CDKN1C gene encodes the p57Kip2 protein which has been identified as the third member of the CIP/Kip family, also including p27Kip1 and p21Cip1. In analogy with these proteins, p57Kip2 is able to bind tightly and inhibit cyclin/cyclin-dependent kinase complexes and, in turn, modulate cell division cycle progression. For a long time, the main function of p57Kip2 has been associated only to correct embryogenesis, since CDKN1C-ablated mice are not vital. Accordingly, it has been demonstrated that CDKN1C alterations cause three human hereditary syndromes, characterized by altered growth rate. Subsequently, the p57Kip2 role in several cell phenotypes has been clearly assessed as well as its down-regulation in human cancers. CDKN1C lies in a genetic locus, 11p15.5, characterized by a remarkable regional imprinting that results in the transcription of only the maternal allele. The control of CDKN1C transcription is also linked to additional mechanisms, including DNA methylation and specific histone methylation/acetylation. Finally, long non-coding RNAs and miRNAs appear to play important roles in controlling p57Kip2 levels. This review mostly represents an appraisal of the available data regarding the control of CDKN1C gene expression. In addition, the structure and function of p57Kip2 protein are briefly described and correlated to human physiology and diseases.The  Cip1/WAF1, p27Kip1 and p57Kip2. Due to their broad inhibitory effect on cyclin-CDK complexes, CIP/Kip members have been mainly considered as antiproliferative proteins and their encoding genes as potential tumor suppressor genes. However, strong emerging pieces of evidence have demonstrated that the activities of CIP/Kip members are well beyond that of modulators of cell division ..KCNQ1 inH19 (a gene for a long noncoding RNA) and IGF2 (encoding for insulin-like growth factor 2) by restricting the access to the enhancers  [IGF2 is paternally expressed, H19 is maternally transcribed, and CDKN1C is maternally expressed, even though a weak expression of the paternal allele has been demonstrated in some human tissues [ICR1 is telomeric and regulates the imprinting of sulator) . Interes tissues .Cdkn1c that are similar in humans. First, a proximal promoter element  contains several consensus sequences for Egr1 and Sp1 [CDKN1C promoter is strongly influenced by the high presence of CpG islands, located upstream and downstream of the transcription start site, responsible for genomic imprinting and epigenetic gene silencing. This is achieved by CpG dinucleotide methylation and/or through chromatin remodeling by histone covalent modifications (histones H3 and H4 methylation and acetylation) [Cdkn1c transcription by binding to a site located at −3300 [CDKN1C in proliferating lens epithelial cells [CDKN1C gene and is responsible for the glucocorticoid inducibility of the CDKN1C gene [Two main promoter elements have been identified in mouse  and Sp1 ,76. Intr and Sp1 ,78. Furt and Sp1 . Finally and Sp1 . Importaylation) . More diylation) , E47 [83ylation) , repressat −3300 , or Hes-al cells . FurtherN1C gene , thus exN1C gene .Cdkn1c transcription are located distantly from the gene. As a matter of facts, enhancers for its expression lie more than 25 kb downstream of the gene. Experiments with artificial chromosome also suggest the existence of enhancer(s) located very distantly from CDKN1C [CDKN1C enhancer elements localized in a region between 255–387 kb [In mouse, additional key elements of m CDKN1C . Accordi5–387 kb .CDKN1C lies in humans and mice in a very complicated cluster of imprinted genes, controlled through superimposed cis-acting mechanisms. Genomic imprinting is an epigenetic process that results in parent-of-origin specific allelic expression [CDKN1C and UBE3A) or the paternal allele  [pression . A relatpression ,92 showind NNAT) . Imprintnd NNAT) .CDKN1C, its transcription is regulated by the imprinting center KvDMR1 that acquires DNA methylation in the maternal germline [CDKN1C promoter and gene body are also directly methylated on the paternal allele post-fertilization, after allelic silencing has been established [Regarding germline ,95,96. Tablished .cis-acting mechanisms responsible for the imprinted silencing of the paternal allele (briefly summarized in the previous paragraph), trans-acting mechanisms participate in the epigenetic modulation of CDKN1C gene expression [CDKN1C gene body.Besides pression . Indeed,CDKN1C gene is included in a CpG island extended about −600 bp from the transcriptional start site up into the gene body. This CpG island presents, in mice but not in humans, a differential methylation between the two inherited alleles, being the paternal one hypermethylated and the maternal one hypomethylated. This methylation pattern seems to be acquired successively to the ICR2-dependent DNA-modifications and is involved in the maintenance and reinforcement of the imprinted repression of the paternal allele. Several regulators have been involved in this process. One of them is Lsh (lymphoid-specific helicase) a protein belonging to the family of SNF2/helicases that act as chromatin remodeler and regulate DNA methylation. Lsh directly binds to CDKN1C promoter and allows the maintenance of hypermethylation of the paternal allele [l allele .d-dependent expression of p57Kip2 [Complete biallelic hypermethylation occurs in human tumors and tumor cell lines ,101, as  p57Kip2 .CDKN1C promoter methylation. As matter of fact, both DNMT1 [CDKN1C promoter. Consistently with the importance of DNA methylation not only in paternal allele imprinting but also in p57Kip2 expression modulation in specific cellular and cell cycle phase contexts, treatment of many human tumor cell lines with demethylating agents such as 5-azacytidine and 5-aza-2′-deoxycytidine results generally in p57Kip2 expression activation [Most interesting is also the role played by different members of the DNA methyltransferase (DNMT) family, the enzymes catalyzing the transfer of methyl groups to cytosines. Results from genetic ablation studies support the notion that not only DNMT1, mostly in charge of maintaining the methylation pattern of CpG islands during DNA replication, but also DNMT3a which is generally involved (together with DNMT3b) in de novo methylation of most imprinting control regions in the germline, are involved in th DNMT1 ,103, andth DNMT1  have beetivation ,104.Histone modifications represent fundamental factors involved in chromatin plasticity, controlling gene promoter accessibility and gene expression activation ,106.CDKN1C transcription. Accordingly, the level of H3 and H4 acetylation directly correlates with the gene expression and, in turn, with several phenotypes including differentiation and carcinogenesis.Acetylation and methylation of core histone tails, in addition to DNA methylation, are key mechanisms for regulating CDKN1C inactivation [Specifically, a decrease of H3 lysine 4 dimethylation and histone H3 lysine 9 and 14 acetylation is observed on the paternal allele respect to the maternal one, facilitating its inactivation ,108. On tivation .CDKN1C expression and H3K9/K14 acetylation has been demonstrated. For example, cancer cells with low (or absent) p57Kip2 present histone hypoacetylation and, vice versa, tumors with a high level of the CKI show hyperacetylation [Kip2 after histone deacetylase (HDAC) inhibitor treatment [CDKN1C promoter region. We must underline that HDAC1 is highly expressed in many cancers including gastric [Under various conditions, a direct correlation between tylation ,110. Thereatment . Mechani gastric , colorec gastric , hepatic gastric , breast  gastric , and pan gastric . HDAC2 h gastric  and is o gastric , prostat gastric , and gas gastric .An additional recognized histone epigenetic mark includes a lysine trimethylation, specifically H3K27me3 (trimethylation of lysine 27 of histone H3). Such a modification, also responsible for the paternal allele exclusion, is involved in the maturation of glial cells .CDKN1C expression, while its decrease, due to a reduction in the levels or activity of EZH, a specific promoter-binding PRC2 subunit, up-regulates gene transcription [This histone trimethylation mark is due to the Polycomb repressive complex 2 (PRC2). The increase of H3K27me3 reduces cription .KvDMR1 on the maternal locus, while it is not present on the paternal allele, in accord with the imprinted silencing of the paternally-derived allele [Di- and trimethylation of lysine 9 of the histone H3, an additional histone modification, also appears to control the expression of d allele ,108.CDKN1C expression. As matter of fact, an altered accumulation of H3K9me2 on the maternal KvDMR1 allele results in the lack of response to MyoD in that it reduces the accessibility of the transcription factor to the DNA [In the same context, it is important to stress the role of histone modifications in MyoD control of  the DNA .KvDMR1 due to epigenetic factors  appears a key mechanism in the control of CDKN1C expression. The last several years have identified the existence of a strict crosstalk between all the epigenetic modifications, including the binding of modifying enzymes to the specific sites of action. In this complex interplay, an important role for non-coding RNAs has emerged.In brief, the regulation of CDKN1C gene expression is controlled by lncRNAs, which act in strict crosstalk with signal pathway-induced transcription factors and chromatin modifiers, accounting for spatial- and temporal-specific gene activation during development or cell commitment and differentiation in adult life. Specifically, the macro lncRNA Kcnq1ot1, first discovered both in humans and mice as a KvDMR1-associated RNA, has emerged as a critical regulator of the chromatin status of the gene, at least in relation to the imprinting control [KCNQ1 and KCNQ1OT1 share a region of overlapping DNA and are transcribed in opposite directions. KCNQ1 encodes the potassium voltage-gated channel subfamily Q member 1, a protein required for the repolarization phase of the cardiac action potential. Differently from KCNQ1 that allows the synthesis of a protein, KCNQ1OT1 codifies a long-noncoding RNA that regulates the expression of several genes. Its promoter is normally hypermethylated in the maternal allele, thus hampering its expression. On the contrary, the paternal allele is normally transcribed, being hypomethylated [in cis). Mechanistically, it is able to interact with histone methyltransferase complexes  causing the enrichment of repressive histone modifications. This activity results in the epigenetic inactivation of paternally inherited CDKN1C [CDKN1C promoter and therefore implementing the repression of gene expression [As for many genes playing fundamental roles in development,  control . KCNQ1 athylated ,123. Whed CDKN1C . Kcnq1otpression .CDKN1C expression. Tug1 is a long non-coding RNA mostly occurring in the retina and in the brain. It has been proposed to control cell growth by epigenetically down-regulating CDKN1C. In addition, Tug1 seems to predict a negative prognosis in gastric cancer [Kip2 at the cellular level probably acting in a phenotype-specific manner.Additional long non-coding RNAs, like Tug1 (taurine upregulated gene 1) , Linc006c cancer . These dCDKN1C mutations can lead to embryonic abnormalities, such as those occurring in Beckwith-Wiedmann syndrome , IMAGe syndrome (OMIM 614732) and Russell–Silver syndrome (RSS-OMIM 180860) and acquired diseases such as cancer.Genetic and epigenetic disorders in the imprinted region 11p15 and BWS, IMAGe syndrome and RSS are genetic diseases with different features, belonging to the group of congenital imprinting disorders. BWS has a prevalence of 1–5:10,000 live births and is characterized by overgrowth, tumor predisposition, abdominal wall defects and congenital malformations such as macroglossia, hemihyperplasia, hyperinsulinaemic hypoglycemia, ear anomalies, nephrologic and capillary malformations and organomegaly . EpigeneCDKN1C gene mutations are frequently noticed [CDKN1C mutations are also reported in sporadic BWS cases and they have been identified as the causative genetic alterations [They include epigenetic alterations such as methylation defects, specifically loss of methylation at IC2 regulatory region (IC2-LoM) and gain of methylation at IC1 (IC1-GoM), as well as genetic alterations including uniparental paternal disomy of the 11p15.5 locus (UPD), followed by a lower percentage of cases with microdeletion/duplications or point mutations involving either one of the two ICRs responsible for the locus imprinting region ,130,131. noticed , reachinerations ,133. Miserations ,135. Interations .www.orpha.net) in which a cluster of CDKN1C missense mutations in the PCNA binding domain, result in growth inhibition) [CDKN1C, respectively [Opposite to BWS, IMAGe  syndrome is a rare condition (ibition) . Interesectively ,137.CDKN1C mutation affecting the PCNA binding domain has been found [Imprinting alterations in the 11p15 region are also described in RSS, a disease  characterized by intrauterine growth retardation, very limited postnatal growth, skeletal abnormalities such as peculiar craniofacial characteristics and body asymmetry, and several minor malformations. Moreover, in RSS is also reported a maternal duplication in this region ,139 and en found .Kip2 in BWS and in some human tumors suggests that p57Kip2, like p27Kip1, might also have a role in the process of carcinogenesis , and complete and partial hydatidiform moles [Gestational diseases, such as pre-eclampsia and intra-uterine growth restriction (IUGR), are also associated with altered p57gulation  and upregulation  of CDKN1rm moles ,173.Importantly, placentomegaly due to abnormal proliferation of extravillous trophoblasts, and accumulation of intervillous fibrinoid can be observed also in BWS syndrome ,174.IGF2 and CDKN1C locus has been proposed as the genetic link between them [PMD is a rare condition (0.02% pregnancies) associated with different fetal outcomes, ranging from structurally normal fetus/newborn (in most cases) to fetal and neonatal abnormalities, including those present in BWS, and mortality . Indeed,een them ,177,178.Kip2 as an important player in these placental diseases has led to the development of diagnostic procedures based on immunohistochemistry using anti-p57Kip2 antibodies and histological analysis for the characterization of hydatidiform moles and PMD, and their differential diagnosis [The identification of p57iagnosis ,180,181.Kip2, among others, may provide a similar origin for neoplastic proliferation and metabolic disorders. p57Kip2 is specifically expressed in the endocrine portion of the pancreas and particularly in β-cells where it is paternally imprinted [Kip2 is not expressed within the focal adenomatous hyperplastic lesions. This missing expression is caused by somatic loss of heterozygosity and associated with increased proliferation of β-cells [Kip2 negative human pancreatic islets restored proper glucose control when transplanted into hyperglycemic, immunodeficient mice [Kip2 is associated with early-adulthood-onset diabetes, in addition to intrauterine growth restriction and short stature [CDKN1C. In fact, a double dose of Cdkn1c promoted the brown adipose tissue development in a mouse model of the RSS [Kip2, in the same model, hindered completely the formation of brown adipocytes [Although some human diseases may represent very different clinical entities, some common pathways may be identified in their etiopathogenesis. This could be the case for cancer, metabolic diseases and related cardiovascular disease ,183,184.mprinted . In foca β-cells . On thisent mice . Interes stature . Not onl the RSS . Conversipocytes .Kip2 may also change according to genetic variations that do not pertain to CDKN1C, the p57Kip2-coding gene but are located in neighboring genomic sites. In particular, a mutation has been identified at the KCNQ1 locus that increases the expression of p57Kip2 in mouse pancreatic islets by epigenetically modifying Cdkn1c [CDKN1C diseases, this mutation is effective only when inherited from the paternal allele and, no matter which is the mechanism increasing the levels of p57Kip2 in the endocrine pancreas, β-cell mass is reduced [The expression of p57g Cdkn1c . Differe reduced .Kip2 expression could be a promising therapeutic strategy also in humans with type 2 diabetes and/or obesity. Indeed, oral administration of FTY720, a sphingosine 1-phosphate receptor agonist, normalizes glycemia in diabetic db/db mice by downregulating islet p57Kip2 and promoting β-cell regeneration [Kip2 should be pursued, in order to avoid undesired effect in either pancreatic or adipose tissue when trying to manipulate p57Kip2 in one of the two tissues.Thus, it is conceivable that altering p57neration . HoweverKip2 plays pivotal and specific roles in human physiology that cannot be replaced by the other two members of its protein family, i.e., p27Kip1 and p21Cip/WAF1. As a matter of fact, CDKN1C-deleted mice show a very high percentage of mortality demonstrating that the protein is necessary for correct embryogenesis. Accordingly, in humans, three important syndromes, all showing an altered growth, are due to CDKN1C alterations. Particularly, the BWS is characterized by signs of overgrowth with infants considerably larger than normal and the IMAGe and RSS are both characterized by slow growth before and after birth and growth retardation.It has been clearly established that p57Kip2 has been discovered as a very important factor [It is well known that adult stem cells settle specific niches in different tissues and organs, thus supporting their repair/regeneration ,192,193.t factor ,63,194.Kip2 is so important for a normal growth and tissue differentiation, are still not well understood. They refer, only in part, to the capability of the protein to modulate cyclin/CDK activity, a function that is played approximately by the first 100 residues of p57Kip2 where the KID is localized. On the other hand, numerous activities of CKI have been associated with the C-end region that includes the PAPA domain and the QT domain. These protein domains seem to be involved in the control of cell movement and the organization of mitotic spindle (via interaction with the cytoskeleton). Numerous pieces of evidence also suggest that p57Kip2 C-terminus participates in endoreduplication, apoptosis, autophagy, and senescence. Altogether these observations point to the definite identification of p57Kip2 interactors as a pivotal issue in the studies on CKI. Unfortunately, the protein belongs to the family of IUP, namely, proteins lacking a tertiary structure that fold upon binding. This structural feature allows p57Kip2 to have a large degree of plasticity and to interact with several different proteins. Although this is certainly a great advantage in terms of function, it results in remarkable difficulties in the precise definition of the mechanism of p57Kip2 action. An additional important aspect of p57Kip2 studies is the knowledge of processes that regulate its level. CDKN1C is subject to an epigenetic control and only the maternal allele is expressed. The gene lies in a locus, 11p15.5, that represents a major example of regional imprinting and that is strongly regulated by mechanisms acting in cis  as well as in trans (namely via DNA methylation and histone acetylation). An additional level of intracellular control of p57Kip2 amount could be related to the protein degradation that involves, a not completely clarified, ubiquitination/proteasomal mechanism.The molecular mechanisms by which p57Kip2 is clear, the details of its regulation and interactors appear enigmatic and intensive research and the development of novel cellular and animal models are required. This is particularly relevant in view of the plethora of p57Kip2 functions played in different tissues and distinct phenotypes both in normal and pathological conditions. It is conceivable that the elucidation of these aspects will provide important directions for human physiologic research and for the development of novel strategies for targeted therapy of several relevant human diseases.In conclusion, while the important role of p57


Krüppel-like factor 9 (Klf9) is a zinc finger transcription factor that functions in neural cell differentiation, but little is known about its genomic targets or mechanism of action in neurons.Klf9-null mice. Gene ontology analysis revealed that Klf9 genomic targets include genes involved in cystokeletal remodeling, Wnt signaling and inflammation.We used the mouse hippocampus-derived neuronal cell line HT22 to identify genes regulated by Klf9, and we validated our findings in mouse hippocampus. We engineered HT22 cells to express a Klf9 transgene under control of the tetracycline repressor, and used RNA sequencing to identify genes modulated by Klf9. We found 217 genes repressed and 21 induced by Klf9. We also engineered HT22 cells to co-express biotin ligase and a Klf9 fusion protein containing an N-terminal biotin ligase recognition peptide. Using chromatin-streptavidin precipitation (ChSP) sequencing we identified 3,514 genomic regions where Klf9 associated. Seventy-five percent of these were within 1 kb of transcription start sites, and Klf9 associated in chromatin with 60% of the repressed genes. We analyzed the promoters of several repressed genes containing Klf9 ChSP peaks using transient transfection reporter assays and found that Klf9 repressed promoter activity, which was abolished after mutation of Sp/Klf-like motifs. Knockdown or knockout of Klf9 in HT22 cells caused dysregulation of Klf9 target genes. Chromatin immunoprecipitation assays showed that Klf9 associated in chromatin from mouse hippocampus with genes identified by ChSP sequencing on HT22 cells, and expression of Klf9 target genes was dysregulated in the hippocampus of neonatal We have identified genomic targets of Klf9 in hippocampal neurons and created a foundation for future studies on how it functions in chromatin, and regulates neuronal morphology and survival across the lifespan.The online version of this article (doi:10.1186/s12864-017-3640-7) contains supplementary material, which is available to authorized users. Cyp1a1 (Cytochrome P450) gene ) was slightly higher than that of the parent line, but this was not statistically significant  = 480.974, p < .001; n = 3/treatment; ANOVA), which is within the physiological range seen following hormone treatment in HT22 cells, in neonatal mouse brain following hormone treatment  cells with a luciferase reporter vector containing three tandem repeats of the BTE sequence (pGL4.23-3xBTE), which supports Klf9-dependent transactivation or transrepression depending on the cell type  cells, and that this leads to the production of functional Klf9 protein.We were unable to detect the endogenous (native) or recombinant (dox-induced) Klf9 protein by Western blotting on nuclear extracts of HT22 cells (data not shown), or with extracts from different cells/tissues of mouse or ell type ; a promon = 3/treatment for the parent line, 3 for HT22 [TR/TO-Klf9] vehicle treated and 2 for HT22 [TR/TO-Klf9] dox treated; a third replicate had to be discarded due to a technical error). We aligned sequencing reads to the mm8 build of the mouse genome using Bowtie  cells and found 238 differentially expressed genes, 217 downregulated and 21 upregulated (FDR-adjusted p < .005). The changes in mRNA level after 8 h of dox treatment ranged from − 1.2 to 1.52 (log2 fold change). The top 10 most strongly induced and repressed genes are listed in Table Klf13: F = 7.708, p < .005; Limk1: F = 6.417, p < .005; Mapk11: F  = 22.107, p < .001; Pou6f1: F = 4.286, p < .05; n = 4–6/treatment; ANOVA).We conducted RNA sequencing (RNA-seq) on parent HT22 cells and the HT22 [TR/TO-Klf9] cell line treated with vehicle or dox for 8 h  = 14.97, p < .001; n = 4/time point; ANOVA). We then conducted RTqPCR on 10 Klf9-repressed and 6 Klf9-induced genes with log2 fold changes ranging from − 0.44 to − 1.12 for repressed, or 0.38 to 0.56 for induced genes. We validated 9 repressed genes  = 6.642, p < .001; Limk1: F = 4.048, p < .05; Mapk11: F = 4.787, p < .01; Pou6f1: F = 4.527, p < .05; Klf16: F = 6.914, p < 0.05; Apc2: F = 5.548, p < .005; Nlgn2: F = 3.185, p < .05; Smurf1: F = 4.689, p < .01; Nyap1: F = 3.474, p < .05; n = 4/time point; ANOVA). Messenger RNA for Hhipl was unaffected (data not shown). However, of the 6 Klf9-induced genes tested, we could validate only 3 . The mRNAs of the 3 induced genes that we validated were increased following 8–12 h of dox treatment, but returned to baseline by 24 h  = 3.055, p < .05; Cdkn3: F = 3.736, p < .05; n = 4/time point; ANOVA).To further validate our RNA-seq data set, and to investigate the kinetics of Klf9-dependent gene repression, we conducted RTqPCR on HT22 [TO/TR-Klf9] cells treated with dox for different times. We observed a statistically significant increase in E. coli biotin ligase BirA (HT22 [BirA]) or BirA plus a Klf9 fusion protein with an N-terminal FLAG tag and biotin ligase recognition peptide (HT22 [BirA/FLBIO-Klf9])  and HT22 [BirA/FLBIO-Klf9] cells. This resulted in ~25 fold enrichment above background (ChSP on chromatin from HT22 [BirA]) at the Klf13 promoter but not at the same Klf13 intronic region  = −8.315, p < .0005; n = 4/cell line; Student’s two-sample t-test). These findings support that both native Klf9 and the FLBIO-Klf9 fusion protein associate in chromatin at the Klf13 locus, and that the ChSP technique results in a greater signal/noise ratio than ChIP assay.We engineered HT22 cells to express the O-Klf9]) . This alWe conducted ChSP sequencing (ChSP-seq) on chromatin isolated from HT22 [BirA] and HT22 [BirA/FLBIO-Klf9] cells to identify sites of Klf9 association in chromatin. We used two independent peak calling programs (MACS and PePr) to identify genomic regions with higher densities of mapped reads , 25. MACSlc11a2; upper left panel) to smallest . The small peak at the Mapk11 5′ upstream region was detected by MACS but not by PePr  = −10.981, p < .001; Klf16: t(6) = −14.417, p < .001; Klf13: t(6) = −10.981, p < .001; Sin3a: t (6) = −10.135, p < .001; Nr3c1: t (6) = −9.402, p < .001; Limk1: t(6) = −8.561, p < .001; Klf11: t (6) = −8.653, p < .001; Mapk11: t(6) = −5.448, p < .005; n = 4/cell line; Student’s two-sample t-test). We also conducted ChIP assay for Klf9 on chromatin extracted from the parent HT22 cell line, which showed statistically significant Klf9 ChIP signal  at four genes tested  = 6.285, p < .005; Limk1: t (4) = 4.093, p < .05; Nr3c1: t (4) = 3.778, p < .05; Sin3a: t (5) = 3.159, p < .05; n = 4/treatment; Student’s two-sample t-test). The Klf9 ChSP signal was at the background level at intronic regions located 10 kb or more downstream from the identified Klf9 peaks at the Klf16, Limk1 and Nr3c1 genes  = 2.260, p < .05; Klf16: t(12) = .4.458, p < .001; Klf13: t(13) = 3.752, p < .005; Sin3a: t(12) = 5.659, p < .0005; Nr3c1: t(10) = −2.684, p < .05; Limk1: t(11) = 3.887, p < .005; Klf11: t(16) = 2.246, p < .05; Mapk11: t(16) = −.0265, p = .979; n = 10; Student’s two-sample t-test). Since there were no statistically significant differences between the sexes we pooled the data for analysis. The lack of Klf9 ChIP signal at the 5′ upstream region of Mapk11 is consistent with this region having the lowest ChSP signal in HT22 [BirA/FLBIO-Klf9] cells . We analyzed the same 8 genomic regions described above for HT22 cells and found statistically significant Klf9 ChIP signal at 7 of the 8 regions in both males and females  have a Klf9 peak within 10 kb of their TSS. By contrast, of the 21 genes found to be induced by Klf9 only 1 (5%) had a peak within 10 kb of its TSS Fig. . This isp = .057; HT22 [TR/TO-Klf9]: p < .001; Mann–Whitney U-test). This supports that Klf9 exerts a general repressive action on transcription of genes with which it associates.An additional 2,322 genes had at least one Klf9 peak located within 10 kb of their TSS, but their mRNA levels analyzed by RNA-seq were not significantly affected by forced Klf9 expression. However, when we looked at the mean mRNA level for all polyadenylated transcripts detected by RNA-seq in the HT22 [TR/TO-Klf9] cell line treated with vehicle or dox, and compared genes that did or did not have Klf9 peaks within 10 kb of their TSS, we found evidence for a general repressive action of Klf9 on transcription of genes possessing Klf9 peaks Fig. . We calcp < 1×10−20) matched previously reported binding sites for early growth response 2 (Egr2), ELK4, ETS transcription factor (Elk4), E2F transcription factor 1 (E2f1), Fos-like 2 (Fosl2), basic helix-loop-helix (bHLH), and regulatory factor X domain-containing 2 (Rfxdc2). A complete list of enriched motifs is given in Additional file We used the program HOMER to identify enriched DNA sequence motifs in Klf9 ChSP-seq peaks . The mosp < .0001; Spearman Rank Order correlation).The Klf9 consensus motif tended to occur at or near the center of peaks, and the average density of mapped sequencing reads across the peaks closely matched the frequency of motif occurrence Fig. . The peade novo analysis of the Klf9 ChSP peaks found that only 4 of 21 (19%) contained one copy of the Klf9 consensus motif, while 17 of 21 (81%) had at least one Sp/Klf motif.We also used HOMER to analyze the promoters of the 21 genes that were induced by Klf9. Sequences of 1000 bp in length located upstream of the TSSs of these genes were downloaded from the UCSC genome browser. Scanning these sequences for the presence of Sp/Klf motifs found by We analyzed Klf9 peaks using the program SIC-ChIP , which c(2) = 528.802, p < .001; Kruskal-Wallis non-parametric ANOVA). There was no significant difference between Clusters 2 and 3 in average number of Sp/Klf motifs, but peaks from Cluster 3 contained more copies of the Klf9 consensus motif  = 245.055, p < .001; Kruskal-Wallis non-parametric ANOVA).The Klf9 consensus motif was highly enriched in peaks from all three categories. In genomic regions defined by Clusters 1 and 3, the motifs were located near the center of the peak, while in Cluster 2 the distribution was spread evenly across the peak .The proportion of peaks associated with Klf9-repressed genes differed among clusters, with Cluster 3 having the highest percentage, and Cluster 1 the lowest Fig. . This su(2) = 45.181, p < .001; Kruskal-Wallis non-parametric ANOVA).We also calculated the expression ratio of genes with peaks from each cluster in vehicle vs. dox-treated HT22 [TR/TO-Klf9] cells. Genes with peaks from Cluster 2 show a lower expression ratio (indicating greater repression) than genes with peaks from Cluster 1, and genes with peaks from Cluster 3 have a lower expression ratio than from Cluster 2, supporting that Klf9 exerts a stronger repressive effect on transcription from peaks with either a greater number of consensus motifs or a higher ChSP signal , Klf16 , Limk1  and Mapk11   = 8.512, p < 10−5; Klf16: t (10) = 4.430, p < .005; Limk1: t (10) = 3.275, p < .01; Mapk11: t (9) = 1.509, p = .166; Klf13 mutant: t (10) = −1.331, p = .213; n = 6/treatment; Student’s two-sample t-test).To determine if Klf9 can repress transcription of genes with which it associates in chromatin we transfected HT22 [TR/TO-Klf9] cells with pGL4.23 reporter constructs containing DNA fragments corresponding to genomic regions with Klf9 ChSP peaks: Klf13 5′ flanking region, which contains 6 Sp/Klf motifs  = −4.628, p < .005; Klf16: t(8) = −4.628, p < .005; Klf13: t(10) = −2.829 p < .05; Sin3a: t(9) = −2.138, p = .06; Nr3c1: t(10) = −2.684, p < .05; Limk1: t(8) = −2.334, p < .05; Klf11: t(7) = −2.371, p < .05; Mapk11: t(6) = −2.299, p = .06.; n = 6/treatment; Student’s two-sample t-test).Our RNA-seq experiment showed that Klf9 acts predominantly as a transcriptional repressor in HT22 cells. The N-terminus of Klf9 has a motif for interaction with the scaffolding repressor protein Sin3a  which reMapk11  = 9.091, p < .005; Klf13: F = 14.267, p < .001; Klf16: F = 14.135, p < .001; Limk1: F = 5.875, p < .05; Klf11: F = 9.1, p < .01; Mapk11: F = .922, p = .422; n = 6/cell line; ANOVA). The CRISPR line 2 also had higher mean mRNA levels for all genes analyzed  and this was statistically significant for Klf13, Limk1 and Klf11.To determine if loss of Klf9 alters the expression of Klf9 target genes we used CRISPR/Cas9 genome editing to generate Klf9 knockdown (CRISPR line 1) and knockout (CRISPR line 2) HT22 cell lines. A description of the mutations introduced into these cell lines is given in Additional file Klf9 mRNA in the mouse CNS is low at birth, then rises during the first 4 weeks of life, paralleling the postnatal rise in plasma TH concentration .–37.35–37We then conducted pathway analysis on the set of all genes with Klf9 ChSP peaks. The most enriched PANTHER pathways among this set of genes were “Apoptosis”, “Integrin signaling pathway”, “Inflammation mediated by chemokine and cytokine signaling pathway” and “Cytoskeletal regulation by Rho-GTPase”  = 56.27, p < .001; M (uppercase): F = 408.754, p < .001; n = 4/cell line; ANOVA). The mRNA levels for two confirmed Klf9 targets that are classified as Wnt-pathway related and are implicated in promoting mitosis, B cell CLL/Lymphoma 6 (Bcl-6) and inositol triphosphate receptor 3 (Itpr3), were significantly elevated in both CRISPR lines  = 19.797, p = 0.001; Iprt3: F = 13.346, p = 0.002; n = 6/cell line; ANOVA).Previous work showed that Klf9 can reduce proliferation of different cell types –40. One bona fide Klf9 targets in mouse hippocampus in vivo, supporting that the HT22 cell line represents a useful model for mature hippocampal neurons, at least for the purpose of identifying candidate Klf9 target genes. Taken together, our findings represent an important advance in understanding the diverse developmental and physiological roles that Klf9 has in the mammalian central nervous system.Here we report the first genome-wide analysis of Klf9 genomic targets in a mammalian neuronal cell type. Klf9 has been implicated in several aspects of neural development and regeneration, and is regulated by hormones , 18, NFκFindings from our RNA-seq experiment conducted in HT22 cells support that Klf9 acts predominately as a transcriptional repressor, with 10 times more genes repressed than induced. This is consistent with a recent study in glioblastoma cells that showed that Klf9 functions primarily as a transcriptional repressor . Of the Using ChSP-seq we found that Klf9 associates predominantly with genomic regions within 1 kb of TSSs. Genes found to be repressed by Klf9 by RNA-seq were much more likely than induced genes to have Klf9 associated in chromatin . These findings support that Klf9 represses gene transcription by associating in chromatin at or near proximal promoters. In support of this, the presence of Klf9 at proximal promoter regions was associated with weak repression when averaged across all genes with peaks associated see Fig. . BecauseThe most enriched motif in Klf9 peaks was an Sp/Klf motif (GCCACGCCCMCY) that was present in 75% of all peaks; furthermore, 98% of all peaks had at least one Sp/Klf motif that was similar to the consensus motif. The consensus motif tended to occur near the center of peaks and its position correlated with the density of mapped reads, supporting that Klf9 binds directly to these DNA sequences.Early studies on Klf9 function showed that it could act as a transcriptional activator or repressor depending on the number of Sp/Klf motifs present in the regulatory element , 3. In CAn additional Sp/Klf-like motif different from the Klf9 consensus motif was present in 14.84% of peaks  was the E-box. The E-box is required for transcriptional activation by the core circadian transcription factors CLOCK and Bmal1, which form a regulatory feedback loop with Per and Cry proteins to orchestrate circadian rhythms in most tissues –51. Seveing Klf9 . Klf9 meing Klf9 , but to ing Klf9 , 54, no Different types of chromatin-associated proteins have different ChIP-seq profiles  and different genomic regions associated with the same factor may have different peak shapes . We founThe different peak characteristics could reflect at least three basic mechanisms: different numbers of Klf9 molecules recruited, perhaps related to the number of Sp/Klf motifs; differences in the chromatin environment  that modulates accessibility of the locus; or local differences in the presence of protein interacting partners that may stabilize Klf9 association at the region. Peaks from Clusters 2 and 3 had more Sp/Klf motifs than peaks from Cluster 1 Fig. , which iKlf13, Limk1 and Klf16 upstream regions, but not by Mapk11. For the Klf13 promoter we showed that Sp/Klf motifs are required for Klf9-dependent transrepression. Although the Mapk11 region has five predicted Sp/Klf-like motifs, it lacks any copies of the Klf9 consensus motif. By contrast, the Klf13, Limk1 and Klf16 upstream regions that we investigated have 3, 4 and 6 Klf9 consensus motifs, respectively. Based on these findings we hypothesize that the Klf9 consensus motif is essential for mediating Klf9-dependent repression. The presence of multiple Sp/Klf motifs at most Klf9 peaks implies that such redundancy may be a feature of many, if not most, Klf9 genomic targets. These sites could also potentially act as binding sites for other Sp or Klf-like proteins, which suggests the potential for combinatorial regulation by a network of Klfs  promoter, pGL4.23 [Limk1] promoter, pGL4.23 [Klf16] promoter and pGL4.23 [Mapk11] promoter, respectively.We constructed a reporter plasmid pGL4.23-3xBTE) containing three tandem repeats of the Basic Transcription Element (BTE) [3-3xBTE cKlf13] promoter plasmid as template, we conducted site-directed mutagenesis of predicted Sp/Klf motifs using the Quikchange kit . We converted seven nucleotides within each Klf9 consensus sequence (described below) to thymidines. The Klf13 5″ flanking region contains six predicted Sp/Klf motifs. We first generated two vectors with mutations in individual Sp/Klf motifs #1 or 2 , and one double mutant . To generate a Klf13 promoter fragment with mutations in all six predicted Sp/Klf motifs we synthesized the entire 439 bp fragment corresponding to that in the pGL4.23 [Klf13] promoter with mutations in all six Sp/Klf motifs (Invitrogen) and subcloned this into pGL4.23 to create pGL4.23 [Klf13] promoter6mut. All oligonucleotides used for subcloning and site-directed mutagenesis are given in Additional file Using the pGL4.23 Klf promoter2 at 37 °C. This cell line was originally derived from mouse hippocampus using the Simian Virus 40 T antigen [Klf9] cell lines we seeded 5 × 106 cells in 100 mm plates and transfected them with 5 μg each of pCDNA4:TO-Klf9 and pCDNA6:TR (Invitrogen) using Fugene6 (Invitrogen). Twenty four hr after transfection we changed to selective medium containing 100 μg/ml zeocin plus 5 μg/ml blasticidin . Louis, MO) and passaging with 0.25% trypsin . We then seeded trypsinized cells in 6-well plates and expanded the clonal lines. We tested seven clonal HT22 [TR/TO-Klf9] lines for dox-inducible Klf9 mRNA by culturing them in 6-well plates and treating with vehicle (0.1% DMSO) or 1 μg/ml dox  for 8 h, after which cells were harvested for RNA isolation and analysis for Klf9 mRNA levels by RTqPCR (described below). To test if the stable cell lines expressed functional Klf9 we seeded cells at 5 × 104 per well in 24 well plates and co-transfected them with pGL4.23-3xBTE (200 μg) plus pRenilla (10 μg) . We treated transfected cells with vehicle or dox for 8 h and harvested for dual luciferase assay (Promega). As a control, we co-transfected parent HT22 cells with pGL4.23-3xBTE plus a Klf9 expression vector (pCS2-Klf9) to independently investigate the action of Klf9 on this reporter.We cultured HT22 cells (gift of Dr. David Schubert) in high-glucose DMEM (Invitrogen) supplemented with 10% fetal bovine serum , penicillin G (100 U/ml) and streptomycin sulfate (100 μg/ml) under a humidified atmosphere of 5% CO antigen . To gene5 or 1.25 × 105, respectively, and began treatment with dox 24 h after plating. We then harvested cells and extracted RNA at different times after dox treatment for analysis by RTqPCR.For gene expression analysis we plated HT22 cells in either 6- or 12-well plates at densities of either 2.5 × 104 cells/well. Twenty-four hr after plating we transfected with 200 ng/well of the luciferase vector and 10 ng/well pRenilla plasmid to monitor transfection efficiency, and 24 h later we treated cells with vehicle or dox for 8 h (pGL4.23-3xBTE vector) or 24 h . We harvested cells for luciferase assay using the Dual Luciferase Reporter Assay System (Promega) according to the manufacturer’s instructions. Firefly luciferase activity was quantified using a luminometer  and normalized to Renilla luciferase activity. All transfection reporter assays were repeated at least two times with 5–6 replicates/treatment.For luciferase assays, we cultured cells in 24-well plates at a density of 5 × 10Gapdh and Klf9 [Gapdh whose mRNA was unaffected by treatments (data not shown.)We extracted total RNA from HT22 cells or mouse brain . Twenty four hr after transfection we treated cells with 100 ug/ml zeocin plus 1 μg/ml puromycin. To make stably transfected HT22 cell lines that express only BirA (HT22 [BirA]) we transfected cells with 5 μg pEF1α-BirA. Twenty four hr after transfection we treated cells with 100 ug/ml zeocin. Following antibiotic selection for 5 days we isolated individual cells by trypsinization using cloning cylinders and subcultured them as described above.g. We quantified the protein concentration of the extracts using the Pierce assay (Thermo Scientific). We then fractionated 40 μg of nuclear protein per lane of a 10% SDS-PAGE gel, transferred proteins to nitrocellulose membrane and blocked the membrane with SuperBlock , or with PBS containing 5% bovine serum albumin, 10% normal goat serum, 0.5% Triton-X (for FLBIO-Klf9). To detect BirA-V5 we incubated the membrane with V5 antibody  overnight before washing and incubation with HRP-conjugated goat anti-rabbit secondary antibody for 1 h ; diluted 1:30,000). To detect FLBIO-Klf9 we incubated the membrane with Streptavidin-HRP  for 1 h. Immune or streptavidin-HRP complexes were revealed by chemiluminescence detection using Pierce ECL Substrate (ThermoFisher Scientific).To determine if the stably transfected cell lines expressed the FLBIO-Klf9 fusion protein and/or BirA-V5 we analyzed nuclear extracts by Western blotting. For nuclear extraction we cultured HT22 [BirA] or HT22 [BirA/FLBIO-Klf9] cells in 100-mm plates until they reached 95% confluency, collected cells with a cell scraper into hypotonic buffer , allowed them to swell for 30 min, then lysed them with a motorized homogenizer and added 0.1 volume of sucrose restore buffer . We isolated nuclei by centrifugation at 4000 × g for 15 min, removed the supernatant (the cytoplasmic fraction), resuspended nuclei in nuclear extraction buffer  and incubated on ice for 40 min with vortexing every 10 min, then centrifuged for 1 h at 100,000 × Klf9] line 2–1 with vehicle or dox for 8 h. We extracted total RNA using TRIzol reagent, then purified it using the QIAgen RNEasy kit. Library construction and next generation sequencing was done at the University of Michigan DNA Sequencing Core on twelve RNA samples (1 μg/sample) representing the four treatments (n = 3/treatment). The twelve samples were analyzed in two lanes using an Illumina 2000 HI-seq machine, which generated between 23,555,652 and 50,630,253 million reads per sample. The sequencing reads were de-multiplexed by the University of Michigan DNA Sequencing Core, evaluated and filtered using FastQC software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and aligned to the mouse genome (build mm8) using the program Bowtie  and HT22 [BirA/FLBIO-Klf9], and four ChSP DNA samples from each cell line. We ran 5 samples per lane which generated between 18,818,940 and 27,816,127 million reads per sample. We filtered the sequenced reads using FastQC and aligned them to the mouse mm8 genome using Bowtie . We thenKlf9 gene). We transfected the parent HT22 cell line with this vector, which expresses enhanced green fluorescent protein (EGFP), and conducted fluorescence-assisted-cell sorting (FACS) at the University of Michigan Flow Cytometry core to isolate EGFP positive cells. We isolated clonal lines of the FACS-sorted cells as described above, extracted genomic DNA and screened for mutations by direct DNA sequencing of PCR-amplified DNA corresponding to Klf9 exon 1 subcloned into the pGEM T-easy vector (Promega). We selected two Klf9-null lines , cultured them simultaneously with the parent HT22 line in 6-well tissue culture plates, harvested cells, extracted RNA and analyzed gene expression by RTqPCR.We constructed a guide RNA (gRNA)/Cas9 expression plasmid (OriGene) containing the gRNA sequence 5′ ggggcgctccggaagccgag 3′  containing 1% BSA. Cells were stained in darkness for 30 min at room temperature using FxCycle Violet (Thermo Fisher) diluted 1:1000 in DPBS, and analyzed using an Attune Cytometer (Applied Biosystems) at 405 nm wavelength. The relative proportion of cells in G1/G0 and M phases was determined using ModFit LT .We purchased C57/BL6 mice from Jackson Laboratories  and maintained them on a 12 L:12D photoperiod with food and water provided ad libitum. Animals were killed by rapid decapitation and a section of the brain that included the hippocampus was dissected and flash-frozen for subsequent chromatin or RNA extraction (see above). Mice null for Klf9 were bred from animals provided by Dr. Yoshiaki Fuji-Kuriyama and Dr. Frank Simmen  cells. (DOCX 46 kb)Additional file 3: Figure S2.Klf9 associates in chromatin at the region of the Klf13 promoter in HT22 cells. (TIF 3325 kb)Additional file 4: Figure S3.Comparison of computational approaches used to identify regions of Klf9 association in chromatin across the genome of HT22 cells. (TIF 29857 kb)Additional file 5: Table S2.Genomic locations of all Klf9 ChSP peaks identified in HT22 [BirA/FLBIO-Klf9] cells based on coordinates of the mouse genome mm10 build. (XLSX 316 kb)Additional file 6: Figure S4.Validation of regions of Klf9 association in chromatin in HT22 cells discovered by chromatin streptavidin precipitation sequencing, analyzed by targeted chromatin immunoprecipitation for Klf9. (TIF 3453 kb)Additional file 7: Figure S5.Analysis of genomic regions in HT22 cells and mouse hippocampus that lacked Klf9 peaks by chromatin streptavidin precipitation (ChSP) sequencing. (TIF 4729 kb)Additional file 8: Figure S6.Analysis of the distribution of mapped sequencing reads around transcription start sites (TSS) revealed a moderate bias towards regions immediately upstream of the TSSs. (TIF 13937 kb)Additional file 9: Table S3.List of all Sp/Klf sequences identified as enriched above background in Klf9 ChSP peaks in HT22 [BirA/FLBIO-Klf9] cells. (DOCX 112 kb)Additional file 10: Table S4.List of all DNA sequences found to be enriched above background at Klf9 ChSP peaks in HT22 [BirA/FLBIO-Klf9] cells. (DOCX 124 kb)Additional file 11: Figure S7.Quantification of peak shape parameters from each cluster identified using the computer program SIC-ChIP. (TIF 23137 kb)Additional file 12: Figure S8.A greater relative percentage of chromatin streptavidin precipitation (ChSP) sequencing peaks belonging to Clusters 2 and 3 are associated with genes repressed by Klf9 compared with peaks from Cluster 1. (TIF 3611 kb)Additional file 13: Table S5.Klf13, Klf16, Limk1 and Mapk11 into the pGL4.23 vector. (DOCX 15 kb)Subcloning of the 5′ upstream regions of Additional file 14: Figure S9.Validation of Klf9 association in chromatin in HT22 cells with the 5′ flanking regions of genes identified by chromatin streptavidin precipitation sequencing. (TIF 5685 kb)Additional file 15: Table S6.Klf9 gene mutations introduced into HT22 cells by CRISPR/Cas9 genome editing. (DOCX 13 kb)Description of Additional file 16: Table S7.List of all GO: PANTHER pathways enriched in genes with associated Klf9 ChSP peaks. (DOCX 16 kb)Additional file 17: Table S8.Genes with peaks from different clusters were subjected to pathway analysis using GeneCoDis. (DOCX 15 kb)Additional file 18: Table S9.Oligonucleotides used for reverse transcriptase quantitative PCR (RTqPCR), chromatin immunoprecipitation assays, subcloning and site-directed mutagenesis. (DOCX 14 kb)


Pseudomonassyringae and similar pathogens. Recently, we showed thatassociation of HrpL with a target promoter directly adjacent to thehrpL gene imposes negative autogenous control on its ownexpression level due to overlapping regulatory elements. Our results suggestthat by down-regulating T3SS function, this fine-tuning mechanism enablesP. syringae to minimise effector-mediated elicitation ofplant immunity.Microbes are responsible for over 10% of the global yield losses in staple cropssuch as wheat, rice, and maize. Understanding the decision-making strategiesthat enable bacterial plant pathogens to evade the host immune system and causedisease is essential for managing their ever growing threat to food security.Many utilise the needle-like type III secretion system (T3SS) to suppress plantimmunity, by injecting effector proteins that inhibit eukaryotic signallingpathways into the host cell cytoplasm. Plants can in turn evolve resistance tospecific pathogens via recognition and blocking of the T3SS effectors, soleading to an ongoing co-evolutionary ‘arms race’ between pathogen and hostpairs. The extracytoplasmic function sigma factor HrpL co-ordinates theexpression of the T3SS regulon in the leaf-dwelling  In a major subset of plant pathogens, theextra-cytoplasmic function (ECF) sigma factor HrpL co-regulates these 50 or so genes viaa conserved promoter motif known as the hrp-box. The expression of HrpLitself is dependent on the alternative sigma-54 (σ54), necessitating acomplex transcription initiation process: a hexameric bacterial enhancer binding protein(EBP) binds the promoter at a distal upstream activation sequence (UAS) before beingbrought into contact with the inactive RNA polymerase (RNAP)-σ54 holoenzymevia DNA looping. In P. syringae, transcription of hrpLis activated by a hetero-hexamer comprising the co-dependent EBPs, HrpR and HrpS, whichare thought to respond to currently unknown environmental signals. Located within thehrp/hrc gene cluster, the hrpLgene shares a short, divergent promoter region (approximately 200 nt) with theHrpL-activated hrpJ operon . Given that thehrpL UAS is located approximately 100-150 nt upstream of thetranscriptional start site, our objective was to determine whether the juxtaposition ofthe HrpRS and RNAP-HrpL binding sites leads to regulatory coupling between theexpression levels of hrpL and the hrpJ operon,specifically in the model P. syringae pathovar tomatoDC3000.The hrpL promoter (PhrpL) is subject to a strongnegative feedback, since its activity increased in a ΔhrpL deletionmutant. This observation was not unexpected given that a small regulatory proteinbelonging to the HrpL regulon, HrpV, is known to repress HrpS during activation ofPhrpL. However, the increase in PhrpL activity wasfar more exaggerated in the ΔhrpL mutant compared toΔhrpV, hinting at an additional layer of negative feedback in play,independent of HrpV. Surprisingly, when hrpL transcription wasreconstituted heterologously in E. coli, using plasmids carryinghrpRS and the GFP reporter fusion, HrpL was able to repress theactivity of its own promoter by approximately 90%. The fact that this occurredindependently of the wider DC3000-specific regulatory network led us to conclude thatHrpL is able to act sufficiently in negative autogenous control ofPhrpL.Data from a plasmid-borne GFP reporter fusion and RNA sequencing suggested that thehrp-box sequence and HrpLDNA-binding domain, we subsequently demonstrated that negative autogenous control byHrpL is dependent on its canonical sigma factor activity at the adjacenthrpJ promoter: only when both mutations were acting simultaneouslywas repression of PhrpL completely relieved. Given that the classicalECF sigma factor in E. coli, σE, permits the RNAP to occupyDNA upstream of the promoter element, we hypothesised that thePhrpJ-bound RNAP-HrpL holoenzyme, likewise, might overlap the distalregion of the PhrpL UAS and interfere with HrpRS function. In fact, ourExoIII footprint data suggested that RNAP-HrpL occupies a longer 130 nt stretch ofpromoter DNA, occluding completely the PhrpL UAS and partially the siteat which the Integration Host Factor (IHF) binds to induce DNA looping. Closerexamination of the distal-most region occupied by RNAP-HrpL revealed a near-consensus UPelement, an AT-rich sequence with which the C-terminal domain (CTD) of the RNAP αsubunit, separated by a flexible linker, can associate in E. coli. Wehave therefore proposed that the α-CTD is the mediator of negative autogenous control bythe RNAP-HrpL holoenzyme, although this has yet to be tested experimentally.Making complementary mutations in the 54-dependent transcription by other means isvery rare. Negative autogenous control by HrpL is therefore not only remarkable in termsof σ54-dependent systems, but, given that negative feedback is alsointroduced by the repressive function of HrpV on HrpS activity, further highlights theapparent importance of fine-tuning T3SS expression in DC3000. Previous studies ofnegative autogenous control, believed to affect 40% of transcription factors inE. coli, showed that it can provide synthetic gene circuits withrobustness and rapid response times. However, with this comes the potential fitness costof reduced transcription factor expression. We therefore explored a range of hypothesesthat might explain how decreased expression of the T3SS regulon conveys a positiveeffect on fitness. Feedback loops can influence the population-level behaviours ofpathogens during infection and indeed heterogeneity of T3SS gene expression has recentlybeen shown in P. syringae. However, our flow cytometry analyses did notsuggest that negative autogenous control impacts significantly on the T3SS expressionvariance across the population. Similarly, despite previous reports hinting atfunctional antagonism between the T3SS and the flagellum, another key determinant ofplant colonisation, our data did not evidence HrpL-dependent repression of motility.As strict EBP-dependent activation usually negates the need for additional regulatorycontrol, direct repression of σP. syringae,that colonise both susceptible hosts and resistant non-host species. A caveat ofhost-specificity, defined by highly-evolved effector repertoires, is susceptibility tothe adaptive immune responses of non-hosts. Given the observations that (i) manyP. syringae pathovars can colonise non-hosts asymptomatically and(ii) T3SS expression in DC3000 is induced non-specifically by a range of plant exudates,a T3SS-associated fitness trade-off between virulence and immune elicitation is entirelyplausible. Thus, when considering the meta-population of DC3000 cells dispersed across avariety of plant hosts, genotypes that impose negative feedback on HrpL expression mayexperience a positive fitness benefit compared to otherwise more virulent strains. Insupport of this model, although our genetic studies of negative autogenous control inculture medium do not directly speak to its relevance in planta,preliminary data suggest that negative feedback still acts upon PhrpLduring infection of the Arabidopsis thaliana host (our unpublishedwork).We instead propose a fitness model whereby an overall limitation of T3SS expression is anadvantageous strategy for host-specific pathogens, such as Using targeted multiple reaction monitoring mass spectrometry (MRM-MS) to determine therelative intracellular and extracellular abundances of four key T3SS-associated proteins, westudied the effect of modest increases in HrpL concentration on T3SS activity.Remarkably, only a 2-fold increase in HrpL was sufficient to compromise normal function,resulting in hyper-secretion of the pilus protein HrpA1 and intracellular accumulation,by more than 5-fold, of the three later substrates. We hypothesised that theaccumulation of T3SS substrates inside the cell signifies either (i) rate saturation ofthe translocation step, in which proteins are thought to pass through the pilus channelin an unfolded state, or (ii) an inability to correctly regulate the switch from pilustranslocation to substrate translocation, perhaps due to an imbalance in thestoichiometry of T3SS proteins. In the first scenario, the accumulation of surplus,non-secreted substrates might impose a metabolic cost to the cell, an idea supported bythe fact that ΔT3SS mutants have a growth advantage over wild-type cells in otherbacteria. In the second, the fact that HrpA1 is thought to be an elicitor of plantimmunity implies that its release from the cell, whether due to mechanical shearing ofexcess pilus or complete translocation, would be deleterious in the contexts of bothhost and non-host plants.The discovery of negative autogenous control by HrpL advances our knowledge of theregulatory system controlling T3SS gene expression in DC3000 and highlights theimportance of exploring the complexity that underlies otherwise well described geneticnetworks. New insights into the regulation of the T3SS and other bacterial virulencefactors promise to inform next-generation interventions for plant disease management. Inrecent years there has been growing interest in anti-virulence strategies as moreevolutionary stable alternatives to crop resistance breeding, whereby the suppression ofvirulence it supposed to impose a weaker selection pressure for bacterial resistancethan inhibiting host colonisation altogether. Promising reports suggest that the T3SScan be readily inhibited by a range of specific anti-virulence chemicals.


Stretched histone regions, such as super-enhancers and broad H3K4me3 domains, are associated with maintenance of cell identity and cancer. We connected super-enhancers and broad H3K4me3 domains in the K562 chronic myelogenous leukemia cell line as well as the MCF-7 breast cancer cell line with chromatin interactions. Super-enhancers and broad H3K4me3 domains showed higher association with chromatin interactions than their typical counterparts. Interestingly, we identified a subset of super-enhancers that overlap with broad H3K4me3 domains and show high association with cancer-associated genes including tumor suppressor genes. Besides cell lines, we could observe chromatin interactions by a Chromosome Conformation Capture (3C)-based method, in primary human samples. Several chromatin interactions involving super-enhancers and broad H3K4me3 domains are constitutive and can be found in both cancer and normal samples. Taken together, these results reveal a new layer of complexity in gene regulation by super-enhancers and broad H3K4me3 domains. Recent studies have suggested that broad tracts of histone modifications are associated with cancer and cell identity4. A recent study characterizing the epigenomic properties of super-enhancers in over 80 cell types showed that super-enhancers can be identified through H3K27ac marks, and they are associated with disease-associated genomic variants2. In addition, there have been reports that broad H3K4me3 domains are involved in increased transcription elongation and increased enhancer activity at tumor suppressor genes4. A major question in understanding super-enhancers and broad H3K4me3 domains is the genes they regulate, and if there are any similarities between the two types of broad domains.Cancer is one of the major causes of mortality and morbidity worldwide. Super-enhancers are large enhancer regions found to drive key oncogenic factors in multiple myeloma, lung cancer, and glioblastoma multiforme5. Several molecular and genetic approaches have been developed to study three-dimensional (3D) chromosome folding7. We exploited published chromatin interactions identified by Chromatin Interaction Analysis with Paired-End Tag Sequencing (ChIA-PET)9, which captures genome-wide chromatin interactions bound by a specific protein factor, for data analysis. Two regions of interest were validated using circular chromosome conformation capture (4C), which identifies genome regions that a specific viewpoint interacts with11.Chromatin interactions are two distal regions of the genome that are brought together in close spatial proximity by protein factors, and can play multiple roles in cancer12. The authors proposed that super-enhancers are wrapped within insulated domains marked by CTCF, together with one or two genes in most cases. However, it is not clear how the presence of super-enhancers involved in chromatin interactions affects their target genes within these insulated domains. Here, we asked how the looping of a super-enhancer to a gene is associated with the gene’s expression level and cell-type specificity.The relationship between chromatin interactions and super-enhancers has been studied in the context of cohesin-associated chromatin interactions identified by ChIA-PET13 in K562 chronic myelogenous leukemia cells and MCF-7 breast cancer cells. We associated super-enhancers and broad H3K4me3 domains with chromatin interactions and examined the differences between proximal and distal regulatory elements and their effects on transcription. We found that many proximal super-enhancers not only target oncogenes but also tumor suppressor genes. By comparing super-enhancers with broad H3K4me3 domains, we found that most proximal super-enhancers targeting tumor suppressor genes overlap broad H3K4me3 domains. We confirmed several chromatin interactions in clinical samples and found examples of constitutive transcriptional circuits between cancer and non-cancer samples. Taken together, our findings suggest a role for these broad elements in transcriptional regulation via chromatin interactions.In this analysis, we studied the transcription regulatory effects and cancer related roles of genes targeted by super-enhancers and broad H3K4me3 domains through proximity or transcription-associated chromatin interactions detected by RNA Polymerase II-associated ChIA-PET data14 following the procedure in Hnisz et al.2 with some variations   Fig. .16. We examined the capped analysis of gene expression data (CAGE) from the FANTOM 5 project18 to obtain the expression levels of genes across a wide range of cell types. We found that transcription of genes at proximal super-enhancers was more cell-type specific  from Pol II ChIA-PET data13 Fig. .19, a computational method that analyzed mutational signatures in tumor-normal pairs to predict how likely a gene is a tumor suppressor gene or an oncogene; (2) COSMIC Cancer Gene Census15.We annotated the genes using two different databases: (1) the lists of oncogenes and tumor suppressor genes from Tumor Suppressor and Oncogene (TUSON) ExplorerMany super-enhancers were associated with cancer-associated genes, including tumor suppressor genes and oncogenes, through both proximity and looping Fig. . SpecifiPOLR2A promoter and Pol II ChIP-Seq14, we validated several chromatin interactions from POLR2A promoter to other proximal super-enhancers and proximal typical enhancers which were also enriched in RNA polymerase II  and COSMIC census cancer genes (51 out of 64 in K562 cells) located at proximal super-enhancer are located at regions with both super-enhancers and broad H3K4me3 domains Figures  and S13cTaken together, these results suggest that the enrichment of proximal super-enhancers at tumor suppressor genes and COSMIC census cancer genes through proximity is mainly due to the co-localization with broad H3K4me3 domains, suggesting a leading role of broad H3K4me3 domains in regulating cancer associated genes in K562 and MCF-7 cells.MYC, and the other at tumor suppressor TP53 in K562 cells and in clinical samples. The EpiSwitch method follows similar steps as in conventional 3C, with several modifications such as including genetically modified TaqI for chromatin digestion and incubation with high efficiency DNA ligase to increase the efficiency of the method, as described previously26.We examined whether super-enhancer and broad H3K4me3 domain-associated chromatin interactions can be found in clinical samples. We used Oxford Biodynamics’ EpiSwitch™ platform to investigate two interaction regions, one at oncogene After validating the EpiSwitch method in K562 cells (Supplementary Text), we performed this method with peripheral blood samples and bone marrow samples. In the case of bone marrow samples, we obtained white blood cells from the buffy coat Fig. . The detMYC to a typical enhancer (bait-hit1), and another interaction connected MYC to a distal super-enhancer that overlapped with a broad H3K4me3 domain (bait-hit2)   Fig. . Within t2) Fig. . These cMYC and TP53 could be detected in bone marrow and peripheral blood samples , making it necessary to investigate large numbers of samples to dissect out the possible roles of chromatin interactions in health and disease.Super-enhancer-associated and broad H3K4me3 domain-associated chromatin interactions can be detected in human clinical samples. While many previous chromatin interaction analyses have employed cell line models, several groups are starting to explore chromatin interactions in patient cancers27. Detailed comparison of the chronic myelogenous leukemia patient samples with non-leukemic controls will be needed to characterize novel cancer-specific super-enhancer interaction circuits. Alterations in chromatin interactions might underlie the transition from chronic phase to accelerated phase and eventually to blast crisis in chronic myelogenous leukemia and would be valuable to study further.Chromatin interaction aberrations arising from genetic abnormalities have been observed previously in leukemiaComparison of the peripheral blood and bone marrow samples may reveal cell-type specific chromatin interactions. In particular, bone marrow cells tend to be less mature, while peripheral blood tends to comprise large population of granulocytes. Chromatin interaction analysis of additional patient samples, together with fractionation of blood samples, will be needed to explore these possibilities.In conclusion, this research sets the stage for further elucidation of super-enhancers and broad H3K4me3 domains involved in chromatin interactions, and investigation into the chromatin interaction profile of more patient samples, by revealing a new level of complexity in gene regulation.The list of ChIP-Seq libraries used in this study are detailed in Supplementary Table 14. The sequences were mapped to hg19 assembly using Bowtie2 with the option –n 1, indicating that we allowed 1 mismatch in the seed alignment. PCR duplicates were removed using ‘samtools rmdup‘. Reads that fall within the ENCODE consensus blacklisted regions were removed. The two replicates were then combined and peaks were called from the combined dataset using MACS2 (version 2.1.0.20150731) with option “-p 1e-8”. A custom script was used to stitch the peaks using 4 kb as a window, and compute the treatment and control signal for each stitched peak . The formula used to normalize and compute signal mimics that of ROSE package. The purpose of the custom script was to speed up the computation process. After the treatment and control signals calculated for each stitched peak, the R script from ROSE package was used to identify the set of super-enhancers. In summary, super-enhancers were identified as the highest regions of H3K27ac occupancy from ChIP-Seq data.K562 and MCF-7 H3K27ac ChIP-Seq and matched input raw sequencing data were obtained from ENCODENext, we assigned super-enhancers to genes. Gencode V19 annotation was used to obtain the set of transcription starting sites. Transcripts of biotypes Mt_rRNA, Mt_tRNA, Mt_tRNA_pseudogene, miRNA, miRNA_pseudogene, misc_RNA, misc_RNA_pseudogene, rRNA, rRNA-pseudogene, scRNA_pseudogene, snRNA, snRNA_pseudogene, snoRNA, snoRNA_pseudogene, tRNA, tRNA_pseudogene, tRNAscan, retained_intro, TEC, and disrupted_domain were removed. The stitched peaks that are within 4 kb of any transcription start site were annotated as proximal; the rest were termed distal.et al., using the genes with Tumor Suppressor and Oncogene Explorer (TUSON) p-value smaller than 0.0119.Oncogenes and tumor suppressor genes were annotated following Davoli Two sets of previous called chromatin interactions from saturated K562 and MCF-7 ChIA-PET datasets were obtained from GEO (GSM832465). Only chromatin interactions that were found in both replicates were used for the study. The overlap was found using the “pairToPair” command from BEDTools.http://fantom.gsc.riken.jp/5/). The grouping facets were obtained from Andersson et al.18. The expression of a cluster in a facet was obtained by taking the mean of the cluster’s expressions in all the samples assigned to that facet. We obtained 367 samples (including 4 K562 samples), and grouped them into 70 facets with K562 as an independent facet. In other words, we obtained the expression levels of genes from different cell types from the CAGE data, including K562 expression levels. This was used to calculate the expression level and cell-specificity of the genes associated with super-enhancers and typical enhancers.Annotated CAGE clusters with normalized expression values were downloaded from the FANTOM 5 project website 14 using MACS223 (version 2.1.0.20150731) with –broad option. The “gappedPeak” file was used in the subsequent analysis. The flanking “broad” regions were trimmed off. The resulting gapped peaks were then ranked by size and top 5% were taken as broad H3K4me3 domains, others as typical H3K4me3 domains. These domains were then separated into proximal and distal based on whether they are within 4 kb of any transcription starting sites.Broad peaks were called from K562 and MCF-7 H3K4me3 ChIP-Seq data downloaded from ENCODEThe H3K27ac enhancers and H3K4me3 domains produced were merged. The resulting regions with broad H3K4me3 domains and super-enhancers are annotated as SE_BD regions, those with only super-enhancers were annotated as SE_O regions, those with only broad H3K4me3 domains were annotated as BD_O regions, and the others were annotated as O_O regions. For SE_BD, SE_O and BD_O regions, those contain at least one proximal broad H3K4me3 domain or one proximal super-enhancer are considered as proximal and others are distal. For O_O regions, those contain at least one proximal typical enhancer or one proximal typical H3K4me3 domain were considered as proximal and the others were distal.For the enrichment analysis on remote targets through chromatin interactions, the total number of each type of regions considered were those involved in chromatin interactions. For the enrichment analysis on proximal targets, the total number of each type of regions were the actual number of the regions. As the list of housekeeping genes contains 3804 genes, we repeatedly sampled 500 genes from the list and make the counts for 1000 times. The average counts were used as the numbers for housekeeping genes. The enrichment analyses were performed using Fisher’s Exact Test followed by Holm-Šídák correction.2 with 1x penicillin-streptamycin.K562 cells were a kind gift from Daniel Tenen’s lab in the Cancer Science Institute. They were cultured in RPMI-1640 supplemented with 10% FBS at 37 °C, 5% CO29. In brief, 4 × 107 cells were harvested and crosslinked with 1% formaldehyde for 10 min at room temperature with rotation. The crosslinking was quenched by glycine for 5 min at room temperature with rotation. Following SDS and Triton X-100 permeabilization, nuclei were digested with HindIII-HF (NEB) overnight. Following proximity ligation, reverse cross-linking and DNA purification, the circular DNA was digested with DpnII (NEB) 37 °C overnight, and circularized. The 4C-seq library was generated by performing nested inverse PCR using Phusion DNA polymerase (Thermo Scientific) with the primers listed in Table st PCR was: 98 g condition of the 1inked with 1% formaldehyde for 10 min at room temperature with rotation. The crosslinking was quenchension at 72 °C for 60 s. 10% of the 1st PCR product was used for the 2nd PCR. The cycling condition for the 2nd PCR was: 98cling condition for the 2 with 1% formaldehyde for 10 min at room temperature with rotation. The crosslinking was quenchension at 72 °C 72 °C for 60 s. The 4C-seq library was purified by 4–20% gradient TBE PAGE gel (ThermoFisher Scientific) and the smear band regions including the expected sizes were excised. The library was recovered by incubating the shattered gel slice with 200 uL TE buffer overnight at 37 °C and the DNA in the supernatant was ethanol precipitated in presence of GlycoBlue (ThermoFisher Scientific). The multiplex 4C-seq library was pooled in equal molar ratio and sequenced on MiSeq (Illumina) with 2X250 bp or 1X150 bp. 500,000–1,000,000 reads were produced for each library. The viewpoint sequence was removed by Tagdust2 and the processed read was mapped by BOWTIE2 and over 80% of each library could be mapped to the hg19 genome. The mapped 4C-seq data was analysed by r3CSeq30.4C-seq was performed as previously described with some modifications2 million cells were fixed with 1% formaldehyde in PBS for 10 minutes at room temperature. The fixation reaction was quenched by adding glycine to 0.25 M final concentration. The cell pellet was washed twice by cold PBS supplemented with 1 mM PMSF. The fixed cell was lysed in 100uL lysis buffer  for 10 minutes on ice. The cell lysate were sonicated by Bioruptor (Diagenode) until 100 bp to 500 bp DNA fragment were obtained. The cell debris was cleared by spinning the sonicated lysate at 15000 RPM for 10 minutes at 4 °C. The lysate was diluted 10 times by dilution buffer  and precleared by adding 100 uL of Protein A Dynabead (Thermo Scientific) and incubated at 4 °C for 2 hours. Antibody conjugated beads were prepared by adding 50 uL of antibody or IgG  to 50 uL Protein A Dynabeads in PBS/0.1% TX-100 and incubated at room temperature for 2 hours. The prepared antibody conjugated beads were added into 500 uL preclear lysate and incubated at 4 °C overnight. The beads were washed once with low salt washing buffer , twice with high salt washing buffer , twice with LiCl washing buffer  and twice with TE buffer . The DNA was eluted and decrosslinked by incubating the bead with Elution Buffer  for 6 hours at 65 °C. The eluted DNA was purified by phenol-chloroform and precipitated with ethanol in the presence of glycogen. The ChIP-qPCR reactions were prepared using Gotaq qPCR Master Mix (Promega) and run on ABI 7500 Fast in duplicates. Antibodies used included normal rabbit IgG, sc-2027X from Santa Cruz and rabbit polyclonal H3K27ac ab4729 from Abcam. The primers used are listed in Table CML and non-CML patient samples were collected under Institute Review Board-approved procedures  as part of the Cancer Science Institute Leukemia Cell Bank headed by Prof. Chng Wee Joo. Knee bone aspirate samples were collected under NHG Domain Specific Review Board-approved procedures . Informed consent was obtained from all subjects.Blood from completely normal individuals was not collected and stored as part of the bank. Blood from CML patients was collected. Blood from patients without CML (“non-CML”) but who had other diseases that were not expected to affect the blood was collected. Adult blood in the form of bone marrow (BM) and peripheral blood (PB) in EDTA was diluted in 1:1 ratio of Hank’s Balance Salt Solution (HBSS) or Phosphate Buffered Saline (PBS). Diluted BM and PB samples were layered on Ficoll paque medium in 1:1 ratio and centrifuged at 2000 RPM for 30 minutes with low deceleration. The mononuclear layer was collected and washed twice with 10 mL HBSS or PBS and centrifuged at 1500 RPM for 5 minutes. Upon the last wash, sample pellets were resuspended with cold freezing media (10% Dimethyl sulfoxide (DMSO), 90% Fetal Calf Serum (FCS)) in a ratio of 1 mL freezing media:10 million cells. Sample resuspension was performed in ice-water bath and all samples were subsequently transferred to cryovials and stored in liquid nitrogen for long-term storage.Knee bone aspirate collected from normal individuals was resuspended in 50 ml PBS containing 10% FCS, 0.4% Sodium Citrate and 3 mM EDTA, and homogenized with a 5 ml syringe and 21 G needle. Homogenized samples were passed through a 100 μm cell strainer and pelleted at 300 × g for 10 minutes. Samples were then lysed in 50 ml lysis buffer  following a 5-minute incubation at room temperature. After 5 minutes, blood cells were pelleted at 300 × g for 10 minutes at room temperature and resuspended in 13 ml PBS containing 2% FBS and 2 mM EDTA. Samples were then layered on Ficoll-Paque Plus in 6 ml cell suspension: 4 ml Ficoll ratio and centrifuged at 1000 × g for 20 minutes. Mononuclear layer was collected and washed twice with 10 mL PBS containing 2% FBS and 2 mM EDTA and once with PBS alone. Upon the last wash, knee BM samples were pelleted at 300 × g for 10 minutes and resuspended with cold freezing media  and transferred to cryovials, stored in liquid nitrogen for long-term storage. All patient sample information is listed in Table K562 cells were maintained for 1 week before 3–5 million cells were rinsed twice with PBS at room temperature and subsequently resuspended in PBS containing 10% glycerol, transferred to cryovials and stored in liquid nitrogen until shipping. Patient samples were directly shipped on dry ice to Oxford BioDynamics Plc.26. Briefly, following washes with PBS, cells were fixed with formaldehyde for 15 minutes and quenched with glycine. Cell lysis was performed and the nuclei was purified using density cushion centrifugation. TaqI and proximity ligation was performed using T4 DNA ligase, followed by subsequent proteinase K and RNAse A treatment to remove protein and RNA content. Upon DNA extraction from the samples, nested PCR was performed using primers listed in Table All samples were processed for chromosome conformation capture analysis as per manufacturer’s instructions using EpiSwitch™ proprietary reagents and procedure developed for blood, tissue and cell lines analysis  as described previously” GX from Perkin Elmer, using the LabChip DNA 1 K Version2 kit  and internal DNA marker was loaded on the DNA chip according to the manufacturer’s protocol using fluorescent dyes. Fluorescence was detected by laser and electropherogram read-outs translated into a simulated band on gel picture using the instrument software. The threshold we set for a band to be deemed positive was 30 fluorescence units and above. The EpiSwitch™ bait and hit regions are described in Figure All PCR amplified samples were then visualized by electrophoresis in the LabChipTotal RNA and genomic DNA were isolated from patient blood samples using the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen), incorporating on-column DNase and RNase digestions (Qiagen). Extracted RNA samples were analyzed on the Agilent 2100 bioanalyzer using the Agilent RNA 6000 Nano Kit (Agilent) while extracted DNA samples were separated on a 0.4% (w/v) 1x Tris/Acetate/EDTA buffered agarose gel to check for the quality of the extracted samples.c-MYC were normalized by the Ct values of β-actin and the fold difference in expression levels was calculated using the 2−ΔΔCt method. The primers used are listed in Table cDNA was reverse transcribed from 500 ng total RNA extracted from the patient blood samples using the SuperScript III First-Strand Synthesis System using oligodT (Invitrogen). Quantitative real-time PCR (qPCR) was performed with 50 ng of reverse transcribed cDNA using the GoTaq qPCR Master Mix (Promega). All qPCR reactions were prepared in duplicates and analysed by the LightCycler 96 System (Roche) and quantitated using the Abs Quant/Fit Point analysis software (Roche). The mean expression levels of the threshold cycle number (Ct) of The Bait-Hit 1 region, Hotspot 1 region and Hotspot 2 region were amplified from extracted genomic DNA samples using the Phusion High-Fidelity PCR Master Mix (NEB) and sent for Sanger sequencing at Axil Scientific Pte Ltd. The raw data obtained was analysed using the Chromas Lite software v2.1.1 to look for mutations. The primers used are listed in Table https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=yfkjgggozfgpryv&acc=GSE77125).The list of ChIP-Seq libraries used in the study were detailed in Supplementary Table All methods were carried out in accordance with relevant guidelines and regulations.Supplementary MaterialsSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4