Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression. Huntington’s disease is a complex neurodegenerative disorder caused by CAG expansion in the huntingtin gene. Huntington’s disease leads to abnormal involuntary movements in patients, but recent studies have shown that many non-motor psychiatric and sleep changes also manifest in the earliest phases of the disease, often before gross changes in movement. This study identifies gene networks that are affected by Huntington’s disease and associated with these non-motor traits, revealing biological pathways and the cell-types likely involved in the earliest stages of Huntington’s disease. Huntington’s disease (HD) is a progressive and fatal neurodegenerative disorder caused by abnormal expansion of the CAG repeat in the huntingtin gene (HTT). Mutant huntingtin protein causes variable morphological pathology and differential gene expression throughout the brain, with the striatum exhibiting the earliest and most severe effects. ConsequThere is rapidly accumulating evidence that many genetic and molecular factors contribute to complex phenotypes,12, and To perform this analysis, we primarily integrated two different datasets: 1) a public microarray dataset (GSE3790 from GEO) from a human HD cohort and age- and sex-matched controls; and 2) genetic, phenotypic, and RNA-sequencing data from a large (N = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements). Previous work has described systems-based pathologies involved in the onset and progression of HD–16, and To identify HD-relevant molecular networks, we examined publicly available human microarray gene expression data (GSE3790) from 203 brain samples. The HD-gene-positive cases include 39 caudate nucleus (CN) samples, 38 cerebellar (CB) samples, and 37 frontal cortex (CTX) samples, and the age- and sex-matched controls include 28 CN, 32 CB, and 29 CTX samples. Using weighted gene coexpression network analysis, we consTo identify modules most relevant to HD, we compared modules between the pathological cohort and a cohort of age- and sex-matched controls. For each brain region, we calculated modular differential connectivity (MDC), which measures changes in connectivity in HD-associated modules with respect to their module counterpart in the control cohort. We idenSince differential connectivity reveals novel pathological information, we investigated if network-level analysis supported the common notion that HD primarily affects the caudate. Hodges et al. (2006) argues that the number of differentially expressed genes in HD parallels morphological pathology (caudate>cortex>cerebellum), and we P = 5.7 x 10−23; GSE3790: P = 7.1 x 10−19; Bonferroni corrected, Fisher’s exact test), particularly genes upregulated in HD .Since the caudate network is most significantly affected in HD, we focused our downstream analysis on this brain region. We reasoned that modules most relevant to HD would exhibit both gene- and network-level dysregulation, so we ranked modules by their MDC score and their enrichment for differentially expressed genes in HD . We calcP = 1.1 x 10−82), which was specific to in vivo astrocytes (P = 3.0 x 10−40) and not cultured astrocytes (P > 0.05). Thistle2 was not overrepresented with either neuronal (P > 0.05) or oligodendrocyte (P > 0.05) gene signatures. We also noted Thistle2 was enriched with an independent astrocyte gene signature derived from in situ hybridization[P = 4.7 x 10−10). Lastly, we found that this module was overrepresented with an astrocyte-specific proteome signature as well [P = 2.6 x 10−84). These analyses support the conclusion that Thistle2 is astrocyte specific using multiple independently derived cell-type specific signatures across two orthogonal data types. Additional analyses confirmed that this module enrichment was not correlated with pathological grade, suggesting it is unlikely that this network simply reflects astrocytosis . We noteidization.Since genes in the Thistle2 module are coexpressed, we reasoned that their common regulatory control could be driven a by shared transcription factor (TF). We identified 36 TFs predicted to regulate the astrocyte module and calculated a composite rank statistic that prioritizes TFs by their binding site enrichment score and differential expression profile in HD . FOXO3, P = 7.33 x 10−8)  . TogetheH. sapiens Huntington’s disease cohort (hsHD) to the modules previously calculated from RNA-seq data (N = 100) in the M. musculus sleep/stress cohort (mmSS). This analysis revealed that 18% of hsHD modules were also coexpressed in the mmSS cohort and that the Thistle2-hsHD module mapped specifically to the Blue-mmSS module F2 mouse population(GSE60312 from GEO) that has been extensively phenotyped (328 stress- and sleep-related measurements). We unbiS module . FurtherS module .st principal component of the module) was significantly correlated with any of the 328 sleep and stress traits measured in the (B6xA/J)F2 population[To determine if the Blue-mmSS module was relevant to sleep and stress traits, we investigated if its module eigengene . Previous work also demonstrated that this CAG length-dependent network (“M11”) is conserved in BACHD-ΔN17, R6/2, and HdhQ150 mice, suggesting this module is robust across mouse models of HD. Our analysis shows that a bottom-up (CAG length-dependence) and top-down (phenotype) approach to studying early phases of HD pathogenesis converge onto the Thistle2-hsHD astrocyte network.These data suggest that Thistle2-hsHD may be relevant to early HD pathogenesis because of its associations with non-motor phenotypes. In order to corroborate this evidence, we investigated if Thistle2-hsHD gene expression is dependent upon CAG length. CAG repeat length is inversely correlated with HD onset age, but weacis-eQTLs) in order to construct a Bayesian network (with cis-eQTLs priors) of all gene-gene relationships[Nucb1 encodes a protein that inhibits amyloid fibril formation often observed in HD[Gpx8 encodes a glutathione peroxidase, an enzyme recently shown to be neuroprotective in HD animal models[To identify the candidate causal regulators (CCRs) of the Blue-mmSS module, we used an integrative genomics approach that leveraged both genetic and gene expression data from 100 (B6xA/J)F2 mouse samples to calculate causal probabilistic relationships between genes ,33–36. Ived in HD, and Gpxal models.Tcn2, Ace, Spint2, and Ltbp3) are differentially expressed in the human HD cohort (GSE3790). Ltbp3 is especially interesting since the LTBP family regulates the localization, activation, and bioavailability TGFβ[P = 0.8), while their downstream nodes are (P = 1.6 x 10−3). This relationship is further supported when examining the members of Blue-mmSS that are also coexpressed in Thistle2-hsHD. Only one Blue-mmSS CCR, Tspan33, is a member of the human HD Thistle2 module, while the downstream network is overrepresented for the Thistle2-hsHD module (P = 2.9 x 10−6), and more specifically for Thistle2-hsHD genes with FOXO3 binding sites (P = 2.7 x 10−7)  . As a whP = 5.7 x 10−8) and abnormal emotional and affective behavior (P = 5.3 x 10−4), suggesting that the CCRs strongly interact with proteins that are necessary for motor and non-motor traits relevant to HD (P = 2.7 x 10−15). We confirmed this finding with a second independent dataset that identified in vivo HTT-complexed proteins from a mouse model of HD by affinity purification-mass spectrometry[P = 6.4 x 10−7). Overall, these results provide strong orthogonal evidence that the Blue-mmSS CCRs are relevant for both motor and non-motor phenotypes associated with HD and associated with HTT proteotoxicity.To further support the claim that CCRs of the sleep and stress mouse network are relevant to HD, we investigated their protein interaction network (PIN). We constructed their PIN by querying first-degree protein-protein interactions previously identified through experimental and computational methods and considered only interactions between proteins that are expressed in the caudate . We founnt to HD . We thenctrometry . Molecules that significantly downregulate these CCRs include drugs which ameliorate motor symptoms, depression, and anxiety. On the other hand, molecules that upregulate these candidate causal nodes include drugs which can induce dystonia, motor restlessness, extrapyramidal symptoms, jitteriness, and insomnia (P = 0.0097) and trihexyphenidyl (P = 0.0027), treatments for depression/anxiety and Parkinson’s disease, respectively. On the other hand, these CCRs are upregulated by cyclizine (P = 0.0166), which has been reported to induce chorea[P = 0.0203), which can induce or exacerbate a variety of neuropychiatric and sleep phenotypes[P = 0.0216), whose side effects include anxiety, insomnia, and decreased motor coordination. These results provide further orthogonal evidence associating these CCRs with the emergence of sleep, stress, and motor symptoms. All scores and statistics reflecting compound-causal regulator associations are provided in To examine if modulating CCRs is associated with motor and non-motor phenotypes, we determined which bioactive small molecules coherently affect our CCRs using transcriptional profiles in Connectivity Map. We reasoned that molecules affecting psychiatric or neurological traits may work in part by modulating candidate causal nodes in the Blue-mmSS subnetwork. This analysis revealed a number of molecules that concordantly up- or down-regulate these CCRs  and Blue-mmSS (P = 6.5 x 10−5). We note that the DBS signature exclusively affects downstream nodes of the Blue-mmSS subnetwork, suggesting the potential therapeutic benefits of upstream modulation. Slc4a2 is directly modulated by DBS and is immediately downstream of Ltbp3, discussed above as a CCR of our network and an important modulator of TGFβ signaling. While Slc4a2 knockout causes motor deficits[Slc4a2 and TGFβ pathway has been proposed in other systems[Deep brain stimulation (DBS) is used as a therapeutic tool in several neuropsychiatric disorders, and recent reports suggest that DBS targeting the globus pallidus internus and the subthalamic nucleus (STN) may ameliorate motor and non-motor symptoms in HD patients,20. Ther deficits, its upr deficits. Though r systems, our netOur work provides a systems-based approach for identifying and prioritizing networks and pathways relevant to HD, and our analysis has revealed many novel insights into the molecular mechanisms underlying HD pathogenesis. Previous systems analyses have identified gene networks in post-mortem HD brain correlated with pathological grade, while oMost functional and molecular work on HD has focused on medium spiny neurons, but our analyses instead suggest a primary role for astrocytes in HD striatal pathology. Our results also suggest that the HD-relevant astrocyte network is insufficiently explained by astrocytosis, since module enrichment for astrocyte-specific genes was not correlated with pathological grade. Given this evidence, we argue that this astrocyte network is critical to HD pathogenesis, a conclusion also supported by data from the prefrontal cortex in multiple HD cohorts,52. Othedaf-16) in a Caenorhabditis elegans model of early mutant htt toxicity in which expanded polyQs cause a defective touch response but minimal cell death[C. elegans evidence for the protective effects of FOXO3a[We integrated our network analysis with experimental knockdown data and highlighted that this astrocyte network is downstream of TGFβ-FOXO3 signaling. Previous work has studied the role of the FOXO homologue  and a second network (Turquoise-mmSS) includes Rrm2b and Ncald among its most connected genes (hubs), two genes near a locus associated with earlier clinical onset of HD[We mapped this astrocyte network to independently derived striatal networks from chronically stressed (B6xA/J)F2 mice and showed that its expression correlated with stress and sleep phenotypes. Previous studies have demonstrated that astrocytes can modulate cortical slow oscillation, sleep amount, and sleep drive–81 and cset of HD. In sum,Further network analyses revealed that sleep and stress CCRs control the expression of the HD-relevant nodes and are affected by HTT-toxicity. The organization within this shared network reflects the emergence of stress- and sleep-related phenotypes in the early phases of HD and the eventual manifestation of significant motor symptoms later in the disease. It also suggests upstream modulation of this sleep and stress subnetwork may be therapeutically efficacious by delaying the progression of HD. These results support the notion that psychiatric symptoms are a primary clinical presentation of HD and suggest that targeting the molecular mechanisms of non-motor symptoms may significantly improve both psychiatric and motor function in HD patients, as has been done with antipsychotics like risperidone.in silico and prioritizing future in vivo experiments.Notably, we show that this network shared between sleep, stress, and HD is affected by deep brain stimulation of the subthalamic nucleus, a medical intervention that ameliorates both the motor and non-motor symptoms in some HD patients. FunctioIn these analyses, we have shown that sleep, stress, and HD share an astrocyte network affected by signaling pathways of adult neurogenesis, and our systems-based approach provides a framework for considering psychiatric symptoms as the primary manifestation of neurodegenerative disease. It is important to note that not all HD patients experience the same non-motor symptoms, but this astrocyte network is a striatal pathway through which HD can impinge upon sleep and stress traits in genetically or environmentally susceptible individuals. By integrating molecular networks from comorbid psychiatric and neurological conditions, we can identify novel points for therapeutic intervention, prioritize mechanisms of neurodegenerative pathogenesis, and perhaps even capture molecular dynamics of disease progression, which are difficult to measure directly in human brain.in vivo investigation of Thistle2-Blue network is essential for completely understanding the functional and morphological consequences of each CCR and the best points of therapeutic intervention. This may include an expanded investigation of transcription factor binding and upstream pathway regulation. We validated FOXO3, but 35 other transcription factors also are predicted to control the Thistle2 module, including several that are differentially expressed in the HD caudate. Some predicted transcription factors, like HOXA5 and FOXF2, have also been implicated in HD pathogenesis in the prefrontal cortex[Though this work makes significant steps towards systematically studying the common molecular basis of motor and non-motor features of HD, we acknowledge that our analysis focuses on only one of many points of intersection worthy of further exploration. We also acknowledge that further al cortex. Lastly,al cortex,87–89. THodges et al. (2006) studied differential gene expression across three brain regions in a cohort of 44 HD-gene-positive cases and 36 age- and sex-matched controls using Affymetrix microarray HG-U133A and U133B. The samst principal component to pathological grade with a Kruskal-Wallis test, and module-grade associations were expressed as its –log10(P). Fisher’s exact test was used to assess module enrichment for cell-type specific and HD differential expression signatures, and Bonferroni corrected p-values are reported for all Fisher’s exact tests. We used cell-type specific signatures derived from FACS and in situ hybridization experiments, and we tested module enrichment for HD differential expression profiles derived from this cohort (GSE3790) and a second cohort (GSE26927) in order to ensure robustness and reproducibility. Module conservation was tested for all HD modules with greater than 50 module members. Fischer’s exact test was used to determine module conservation between the HD cohort and the (B6xA/J)F2 cohort, for which a module was considered conserved with Bonferroni corrected p-value < 0.05  and Odds Ratio > 2. Module conservation was similarly assessed between the HD cohort and the allelic series knock-in cohort. When necessary, homology information provided by Ensembl was used for human-mouse conversion. In the case of many-to-many homology, all possible pairs of human and mouse genes were kept.We generated coexpression modules from the corrected expression data in the HD cohort (GSE3790), using weighted gene coexpression analysis. We idenP < 2.2 x 10−16)  . In genex 10−16) , suggestP < 0.05, FDR < 0.05).In a previous study characterizing mouse striatal gene networks underlying multiple stress and sleep phenotypes, we described in detail a set of phenotypic, genetic and RNA-Seq gene expression data collected in a chronically stressed population of 338 male (B6×A/J) F2 mice. BrieflyAs previously described, we calcAs described in our previous work, we recoin vivo experimentation[Bayesian networks do not necessarily contain causal information, since many graphical models are Markov equivalent and causally indistinguishable. By integrating genetic data in the form of expression quantitative trait loci (eQTLs), we can improve network reconstruction, as has been validated by simulation and in vmentation,35, and We identified causal regulators in our directed acyclic graph by calculating the size of the h-layer neighborhood (HLN) downstream of each gene in our network. Nodes wp < 1 x 10−10. We ranked all transcription factors by their Z score and Fisher score, as well as by their differential expression profile in the HD cohort, to identify a prioritized list of significant transcription factors.To predict transcript factor binding, we used the oPOSSUM database. Using tTo identify relevant signaling pathways upstream of the Thistle2 module and its predicted TFs, we utilized 215 heterogeneous microarray experiments curated by the Signaling Pathway Enrichment using Experimental Data Sets (SPEED) database. This methodology identifies elements of the transcriptome consistently regulated by pathway perturbations across many experiments. EnrichmWe constructed a PIN by integrating 13 different sources that describe known and predicted protein-protein interactions, which was compiled by HDNetDB (hdnetdb.sysbiolab.eu). These databases include data from yeast two-hybrid screens , literature curation , computational text mining (COCIT), and orthology-based predictions . The overwhelming majority (~90%) of edges in our constructed PIN are derived from databases curating experimentally validated protein-protein interactions, and more than half of all edges were derived from either REACTOME or HumanP < 0.05 (Benjamini-Hochberg corrected) were considered significant.CCRs of the Blue-mmSS module were used to query Connectivity Map, a large library of drug induced transcriptional profiles. We mergS1 FigZ < 2 (blue line) suggest there is weak evidence for module robustness, while Z > 10 (green line) denotes strong evidence for their reproducibility. Evidence is considered moderate when 10>Z>2. All modules identified in (A) caudate, (B) cerebellum, and , suggesting they are robust, reproducible, and high quality.(TIF)Click here for additional data file.S2 FigP = 1.08 x 10−25), (B) cerebellum , and (C) frontal cortex .Assessing the reproducibility of the MDC statistic by comparing it to an independent methodology for assessing differential connectivity (medianRank statistic) in the (A) caudate Click here for additional data file.S3 FigCorrelating each module’s association to pathological grade with (A) its enrichment for astrocyte gene signatures and (B) its percentage of astrocyte genes. Pathological grade is not associated to the astrocyte transcriptional profile of modules, suggesting module enrichment is not an artifact of astrocytosis.(TIF)Click here for additional data file.S4 FigP < 0.05, two-sided, Bonferroni corrected). (C) Grey60 was also associated to HD status in the caudate . (d) Grey60 from our replication cohort (GSE26297) most significantly overlapped with our Thistle2 module from our original cohort (GSE3790). These results show that the caudate astrocyte network is coexpressed in an independent cohort and is most relevant to HD.(A) Weighted gene coexpression analysis was performed in an independent validation cohort (N = 62) (GSE26297). In this cohort, multiple brain regions were collected from multiple neuropsychiatric diseases, including the caudate from HD patients. (B) Enrichment for HD-relevant differential expression signatures revealed that Grey60 was most relevant to HD ((TIF)Click here for additional data file.S5 FigP = 0.01, FDR = 0.01, Fold enrichment = 2.(A) Enrichment of pathway perturbation signatures  with Thi(TIF)Click here for additional data file.S6 FigP < 2.2 x 10−16). (c) Comparing median expression of genes in each module. Left dotted red line represents the top decile, and the right dotted red line represents the bottom decile. The grey module falls within the bottom decile, as expected, while Thistle2 falls within the top decile. Comparison of median expression between genes that cluster in modules and those that do not (grey) (***: Kolmogorov-Smirnov (TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableModule ranks are based upon differential connectivity and differential gene expression enrichment, denoted by a "1" in columns 2 through 6.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 TableThis table includes module membership and connectivity statistics for modules calculated from a single gene expression matrix that includes all caudate cases and controls. It also includes module-status association P values, as well module enrichment P values for astrocyte signatures and the original Thistle2 module. All reported P values are Bonferroni corrected.(XLSX)Click here for additional data file.


Transcription factor binding sites compete for a limited pool of bioavailable transcription factor molecules to fine tune gene expression. The regulation of gene expression is an intriguing and complex topic that has kept scientists busy for decades. Mechanisms that control the transcriptional and translational output of a gene are manifold, but, despite a tremendous amount of progress, the quantitative consequences of gene regulation in response to qualitative changes in the molecular milieu that govern expression are incompletely understood.in silico with thermodynamic models, and quantitative measurements have validated that such mathematical models predicted the responses of the circuit under investigation with remarkable accuracy. Thermodynamic models usually operate under the assumption that a transcription factor outnumbers the transcription factor binding sites in the genetic circuit being assessed. While this may often be the case, contexts in which the transcription factor becomes limiting certainly exist.Simple artificial genetic circuits have helped to interrogate the intricate interplay between the control and output of gene expression. Genetic circuits have been recreated Cell, Rob Phillips and his team present a thermodynamic model to tackle this problem, which they termed the transcription factor titration effect . Co. Co4]. C protein .et al. when we investigated the competition of RNA transcripts for miRNA binding: competition between RNA transcripts was greatest when the various components of the system were present at near-equimolar abundance [Finally, we and others have shown competitive RNA-RNA interactions whereby RNA transcripts can compete for a limiting pool of shared microRNAs (miRNAs), a phenomenon that has been demonstrated in plants and mammalian cells, and during virus-host interactions . Interesbundance . These oet al., transcription factor binding sites that are ectopically introduced into cells are able to decoy transcription factors from endogenous genes. Overexpression of complementary DNAs (cDNAs) is one of the most commonly employed techniques to study the function of our favorite genes in cells from unicellular species to mammals. Regardless of whether expression plasmids or viral constructs are used for such experiments, the ectopic introduction of a foreign promoter not only leads to overexpression of the cDNA of interest, it may also result in underexpression of genes from which transcription factors are titrated away. Thus, the results of overexpression experiments need to be interpreted with a grain of salt.The transcription factor titration effect has important implications in several contexts. As shown by Brewster A cellular state where the number of transcription factor binding sites is altered is the cell cycle. Specifically, a cell in the G2 phase of the cell cycle has twice as many promoters as when it was in the G1 phase, and high-affinity transcription factor binding sites should be able to outcompete low-affinity binding sites even more efficiently, especially if the abundance of the transcription factor remains constant during the cell cycle. This may be a mechanism to fine-tune gene expression during mitosis, and warrants further investigations. With the advent of genome editing techniques such as zinc finger nucleases and the CRISPR system, the importance of transcription factor titration in physiology and disease can now be systematically interrogated.et al. relates to gene copy number changes that frequently occur in cancers. Genomic amplification or aneuploidy may increase the number of promoters that compete for certain transcription factors by several fold. This, in turn, may drastically alter the transcriptional output of such sequestered transcription factors, especially if the sequestering transcription factor binding site possesses high affinity towards the transcription factor, and promote the development or progression of the disease. One could also imagine that mutations that affect the affinity of transcription factor binding sites alter the transcriptional profile of a cell and, therefore, contribute to cancer development, especially in cases where these binding sites are utilized by multiple transcription factor family members.Another interesting aspect of the work by Brewster It has been demonstrated that altered ceRNA crosstalk between RNA transcripts may promote cancer development. Such alterations may be due to differential 3′ UTR utilization, translocations, differential gene expression and genomic amplification. Genomic amplification represents an interesting scenario in which the amplified gene on the one hand competes for transcription factors, and on the other hand generates more mRNA molecules that will affect the balance of ceRNA networks. In addition, mRNAs encoding transcription factors can also participate in ceRNA crosstalk Figure . Indeed,et al. has provided an exciting new aspect to the field of target competition and brought us one step closer to a qualitative and quantitative understanding of gene regulation.It is becoming increasingly clear that target competition affects multiple steps that control the generation of proteins from a DNA blueprint, and that the various molecules involved in target competition form intricate networks that allow for delicate fine-tuning of gene expression. We anticipate a plethora of novel competitive interactions between DNA, RNA and proteins to be discovered and look forward to their functional characterization. The study by Brewster cDNA: complementary DNA; ceRNA: competitive endogenous RNA; miRNA: microRNA; RBP: RNA-binding protein.The authors declare that they have no competing interests.


We use two examples to demonstrate how users could use functions of ePIANNO webserver to explore useful information about TF related genomic variants. Users could use our query functions to search target regions, transcription factors, or annotations. ePIANNO may help users to generate hypothesis or explore potential biological functions for their studies.Recently, with the development of next generation sequencing (NGS), the combination of chromatin immunoprecipitation (ChIP) and NGS, namely ChIP-seq, has become a powerful technique to capture potential genomic binding sites of regulatory factors, histone modifications and chromatin accessible regions. For most researchers, additional information including genomic variations on the TF binding site, allele frequency of variation between different populations, variation associated disease, and other neighbour TF binding sites are essential to generate a proper hypothesis or a meaningful conclusion. Many ChIP-seq datasets had been deposited on the public domain to help researchers make new discoveries. However, researches are often intimidated by the complexity of data structure and largeness of data volume. Such information would be more useful if they could be combined or downloaded with ChIP-seq data. To meet such demands, we built a webtool: ePIgenomic ANNOtation tool ( Transcription factors (TFs) and chromatin regulators (CRs) play key roles in eukaryotic gene transcriptional processes, 2. MappCurrently, many ChIP-seq web servers and databases have been developed to provide solutions for diverse biological issues. For examples, CR Cistrome is a ChIP-seq database for chromatin regulators and histone modification linkages in human and mouse  and ChIPIn this work, we considered two important databases that have been frequently used in the genetic research. The first one was the 1000 Genomes Project which provided an overview of human genomic variation, mainly single-nucleotide polymorphisms (SNPs) and small InDels , 22. A tePIANNO (http://epianno.stat.sinica.edu.tw/index.html). ePIANNO contained over 1000 samples from over 3000 ChIP-seq experiments of human. ePIANNO a had a user-friendly website interface allowing researchers to explore, navigate, and extract data quickly. It provided two query functions including official gene symbol and local region, respectively. User could query and extract peak data across samples and experiments in small regions they interest. Results of each query were annotated by 1000 Genomes Project phase 1 release, GWAS Catalog of NHGRI[ePIANNO may help researches to explore potential biological functions of the TFs in which they were interested, thereby helping them generate hypothesis for future studies.For most researchers, additional information including genomic variations on the TF binding site, allele frequency of variation between different populations, variation associated disease, and other neighbour TF binding sites are essential to generate a proper hypothesis or a meaningful conclusion. Such information would be more useful if they could be combined or downloaded with ChIP-seq data. To meet such demands, we build an integrative ChIP-seq webserver:  of NHGRI, and ann of NHGRI. ePIANNOePIANNO was constructed by combining the ChIP-seq data part and the annotation part. In the ChIP-seq data part, data came from three sources. The first one was hmChIP database containing 392 ChIP-seq experiments of human including 129 chromatin modification (CM) and 263 transcription factor binding site (TFBS) experiments (http://jilab.biostat.jhsph.edu/database/cgi-bin/download.pl) [http://genome.ucsc.edu/ENCODE/downloads.html). The third one was Roadmap Epigenomics Project containing 1,069 CM experiments (http://genboree.org/EdaccData/Release-9/). The peak calling step had been conducted by hmChIP, ENCODE, and ROADMAP Epigenomics, respectively. We only curate the peak files  of ChIP-seq datasets from three projects and provide original sources. In hmChip, datasets were collected from GEO database in BED file format contained chromosome, start, and end positions. However, they stop updating in 2011 and the genome mapping version was hg18. As a consequence, we remapped the BED file provided from hmChip to hg19. In ENCODE, four peak callers, SPP, GEM, PeakSeq, and MACS, were integrated in their uniform peak calling pipeline to generate final peak file. In ROADMAP epigenomics, Pash 3.0 read mapper was used to make reads mapping first. For the histone ChIP-seq data, the MACSv2.0.10 peak caller was used to identify peak region (https://github.com/taoliu/MACS/). For DNase-seq data, the Hotspot algorithm was used to identify fixed-size (150bp) DNase hypersensitive sites and MACSv2.0.10 was used to call peaks using the same settings as the histone mark. Fragment lengths for each dataset were pre-estimated using strand cross-correlation analysis and the SPP peak caller package  and these fragment length estimates were explicitly used as parameters in the MACS2 program (—shift-size = fragment_length/2). Totally, ePIANNO contained potential 253,000,853 peaks in various cell types or experiments. Genomic variant data from 1000 Genomes Project [load.pl) . The sec Project , GWAS Ca Project , and gen Project  were incePIANNO, 2,824,294 (1.11%) potential DNA binding sites had disease-associated SNPs annotated by the GWAS Catalog of NHGRI. Among all disease-associated SNPs annotated by the GWAS Catalog of NHGRI, 7,797 (98.91%) SNPs were found in the potential protein-DNA binding regions  were covered by Chip-seq experiments based on hg19 genome size . This result showed that although the overall lengths of all peaks in ePIANNO was 206 billion bps, most of them (98%) were overlapped with each other. A hypergeometric test showed that disease-associated SNPs were enriched in the ChIP-seq covering regions  or by coordinate (ex. chr7:10000~10000000). When users search specific peaks or genes, both forward and reverse streams of genomic regions will be reported and results will be shown on the browser. ePIANNO also provided one-click download function for user to retrieve results in EXCEL CSV format.Currently, ePIANNO is the search of TFs. If user wants to know potential TFs that regulate a gene of interest, ePIANNO will search TFs whose DNA-binding locations were around the gene of interest based on peak information of ChIP-seq experiments. The first step is to input the specific gene in the query box  of interest  in mind and want to know the annotations of neighbour regions of the peak. To meet this need, the third function of interest , the num browser . Users c browser  and the  browser . Populat browser .In a protein-DNA binding event, both protein and DNA were involved. Alternations of protein and DNA may both affect the binding event. As a consequence, if a genomic variant was found to be associated with some diseases, alternations of the correspondent DNA-binding protein may also be linked to similar diseases. In the following case, we demonstrate that a DNA-binding protein which targeted a disease-associated genomic variant was also a potential disease-associated protein.CHRNA3, rs1051730, was reported to be associated with lung cancer and lung adenocarcinoma in two independent GWASs, respectively [ePIANNO to explore any potential transcription factor may bind on it. First, we just put “chr15:78894339” in the query box and select “Transcription factor or DNA-binding protein” as the ChIP type to conduct the search , including Crohn's disease and ulcerative colitis . We use uery box , select uery box , and seauery box . We founuery box . Among tePIANNO and search “chr1:8021973” for 20 nearby genes both upstream and downstream  which located 17K upstream of rs35675666. In 2012, this gene was reported as one candidate gene associated with IBD [TNFRSF9 and HNF4A were associated with IBD [Alternations of the TF binding region may cause the expression dysregulation of the downstream gene. If a genomic variant occurred in the TF binding region was found to be associated with some diseases, the expression dysregulation of the downstream gene may also be associated with such diseases. As a demonstration, we used the “peak Annotation” function in the wnstream . We foun TNFRSF9 . DDX11L1with IBD . In 2014with IBD .ePIANNO allowed users to upload their own peak data in TXT format or paste their peak regions in the query region and help them to add annotation. For example, . As a consequence, ePIANNO was built to fill this gap.The main goal of ePIANNO allowed users to upload their own peak data in TXT format or paste their peak regions in the query region and help them to add annotation.Furthermore, ePIANNO integrated the experiment results of ChIP-seq , SNP information of populations (1000 Genomes Project), disease association information of GWAS (NHGRI), and gene annotation (ENCODE annotation). Users could not only search the relationship between TFs and target genes, but also explore the relevant genome variants and diseases. If users have their own genomic regions of interest, ePIANNO provides batch query methods, currently not provided by UCSC Genome Browser, (separated by semi-colon or another line) to explore genes and SNPs located in upstream or downstream with ENCODE gene annotations.SNPs represent the most common type of genomic variation. SNPs in the coding region are most intensively studied since they are relatively easy interpretable with well-annotated protein-coding sequences of human genome. Some SNPs localized to the binding sites of various transcription factors may alter the regulation of gene transcription and exert functional effects. ePIANNO webserver to explore useful information about TF related genomic variants. The example of SNP rs1051730 showed that DNA-binding proteins targeting a disease-associated genomic variant were also likely to be disease-associated genes. The example of SNP rs35675666 illustrated how ePIANNO helped establish the connection between the genomic variant of upstream TF-binding region, the associated disease and the TF expression. We believe that if users have their own disease-associated SNPs, may be from their own study, ePIANNO would be helpful to find out the potential relationship between SNPs and disease by annotating their roles at the epigenomic level.GWAS Catalog of NHGRI is the most comprehensive disease-SNP association database of the public domain. The 1000 Genomes Project contains most comprehensive genomic variants of populations worldwide. Genomic variants may contribute to binding affinity or other properties of protein-DNA interaction. In addition, some genomic variants are associated with known diseases. We demonstrated how users could use functions of ePIANNO with most update ChIP-seq datasets, the GWASs Catalog dataset, and annotations for users to query and annotate regions of their interest.Previous studies had used ChIP-seq data of ENCODE, GWASs Catalogs of NHGRI, and 1000 Genomes Project dataset or HAPMAP III dataset to investigate the relationships between SNPs, disease, and protein-DNA binding effects–20. ResuePIANNO might help user to find out such case especially when they have their own protein-DNA binding regions of their own interest.Genomic variants in the protein-DNA binding region might directly affect the binding strength of TFs or mode of chromatin modification at the transcription level. In addition, effects of the genomic variants might further reach the post-transcription level if the expressions of miRNA or lncRNA are affected. Although we did not give such complicate case of indirect effects of genomic variants in our examples, ePIANNO, data of 1000 Genomes Project came from historical healthy subjects. However, data of NHGRI came mostly from various diseases and ChIP-seq data were generated from cells lines with various experimental conditions. As a consequence, combining data of annotations and ChIP-seq had its own limitations. Although ePIANNO helped users to explore potential associations between disease, protein-binding information, and their own regions of interest, these associations may not broadly applied in all cell types or in all experimental conditions. Results had to be evaluated by users’ aims and hypothesis of their studies. For an example, if lung cancer was considered in NHGRI database, cell line of ChIP-seq experiments was better to be lung cell lines. It may provide a more reasonable association between a SNP and a protein-binding site.In S1 Text(TXT)Click here for additional data file.


Pu.1. This promoter is located in an ORR1A0 long terminal repeat (LTR) retrotransposon intronic to Pu.1 and is regulated by the Krüppel-like factors KLF1 and KLF3. Expression of the resultant chimeric transcript, called Pu.2, spontaneously induces erythroid differentiation in vitro. These experiments illustrate how transcription factor binding sites spread by retrotransposition have the potential to impact networks encoding key biological processes in the host genome.Transposable elements (TEs) are a prominent feature of most eukaryotic genomes. Despite rapidly accumulating evidence for the role of TE-driven insertional mutagenesis and structural variation in genome evolution, few clear examples of individual TEs impacting biology via perturbed gene regulation are available. A recent report describes the discovery of an alternative promoter for the murine erythroid transcription factor  Transposable elements (TEs) have in the past been maligned as ‘junk’ , ‘selfisAs a TE proliferates in its host genome, the number of loci subject to regulation by DNA binding proteins specific to that TE, such as transcription factors, also increases. During evolution, TEs have often contributed transcription factor binding sites to promoter, enhancer, and boundary elements -14. In set al.[Pu.1, regulated in turn by Krüppel-like factors 1 (KLF1) and 3 (KLF3). PU.1, KLF1, and KLF3 are transcription factors active during hematopoiesis, where PU.1 favors myeloid differentiation and KLF1 and KLF3 drive erythroid maturation [Despite extensive evidence for gene regulatory and structural innovation produced by TEs, examples of phenotypic change due to this variation are comparatively limited in mammals. TE-derived alternative promoters, which generate a chimeric mRNA with an adjacent gene, are arguably one of the more straightforward scenarios to link a TE with a functional product, particularly when that gene encodes a protein of known function. In recent work, Mak et al. report tturation ,27. In wturation .Klf3-/- knockout mice, Mak et al. first identified major de-repression of Pu.1 that, oddly, excluded the initial two exons of the gene. To resolve this discrepancy, the authors performed 5′ RACE primed from the third exon of Pu.1. The results of this approach indicated an alternative promoter in an ORR1A0 LTR [Pu.1  and luciferase reporter experiments. Taken together, these data suggest opposing roles for KLF1 and KLF3 in regulating Pu.2 expression.Analyzing a microarray screen of R1A0 LTR  located Pu.2 was predicted to encode an N-terminal truncated isoform of PU.1 that retained a DNA binding ETS domain  to elucidate KLF bound sites in vivo may reveal human TE families dynamically regulated hematopoiesis, in the Pu.1 locus and elsewhere.Finally, via RNA-seq and additional qRT-PCR, Mak lsewhere . One realization . As suchAgouti gene in rodents [et al. that the LTR-initiated Pu.2 transcript can promote erythroid maturation in the absence of KLF3 is a valuable addition to this literature, and will likely increase future attention to the role of TEs in regulating various developmental processes, including hematopoiesis.Among a host of alternative promoters derived from mammalian TEs and driving protein-coding and non-coding gene expression ,31-33, w rodents , an LTR  rodents , and the rodents . In eachBrca2: Breast cancer 2, early onset; ChIP-seq: Chromatin immunoprecipitation sequencing; CSF1R: Colony stimulating factor 1 receptor; EMSA: Electrophoretic mobility shift assay; Klf: Krüppel-like factor; LTR: Long terminal repeat; NAIP: Neuronal apoptosis inhibitory protein; qRT-PCR: quantitative real time PCR; RACE: Rapid amplification of cDNA ends; RNA-seq: RNA sequencing; TE: Transposable element.The authors declare that they have no competing interests.GJF wrote the manuscript. KRU provided edits and prepared Figure


The upstream stimulatory factors (USFs) are regulators of important cellular processes. Both USF1 and USF2 are supposed to have major roles in metabolism, tissue protection and tumor development. However, the knowledge about the mechanisms that control the function of USFs, in particular in tissue protection and cancer, is limited. Phosphorylation is a versatile tool to regulate protein functions. Thereby, phosphorylation can positively or negatively affect different aspects of transcription factor function including protein stability, protein–protein interaction, cellular localization, or DNA binding. The present review aims to summarize the current knowledge about the regulation of USFs by direct phosphorylation and the consequences for USF functions in tissue protection and cancer. All cells of an organism carry the same genetic information in their nucleic DNA. But the requirements of distinct cell types are different and they also can change upon environmental stimuli. Thus, the expression of genes into proteins has to be adjusted to the particular needs of a cell. This mainly occurs by transcriptional regulation of gene expression, and transcription factors play a pivotal role in this regard. Deregulated transcription factors are involved in the development of many severe diseases, and thus, elucidation of transcription factor regulation can provide valuable information regarding the development and treatment of these diseases.Transcription factors are proteins that regulate gene expression by binding to the DNA sequences of their target genes and that can be classified according to their DNA-binding domain (DBD). Among the most important ones are homeodomain proteins, zinc-finger proteins, basic helix-loop-helix (bHLH) and leucine zipper (LZ) proteins. Often specific transcription factors contain ligand binding domains as well as one or more transactivating domains (TADs) permitting their ability to act as activators or repressors of gene expression.With respect to the substantial role of transcription factors in gene expression regulation, it is not surprising that mutant transcription factors as well as deregulated transcription factors can cause tissue damage and thus severe diseases. In cancer, the inappropriate growth of cells is often caused by mutated or malregulated oncogenes or tumor suppressor genes, which, in turn, often encode transcription factors. Prominent examples are the oncogenic transcription factor c-Myc  or the tThe eukaryotic cell features several different mechanisms for the regulation of transcription factor function. One possibility is to tightly control the rate of transcription factor synthesis and/or degradation via the ubiquitin–proteasome system . PosttraThe upstream stimulatory factors (USF1 and USF2) are transcription factors that participate in the regulation of a large number of genes and especially USF2 appears to be crucial for the control of embryonic development, brain function, metabolism, iron homeostasis, fertility and growth whereas USF1 has roles in metabolism, as well as in the tanning and immune response . FurtherUpstream stimulatory factors were identified in 1985 by their ability to regulate transcription of the adenovirus major late promoter . SawadogHelicobacter pylori infection caused hypermethylation of the USF2 promoter in mice  transcription factor family  which alLike the other members of the b-HLH transcription factor family, USFs bind in principle to E-boxes with a CANNTG core sequence in the promoters of their target genes . Whole gGiven that USFs were identified as transcriptional regulators of the adenovirus major late promoter, it is not surprising that other viruses like HIV , VaricelκB signaling by inducing expression of the gene tumor necrosis factor alpha (TNFα)-induced protein-3  affect expression of the cystic fibrosis transmembrane conductance regulator gene (CFTR) with YY1 being a repressor and USF2 being an activator. C/EBPβ appears to act as a switch; its phosphorylation favors the interaction between USF2 and YY1 thus blocking the inhibitory activity of the latter, in favor of C/EBPβ transactivation . OverallSo far, not much is known about the mechanisms contributing to the regulation of USFs in cancer and in general. Although phosphorylation appears to be a powerful and fast acting mechanism by which USFs function can be modulated, phosphorylation of USF proteins has not yet been investigated intensively and the number of studies showing direct phosphorylation of USF proteins by a kinase at certain residues is limited.The first indirect evidence suggesting that USF1 might be a phosphoprotein came from the finding where phosphatase treatment of a purified USF1-like factor significantly reduced its DNA binding activity . Additioin vivo, and that the phosphorylated form of USF1 bound preferentially to an E-Box in the C4 complement gene promoter (More direct evidence for USF1 phosphorylation came from a study showing that USF1 is a phosphoprotein promoter .Mitogen-activated protein kinases (MAPKs) are a large family of serine/threonine kinases converting various extracellular signals, among them growth factors, mitogens or inflammatory cytokines, into intracellular responses through serial phosphorylation cascades, thus regulating cellular processes like proliferation, differentiation, development, stress responses, and apoptosis. Conventional MAPKs include extracellular signal-regulated kinase (ERK), ERK1 and ERK2 (p44/p42), c-Jun NH2-terminal kinase (JNK1/2/3), p38 MAPK (p38α/β/γ/δ) and ERK5. In addition, some atypical MAPKs such as ERK3/ERK4, NLK (nemo-like kinase), and ERK7/ERK8 have been identified but less is known about their regulation, substrate specificity and physiological functions  treatment of the cells , growth factors , cytokines , TNFα, metabolic signals (glucose) and environmental signals hypoxia; .In this context USFs seem to be quite important in mediating the response of several signals to the PAI-1 promoter. USF1 was shown to stimulate PAI-1 expression when being induced by serum  and by wOverall, these studies indicate that USF1 can be phosphorylated via the ERK1/2 pathway while less is known about the role of ERK1/2 on USF2. In addition, cell-type specific components need to be considered and to be discovered to fully understand the role of phosphoUSF1 in PAI-1 expression and cancer.P38 and USFs. The p38 MAPKs were identified as modulators of TNF signaling  and USFs. PKC is a superfamily of serine/threonine kinases consisting of about 10 to 12 isoforms divided in three subfamilies based on their second messenger requirements  and USFs. The DNA-PK is a heterotrimer consisting of the DNA-dependent protein kinase catalytic subunit (PRKDC) and a dimer composed of the Ku p70 and Ku p86 (XRCC6-XRCC5) proteins. It acts as a serine/threonine-protein kinase that usually responds as a molecular sensor for DNA damage. It is involved in the non-homologous end joining (NHEJ) double-strand break (DSB) DNA repair. There, it usually associates with the DNA-bound Ku heterodimer, but it can also bind to and be activated by free DNA. Apart from phosphorylating histone H2A it was found to modulate transcription factors like c-Jun, p53, or c-Myc and contributes to the determination of the circadian period length by affecting Cry1 phosphorylation . As a consequence DNA-PK phosphorylates USF1 at Ser262 in the b-HLH-LZ domain. Phosphorylation of USF1 allows p300 associated factor (P/CAF) recruitment and subsequent acetylation of USF1 at Lys237. This acetylation event then enhanced the transcriptional activity of USF1 with respect to the FAS promoter .Recently it has become obvious that beside its metabolic functions in lipogenesis, FAS also seems to play an important role in carcinogenesis. It was observed that FAS expression was elevated in breast, prostate, colon, ovary, endometrium, and thyroid tumor tissues compared to normal tissue. FAS expression thereby seems to confer a growth and/or survival advantage and is associated with a poor prognosis , 2006. TProtein kinase A (PKA) and USFs. PKA, a cAMP-dependent protein kinase, is an ubiquitous serine/threonine kinase involved in a wide range of cellular processes such as transcription, metabolism, cell cycle, and apoptosis  and USFs. GSK3 is a serine/threonine kinase that was first identified as a negative regulator of glycogen synthesis; inhibition is achieved through phosphorylation of glycogen synthase  is considered to act as messenger-independent protein serine/threonine kinase. CK2 is a tetrameric enzyme consisting of two catalytic α and α′ subunits and two non-catalytic β-subunits. Interestingly, the majority of CK2 targets are proteins involved in signaling, protein synthesis and transcriptional regulation (in silico, in vitro and in vivo assays added USF1 but not USF2 to that list. Further, by using USF1 deletion mutants and point mutants that study identified threonine 100 as the major phosphorylation site for CK2. In addition, inhibition of CK2 with a specific inhibitor enhanced binding of USF1 to USF2, i.e., heterodimerization. Furthermore, transactivation studies showed that inhibition of CK2-dependent phosphorylation of USF1 stimulated transcription from the pancreatic glucokinase promoter as well as the FAS promoter but not from the HO-1 promoter (gulation . Over thgulation , HIF1α (gulation , upstreagulation  and FoxMgulation  and redugulation  and Chopgulation  just to promoter . Thus, tTranscription factors are critical components within signal transduction pathways and via regulating the expression of various genes they are involved in various aspects of cellular functions including regulation of cell growth and cell death. Therefore, disturbances within the proper function of transcription factors may be related to tumorigenesis and cancer. Phosphorylation events are fundamental processes achieved through the activity of various protein kinases which allow immediate control of protein activity including transcription factor function. Thus, the knowledge of the signaling pathways and the involved kinases are of immediate importance to understand the regulation and proper function of transcription factors. The transcription factors USF1 and USF2 have been shown to be subject of phosphorylation by different kinases; though most of the studies refer to USF1. In addition to their involvement in embryonic development, brain function, metabolism, iron homeostasis, fertility, pigmentation, and the immune response it became also evident that USFs affect tumorigenesis. Since kinase inhibitors became a feasible therapeutic approach in anti-tumor therapy within the last decade, they may be considered also to be of use in terms of the role of USFs in tumorigenesis and cancer. However, a concise view about the impact of USF’s phosphorylation during tumorigenesis has not been reached. The different phosphorylation events need to be considered in a more specific cellular context and in conjunction with USF interacting proteins. This may be of special importance since the phosphorylation events together with cell-specific USF interacting partner molecules may then render USFs either into a tumor suppressor or tumor promoter. Thus, more studies in particular considering interacting partners and cell-type specific effectors are necessary to further define the mode by which USFs affect tissue injury, inflammation, and tumorigenesis.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Drosophila group B proteins SoxNeuro and Dichaete are involved in central nervous system development, where they play both similar and unique roles in gene regulation. Sox genes show extensive functional redundancy across metazoans, but the molecular basis underpinning functional compensation mechanisms at the genomic level are currently unknown.Sox proteins encompass an evolutionarily conserved family of transcription factors with critical roles in animal development and stem cell biology. In common with vertebrates, the Using a combination of genome-wide binding analysis and gene expression profiling, we show that SoxNeuro directs embryonic neural development from the early specification of neuroblasts through to the terminal differentiation of neurons and glia. To address the issue of functional redundancy and compensation at a genomic level, we compare SoxNeuro and Dichaete binding, identifying common and independent binding events in wild-type conditions, as well as instances of compensation and loss of binding in mutant backgrounds.Drosophila group B1 proteins also play prominent roles during later aspects of neural morphogenesis. Our analysis of the functional relationship between SoxNeuro and Dichaete uncovers evidence for redundant and independent functions for each protein, along with unexpected examples of compensation and interdependency, thus providing new insights into the general issue of transcription factor functional redundancy.We find that early aspects of group B Sox functions in the central nervous system, such as stem cell maintenance and dorsoventral patterning, are highly conserved. However, in contrast to vertebrates, we find that  EF and SR wrote the paper. EF, BF and SR read and approved the final manuscript.SoxN mutants.Expression data, gene lists and Gene Ontology enrichments for genes differentially expressed in Click here for fileComparison of the different SoxN gene expression and binding datasets.Click here for fileGenomic coordinates, gene lists and GO:BP enrichments of the SoxN core dataset.Click here for fileGeneral features of SoxN binding.Click here for fileComparison of SoxN genome-wide binding with that of other TFs, chromatin-binding proteins and histone modifications.Click here for fileGenomic coordinates, gene lists, GO:BP enrichments for SoxN direct targets and tables of upregulated, downregulated and variably expressed direct targets.Click here for fileSoxN mutant embryos.Expression of SoxN direct targets in wild-type and Click here for fileGene lists and GO:BP enrichments for genes bound by SoxN and Sox2 or Sox11.Click here for fileDichaete and SoxN mutants.Genomic coordinates, gene lists and GO:BP enrichments of SoxN and Dichaete binding in wild type and Click here for fileDifferential enrichment of genes targeted by SoxN and Dichaete.Click here for fileSoxN and Dichaete mutants.Differential enrichment of genes associated with the five types of events observed in Click here for fileSoxN and Dichaete binding intervals targeting FlyLight enhancers with reported CNS expression.Click here for fileSupplementary Methods and Legends.Click here for file


Long-range chromatin interactions play an important role in transcription regulation. Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIA-PET) is an emerging technology that has unique advantages in chromatin interaction analysis, and thus provides insight into the study of transcription regulation.This article introduces the experimental protocol and data analysis process of ChIA-PET, as well as discusses some applications using this technology. It also unveils the direction of future studies based on this technology.Overall we show that ChIA-PET is the cornerstone to explore the three-dimensional (3D) chromatin structure, and certainly will lead the forthcoming wave of 3D genomics studies. Transcription regulation is a complex yet well-organized process in eukaryotes, in which chromatin interactions play a critical role and thus serve to regulate gene expression as well as further influence other cellular activities. Many technologies have been developed to study the binding of transcription factors (TF) for transcription regulation, such as chromatin immunoprecipitation (ChIP) microarray (ChIP-chip) , ChIP-PEde novo method. Compared with Hi-C . KEV and NAK were supported by the RSF grant 14-24-00123.The authors declare that they have no competing interests.GL conceived and coordinated the project. GL, LC, HC, PH, and QZ wrote this manuscript, with contribution from others.List of published ChIA-PET data.Click here for file


Among them, self-associating domains like so-called topologically associating domains (TADs) appear to be building blocks for large-scale genomic organization. This review describes features of these broadly-defined hierarchical structures, insights into the mechanisms underlying their formation, our current understanding of how interactions in the nuclear space are linked to gene regulation, and important future directions for the field.The manner by which eukaryotic genomes are packaged into nuclei while maintaining crucial nuclear functions remains one of the fundamental mysteries in biology. Over the last ten years, we have witnessed rapid advances in both microscopic and nucleic acid-based approaches to map genome architecture, and the application of these approaches to the dissection of higher-order chromosomal structures has yielded much new information. It is becoming increasingly clear, for example, that interphase  The human body consists of many trillions of cells harboring nearly identical genomes, and yet subsets of these cells are distinct both functionally and morphologically. It is widely accepted that “epigenetic” mechanisms are responsible for the differential regulation of shared genetic information, and thus for the generation of a diverse array of terminal cell types through zygotic development.e.g., CpG methylation and lysine tail acetylation), and several histone variants exist e.g., H2A.Z, CENPA), thus enabling an epigenetic diversity at even this most basic level of chromatin organization. The next level of compaction is commonly believed to be the organization of nucleosomes into a 10 nm “beads-on-string” chromatin fiber Saccharomyces cerevisiae are formed by heterogeneous clutches of nucleosomes that are interspersed with nucleosome-depleted regions, arguing against the existence of highly-ordered structures such as the 30 nm fiber The physical organization of eukaryotic chromosomes within a nucleus is crucially intertwined with the reading, interpretation, and propagation of genetic information by these epigenetic mechanisms. Metazoan cells package genomic DNA up to 2 m long into a tiny nuclear space ∼10 μm in diameter via hierarchy of organizational structures in vivo chromatin folding, we have gained important insights into the higher-order spatial organization of eukaryotic genomes, thanks to significant advances in DNA imaging technology and high-throughput biochemical techniques e.g., nuclear bodies), suggesting that nuclear activities are also spatially organized e.g., transcription), it is tempting to speculate that the modulation of chromatin organization itself is a basic mechanism by which cellular functions are enacted. However, crucial experiments, some of which are mentioned later in this review, are still required to elucidate whether these observed structural features play some general causal roles, or are simply a correlative of the cellular functions. Even still, we can be reasonably certain that features like chromatin loops, clustered highly-transcribed genomic loci, and large-scale chromosome domains are basic elements of chromatin folding, and as such are invaluable when analyzing genome organization in the context of developmental and environmental cues.Though we still know little of the dynamics of Here, we first review well-established and emerging technologies that are revolutionizing our understanding of higher-order genome architecture. We then discuss our current understanding of spatial genome organization in greater detail, providing insights into the mechanisms underlying structure formation, and the links between chromatin folding and gene regulation. Finally, we propose a handful of pressing questions we believe to be central to an ultimate understanding of the spatiotemporal organization and functions of nucleome.i.e., genome-editing) have then been used to ascertain the functional significance of specific architectural elements. Over the last ten years, the field has witnessed tremendous methodological advances in all three areas Three largely-orthogonal approaches are commonly used to study the structure and function of the three-dimensional (3D) genome. Microscopy-based DNA imaging techniques and high-throughput genomic mapping tools based on massively-parallel sequencing have been used to delineate higher-order genomic architecture, while genome perturbation tools  Before the advent of massively-parallel analyses by microarray and later high-throughput sequencing, our knowledge of 3D genome organization was largely derived from studies using fluorescence labeling-based light microscopy, such as DNA fluorescence e.g., core histone proteins) or the DNA itself must be labeled One commonly-cited limitation of light microscopic techniques, despite their versatility, is the resolution limit owing to the wavelength of light. To overcome this, several super-resolution fluorescence microscopy approaches, such as structured illumination microscopy (SIM), stimulated emission depletion (STED), and photoactivation localization microscopy/stochastic optical reconstruction microscopy (PALM/STORM), have been developed during the last decade  technology, and now typically paired with massively-parallel DNA sequencing Broadly, the current state-of-the-art for these biochemical techniques can be classified into three groups, based on the biological origin of the chromatin contacts being assayed ; Table 1Drosophila melanogasterThe second class of methods includes those that probe chromatin–RNA interactions, a hotly debated class of interactions that may eventually be used to define chromatin domains or sub-nuclear bodies. Currently, there are three different methods for identifying chromatin–RNA interactions: chromatin isolation by RNA purification (ChIRP) in situ Hi-C in situ DNase Hi-C Plasmodium falciparum) cis-elements, or pairs of binding sites for the transcription factor CTCF, are best carried out using the second group of approaches, which are each designed to map a specific set of chromatin interactions and thus allow for considerably-higher resolution for a given sequencing depth. Indeed, 4C, 5C, ChIA-PET, Capture-C, Capture-Hi-C, and targeted DNase Hi-C have all successfully been used to map specific regulatory interactions.The third group of techniques covers the chromosome conformation capture (3C) family of methods Crucially, data generated using 3C technologies may also be used to generate 3D predictions of genomic structure. The various computational approaches for tackling this problem have been reviewed in great detail elsewhere The biochemical methods discussed above are able to offer detailed molecular views of chromosome structure. However, these assays are all performed on many thousands to millions of cells per experiment, thus masking the variability inherent between individual cells. Single-cell versions of ChIP-seq A major goal in the field of chromatin biology concerns characterizing the functional significance of the genomic regions identified using the aforementioned techniques. Several genome editing tools are currently available, including the zinc-finger nucleases (ZFNs) e.g., NADs, LADs, and TADs), and chromatin loops. In this section, we discuss their respective biophysical characteristics, and links between these structural features.Microscopy-based and high-throughput biochemical studies have revealed common organizational structures in eukaryotic genomes, including CTs, chromatin, and nuclear compartments, various types of chromatin domains , individual CTs show preferences for nuclear positioning in mammalian cells, which may correlate with genomic properties , as well as with genomic functions  The non-randomness of genome organization in the nuclear space at chromosome level was observed more than a century ago. The Rabl configuration, with centromeres and telomeres at opposite poles of the nucleus, was proposed by Carl Rabl in 1885 e.g., transcription and DNA replication/repair) and constrained by nuclear context . Microscopic and molecular studies have identified several chromatin domains, with each representing some aspect of chromatin folding. Here we summarize the characteristics of the most commonly-discussed chromatin domains and review how they relate among each other.At any given time within a living cell’s interphase chromosomes, certain genomic loci may be embedded in a constitutive heterochromatin region, some may associate with the nuclear lamina, some may be attached to the nucleolus, and others may be embedded in the various sub-nuclear bodies, engaging in specific nuclear activities. One widely-held model for transcription postulates that active genes may co-localize into discrete “transcription factories”, where high local concentrations of RNA polymerase II (RNAPII) and basal transcriptional machinery enforce gene expression Hi-C studies have revealed that within CTs, chromosomes are partitioned into large compartments at the multi-Mb scale, containing either the active and open (A compartments) or inactive and closed chromatin (B compartments) With increases in resolution provided by a greater depth of sequencing, recent Hi-C and 5C studies have revealed that CTs and A/B compartments may be broken down further into smaller self-interacting domains, which have been identified in the genomes of a wide range of species from bacteria to human Drosophila polytene chromosomes revealed equivalence between polytene bands/inter-bands and TAD/TAD boundaries, suggesting that different types of TADs correspond to distinct packing states. For example, inactive TADs, which contain fully-condensed chromatin at the nuclear periphery, correspond to classical heterochromatin, whereas active TADs  and TAD boundaries (fully-extended chromatin fibers) correspond to classic euchromatin (less dense chromatin in the nuclear interior). Since these polytene bands are observed in single salivary gland cells, the correspondence of TADs to polytene bands also suggests that TADs are unlikely to be a statistical feature of population-level Hi-C experiments, but rather exist at the level of single cells. Recently, a super-resolution microscopy study on human and mouse cells using STORM revealed that nucleosomes are grouped into discrete clutches along the fiber, with areas of relative depletion between them While the formal definition for these self-interacting domains is quite broad, they all share common core properties. First, they are characterized by a greater frequency of within-domain interactions as compared to external interactions. This is in fact how TADs are identified in Hi-C data, through a measure of the directionality index (DI) of ligation pairs across a chromosome IDH1-mutant gliomas leads to differential CTCF binding, changes in genome topology, and consequent dysregulation of proto-oncogenes Though the definition of self-interacting domains has greatly helped our understanding of how chromatin might be organized in the nucleus, the functional relevance of these domains and the mechanisms underlying their formation remain poorly understood. To get at the function of particular domain boundaries, recent studies have employed genome editing to edit out or invert CTCF sites e.g., enhancers), and a variety of proteins involved in transcription initiation, elongation, and regulation One common model for transcription posits the existence of transcription factories—discrete nuclear foci in eukaryotic nuclei where transcription occurs e.g., regions of the inactive X chromosome (Xi), repressed olfactory receptor genes, tissue-specifically repressed RNAPII genes), and several RNAPIII-transcribed genes. NADs cover about 4% of the human genome and are significantly overlapped with LADs (discussed in further detail below), indicating that a certain amount of redistribution occurs between the nuclear lamina and nucleolar periphery after mitosis trans acting factors such as CTCF, chromatin assembly factor (CAF)-1, nucleolar proteins, and potentially lncRNAs The nucleolus is the largest subnuclear organelle in the nucleus of eukaryotic cells and is the prototype for transcription factories, as it serves as the primary site of rRNA biogenesis. In addition to its primary role as the site of rRNA transcription and maturation, the nucleolus also hosts several other biological processes, including viral replication, signal recognition particle biosynthesis, and sequestration of proteins  with the nuclear lamina (NL) are extremely gene poor, suggesting a structural role, whereas LADs with variable NL contacts tend to be cell-type specific LADs refer to the regions of the genome that interact with the nuclear lamina at the interior of the nuclear envelope. LADs were first characterized using the DamID technique, which has revealed that mammalian LADs are large, gene-poor domains spanning 40 kb–30 Mb and covering ∼40% of the genome Looping is an intrinsic property of chromatin fibers and serves as the basic mechanism of chromatin folding. In as early as 1878, Walther Flemming observed large chromosomal loops in the so-called lampbrush chromosomes of amphibian oocytes i.e., pairs of interacting loci), the question remains whether an element interacts with multiple other elements simultaneously within the same nuclear environment, or whether these interactions actually occur within different single cells. As discussed in further detail below, arriving at an answer to these questions may become possible through the further development of single-cell epigenomic technologies. Third, promoter–enhancer interactions generally show high cell type specificity and are correlated with cell type-specific transcription Recent genome-wide mapping of chromatin interactions has uncovered general features of this type of loop. First, ∼50% of active genes are engaged in long-range chromatin interactions in the cell types examined Building upon these findings, recent functional studies using gene editing tools have further suggested a causal link between chromatin looping and gene regulation. It has long remained unclear whether looped interactions are a prerequisite for or merely a consequence of gene regulation. Direct evidence has been obtained recently, demonstrating that chromatin looping between a gene promoter and a strong enhancer can lead to transcriptional activation e.g., CTCF, YY1, and NRSF), co-activators , chromatin structural proteins , and ncRNAs  functional dissection of this vast catalog of structural elements, and (ii) large-scale characterization of the dynamics and mechanisms of chromatin folding both across biological processes such as differentiation, and across homogenous and heterogeneous cell populations.e.g., CTCF binding sites), with respect to various phenotypes of interest . Already, several groups have successfully utilized Cas9-mediated genome editing to generate clonal populations harboring inverted or deleted transcription factor binding sites, and have performed assays like Hi-C and RNA-seq to link structural and functional changes The advent of CRISPR/Cas9 as an easy to use, highly-multiplexable system for perturbing primary sequence has opened up considerable avenues to testing the functional significance of genomic elements. We predict the continued use of genome editing reagents in validating key structural elements  approaches become more popular, we anticipate the eventual development of high-throughput screens for large-scale characterization of structural elements. Already, genome editing-based lentiviral and in vivo saturation mutagenesis screens have been employed, to dissect the functional significance of genes e.g., cancer) and dysregulation of chromatin architecture.As low-throughput , and for characterizing transitory states in biological processes such as differentiation. Recently-published approaches such as single-cell DamID Questions regarding the dynamics of chromatin—the processes by which chromatin architecture and state change as a function of a given biological process—remain largely unanswered. The nascent field of single-cell epigenomics in situ transcriptomic datasets In situ transcriptomics may, for example, be necessary to properly spatially organize large populations of tissue-derived nuclei in some biologically meaningful way. Furthermore, by matching in situ transcriptomic data with replicate single-cell epigenomic experiments in this way, one may be able to link differential genome architectural features with gene regulatory phenomena, thus furthering our progress toward ultimately understanding the links between 3D genome architecture and gene regulation.It may also be useful to consider the marriage of single-cell biochemical techniques with complimentary microscope-acquired Of course, the application and development of any of these techniques is intertwined with the development of data analytical techniques. While algorithmic development will have to keep pace with the development of these technologies, we believe that incredible strides already made in the relatively young field of single-cell RNA sequencing i.e., histone modifications and DNA methylation) impact higher-order chromatin structure? How might defects or differences in 3D genome architecture lead to human disease? Obtaining the knowledge necessary to answer these questions requires a multi-pronged approach employing creative microscopic, biochemical, and computational tools. As reviewed here, these are thankfully requirements that the field is actively addressing, suggesting that we will be well-positioned to answer many if not all of these pressing questions in the years to come.There are many fundamental and long-standing biological questions linked to 3D genome architecture, and we close by echoing a handful of them below. Does genome architecture itself define cellular identity? How does chromatin state (The authors have declared that no competing interests exist.


Paramecium these RNAs participate in the programming of large-scale genome reorganization during development, distinguishing germline-limited DNA, which is excised, from somatic-destined DNA. Only a handful of proteins playing roles in this process have been identified so far, including typical RNAi-derived factors such as Dicer-like and Piwi proteins. Here we report and characterize two novel proteins, Pdsg1 and Pdsg2 (Paramecium protein involved in Development of the Somatic Genome 1 and 2), involved in Paramecium genome reorganization. We show that these proteins are necessary for the excision of germline-limited DNA during development and the survival of sexual progeny. Knockdown of PDSG1 and PDSG2 genes affects the populations of small RNAs known to be involved in the programming of DNA elimination (scanRNAs and iesRNAs) and chromatin modification patterns during development. Our results suggest an association between RNA-mediated trans-generational epigenetic signal and chromatin modifications in the process of Paramecium genome reorganization.The epigenetic influence of maternal cells on the development of their progeny has long been studied in various eukaryotes. Multicellular organisms usually provide their zygotes not only with nutrients but also with functional elements required for proper development, such as coding and non-coding RNAs. These maternally deposited RNAs exhibit a variety of functions, from regulating gene expression to assuring genome integrity. In ciliates, such as  Paramecium, small RNAs participate in the programming of large-scale DNA deletion and genome organization during development. A characteristic feature of ciliates is the division of germline and somatic functions between two types of nuclei: the diploid micronucleus (MIC) and the polyploid macronucleus (MAC) respectively. New MIC and MAC develop from zygotic nuclei produced by fusion of haploid parental MIC during sexual cycle. In this process, a new MAC genome (72 Mb in Paramecium tetraurelia) matures from the MIC genome (97 Mb) by extensive editing In ciliates, such as Paramecium tetraurelia IESs are abundant , short (frequently less than 30 bp long), single-copy, noncoding sequences that are precisely excised from MIC DNA to produce the mature MAC genome Paramecium IES is flanked by two 5′-TA-3′ dinucleotides (TA repeats). IES excision leads to the retention of a single TA dinucleotide. The ends of IESs have symmetrical inverted base frequencies, which can be crudely represented by the consensus sequence TAYAGYNR with no other known conserved motifs. IES ends are similar to the ends of Tc1/mariner transposons Paramecium IES excisase, a domesticated PiggyBac-related transposase (PiggyMac)Paramecium, both precise excision of IESs and imprecise elimination of genomic regions containing transposable elements can be controlled by maternal effects. This epigenetic dependency was demonstrated by microinjection of DNA, in the form of an IES sequence. The introduction of an IES into the old MAC can prevent the elimination of identical sequences from the progeny's somatic genome Paramecium IESs assayed In These observations imply that trans-nuclear genome comparison occurs during development.E. coli producing double-stranded RNAs Paramecium tetraurelia and the related Oligohymenophorean ciliate Tetrahymena thermophila, a second RNAi-related pathway employs a distinctive class of sRNAs known as "scan RNAs" (scnRNAs) to perform the trans-nuclear comparison for the precise targeting of DNA elimination. In Paramecium and Tetrahymena Dicer-like proteins are responsible for producing scnRNAs in the meiotic MIC and Piwi proteins for binding and protecting scnRNAs Paramecium sRNA has demonstrated that scnRNAs initially correspond to the entire germline genome, and become progressively enriched in IESs matching sequences Paramecium scnRNAs are produced by a pair of paralogous Dicer-like proteins (Dcl2 and Dcl3) Tetrahymena scnRNAs are responsible for trimethylation of lysine 27 and lysine 9 of histone H3 (H3K27me3 and H3K9me3 respectively) within chromatin destined to be eliminated In ciliates, multiple RNA interference-related pathways exist. Post-transcriptional gene silencing can be induced by untranslatable transgenes Paramecium, a third class of development-specific sRNAs is present, iesRNAs varies in length from ∼21–31 nt (peaking at 27/28 nt) and are produced in the new MAC by a distinct Dicer-like protein, Dcl5 DCL5 shows that iesRNAs are involved in targeting DNA excision in the developing MAC In Paramecium one of the key players in the RNA-mediated trans-nuclear crosstalk is the Nowa1 protein (Nowa1) In Paramecium genome development. Both proteins are necessary for the excision of germline-limited DNA and for the survival of sexual progeny. Through high-throughput sequencing we show that PDSG1 and PDSG2 knockdowns affect Paramecium's development-specific sRNAs. Together these results suggest that these proteins are involved in the epigenetic programming of DNA remodelling and in IES excision. In addition, the knockdowns affect chromatin modification patterns during development which suggests a link between DNA elimination and histone modifications in the process of Paramecium genome maturation.In this study we report the discovery of two proteins, Pdsg1 and Pdsg2, involved in Paramecium strain51, mating type 7 was used in all experiments. Paramecium cells were cultured in Wheat Grass Powder  medium bacterized with Klebsiella pneumoniae, and supplemented with 0.8 mg/l of β-sitosterol. Cultures used in all the experiments were grown at 27°C.E.coli strain. Paramecium cells were seeded into silencing medium at a density of 200 cells/ml and silencing was carried out as previously described For the silencing constructs different regions of the coding sequences of each candidate were selected and cloned into L4440 plasmid  were used for the amplification of PDSG1 gene including 175 bp upstream and 68 bp downstream of the coding region. Full length PDSG2 gene along with 414 bp upstream and 343 bp downstream was amplified (5′-CGATAAAAGTTTGTTTTAATAAAATGATAATAAATCTCATAAAAGTG-3′ and 5′-GTATTTACTGCAGGTTTTTTTTGAATTGCATAAAC-3′). In case of PDSG2, GFP was inserted at the N-terminus. PDSG1 was tagged at both ends but only the C-terminal tagged version was expressing a functional GFP. The constructs were linearized and microinjected into the MAC of the vegetative cells. Positively injected clones were selected by dot blot analysis. Cells were collected at different time points during sexual development and counterstained with DAPI . Images for this experiment were acquired with a Leica microscope .A set of specific primers  was diluted 1∶3000 in 5% BSA supplemented with 0.1% Tween-20. Diluted antibody was incubated with dilution buffer only or with 0.02 ug of unmodified human histone H3 peptide  or with 0.02 ug of human histone H3 (tri methyl K27) peptide  for 3 h at the room temperature. Blot was incubated with secondary HRP conjugated antibody .Paramecium cells from 800 ml of culture were harvested and resuspended in 6 ml of TRI reagent BD (Sigma-Aldrich). Total RNA extraction was carried out following the TRI Reagent BD protocol. The enrichment of small RNA was performed using mirVana miRNA Isolation Kit (Ambion). Enriched samples were used for library preparation following the TruSeq Small RNA Sample Preparation protocol (Illumina). Reads were mapped with BWA PDSG1 and PDSG2 sequences are available from the GenBank under the accessions: XM_001442856 and XM_001425883, respectively. ACTIN gene accession number: XM_001443584. Raw sRNA sequence data for the PDSG1-KD, PDSG2-KD, control early and control late time points can be obtained from the European Nucleotide Archive (ENA) under the accessions: PRJEB5853, PRJEB5867, SRR907874 and SRR907875, respectively.http://pfam.sanger.ac.uk/search) Homology detection and structure prediction were estimated by open access Pfam ) and as a positive control we used a NOWA1 silencing construct which blocks the excision of maternally controlled IESs and is lethal to the sexual progeny To determine whether the developmentally upregulated gene candidates have an effect on the formation of fully functional progeny; we silenced each one of the genes independently during development and observed the effects on the offspring based on the ability of maintaining normal vegetative growth. The silencing was induced by feeding Paramecium protein involved in Development of the Somatic Genome 1 and 2(PDSG1 and PDSG2). The dramatic effect on the survival of sexual progeny of PDSG1-KD and PDSG2-KD (90% and 93%) was comparable to Nowa1 depletion (used as control), suggesting that Pdsg1 and Pdsg2 are essential for adequate completion of developmental process in Paramecium. In addition to the survival test, silencing efficiency was assessed by Northern blot , but does prevent the production of iesRNAs at late developmental stage (iesRNAs (peaking at 27–28 nt) appear in the late developmental stage in cells fed with empty vector (control)  as a conelopment  may thusal stage . The inhE. coli producing RNA from empty vector construct (PDSG1 was silenced in wild type strain and compared to the IES-51A2591 injected strain (PDSG1 promoted complete excision of the IES-51A2591 in the injected strain (In order to determine the role of Pdsg1 in the scanning process we monitored the retention of a specific IES (51A2591) during the depletion of Pdsg1. In wild type strain the IES-51A2591 is normally excised after development and its excision was not affected in PDSG1-KD strains , lane 2.onstruct , lane 3.onstruct , lane 4.d strain , lane 5.d strain , lane 6.PDSG1- and PDSG2-KD affect the production of iesRNAs we decide to check the consequences of the depletion of Pdsg1 and Pdsg2 on Dcl5 localization. As previously described Since both Tetrahymena we decided to test if these decorations are also present in Paramecium and whether they are affected by the knockdowns of PDSG1 and PDSG2.Since H3K27me3 and H3K9me3 histone marks are associated with DNA elimination in Tetrahymena, both H3K27me3 and H3K9me3 are undetectable in the macronucleus of vegetative cells PDSG1-KD cells , shown to be a necessary for scnRNA-dependent DNA elimination in Figure S1Primers used for amplification of silencing constructs for all selected candidates.(TIF)Click here for additional data file.Figure S2Twenty-eight selected candidates with accession numbers and predicted protein domains.(TIF)Click here for additional data file.Figure S3Gene expression profiles of selected candidates.(TIF)Click here for additional data file.Figure S4Primers used for IES retention PCR.(TIF)Click here for additional data file.Figure S5Summarized results of IES retention PCR. Seven tested IES are listed in the first column. IES retention is indicated with (+). Maternally controlled IESs are labelled as ‘mc’ and non-maternally controlled IESs as ‘non-mc’.(TIF)Click here for additional data file.Figure S6Depletion of Pdsg2 affect the normal distribution of Dcl5-GFP in the new MAC. Localization of Dcl5-GFP to the new MAC in control, PDSG1-KD and PDSG2-KD strains. DAPI staining , Dcl5-GFP (green) and merge images are shown. White arrows indicate developing MACs. (bottom panel) New MAC is shown in detail from the merge images.(TIF)Click here for additional data file.Figure S7The effects of PDSG1 and PDSG2 knockdowns on H3K9me3 histone modification.  Maternal MAC in vegetative cells of EV, PDSG1-KD and PDSG2-KD.  Middle stage of development with the fragments decorated with H3K27me3.  Late stage of development with histone modification present in new MAC. Magenta: DAPI; yellow: H3K27me3; white arrow: MAC; arrow head: new MAC. Scale bar 5 µm.(TIF)Click here for additional data file.