model of TRα1 and TRα2 interaction, it is easiest to start from the TRα2 loss of function studies. The mutant mice show loss of TRα2 to increase TRα1 expression. In effect, we observe that hypothalamic TRα2 loss of function has the same effect on 3-dependent transcription genes. Similarly, a changed balance of TRα2/TRα1 in a context where TRα2 is overexpressed would have consequent transcriptional effects on brain gene expression. A biological activity that can be attributed to TRα2 would be an adjustment of the T3 binding TRα1 protein activity to physiologically appropriate levels, implying thus an important, widespread regulatory role in mammalian physiology of the ratio of TRα1/TRα2 expression. Our data showing that TRα2 does indeed act as a dominant-negative receptor on both negatively and positively T3 regulated target genes in the brain strongly bolster this hypothesis.Indeed, the results from the mutant mice studies in vivo function for the nonbinding TRα2 isoform in regulating brain genes, supported by findings on athyroid Pax8-/- TRα1-/- mice who die around weaning unless they are substituted with thyroid hormones due to the negative effects of the TRα1 aporeceptor, but, rather, including a more complex mechanism involving TRα2 and unliganded TR isoform TRΔα2 In this report we define an Trh transcription, and consequently, on the regulation of HPT axis.Taken together, our results emphasize the as yet neglected physiological importance of TRα2, naturally acting as dominant-negative receptor on hypothalamic