Heterochromatin is a constituent of eukaryotic genomes with functions spanning from gene expression silencing to constraining DNA replication and repair. Inside the nucleus, heterochromatin segregates spatially from euchromatin and is localized preferentially toward the nuclear periphery and surrounding the nucleolus. Despite being an abundant nuclear compartment, little is known about how heterochromatin regulates and participates in the mechanisms driving genome organization. Here, we review pioneer and recent evidence that explores the functional role of heterochromatin in the formation of distinct chromatin compartments and how failure of the molecular mechanisms forming heterochromatin leads to disarray of genome conformation and disease. While working on cytological preparations of liverwort chromosomes, botanist Emil Heitz coined the term ‘heterochromatin’ to distinguish regions that remained strongly stained throughout the cell cycle from those that became invisible during interphase . Due to white gene expression inactivation, without alterations in the gene sequence , which have an important role delimiting functional interactions between distant regulatory elements and genes regulating their expression .However, there are still many questions on how heterochromatin can restrain or promote specific DNA interactions and contribute to the formation of distinct chromatin domains and compartments. In this review, we address stimulating current evidence that implies an active role of heterochromatin in 3D genome organization establishment and maintenance and the proposed molecular mechanisms of heterochromatin mediated structure in health and disease.Drosophila and Clr4 in yeast  and S. pombe (Swi6) are composed by two domains separated by a hinge region: an N-terminal chromodomain that binds H3K9me3 and a C-terminal chromo-shadow domain that serves as a platform for HP1 dimerization and binding of numerous chromatin-modifying proteins and components of the nuclear envelope thus promoting heterochromatin spreading through large domains  refers to condensed regions that are consistently silenced in all cell types of an organism and comprises pericentromeric and telomeric repeated sequences, transposons and some gene-poor regions of the genome. CH is molecularly defined by the presence of H3K9me3, a modification carried out by the histone methyltransferases (HMT) Suv39h in mammals, Su(var)3-9 in in yeast . These Hin yeast . Heteroc domains .Facultative heterochromatin (FH) consists of cell-type-specific heterochromatic regions that retain their potential to switch into euchromatin under certain cues and is frequently present at developmental genes. FH is marked by the presence of the Polycomb repressive complexes 1 and 2 (PRC1 and PRC2), the latter being responsible for the deposition of H3K27me3, a histone mark associated with FH .S. pombe and the long non-coding RNA Xist from the mammalian X chromosome are required for gene inactivation  and Nucleolus-Associated Domains (NADs) respectively and suggest a relationship between heterochromatin positioning and gene expression regulation.LADs are defined as chromatin regions associated with components of the nuclear envelope that tether them to the nuclear periphery. They are rich in H3K9me2/3 and have low gene density and/or lowly expressed genes . In mammC. elegans CEC-4 protein localizes stably in the nuclear envelope and binds directly to H3K9me  and both Lamin A and B in mammals. In o H3K9me . Accordio H3K9me .Drosophila  that results in the formation of supramolecular liquid droplets  immersed in a different, more diluted phase , like oil droplets in water . ProteinDrosophila heterochromatin  is accompanied by large-scale rearrangements of heterochromatin positioning forming nuclear structures known as Senescence-Associated Heterochromatin Foci (SAHF) . SAHF arNotably, ChIP-seq experiments in growing and senescent cells showed that the distribution of H3K9me3 and H3K27me3 histone marks remain largely unchanged after OIS, thus SAFH formation reflects changes in the spatial positioning of pre-existing heterochromatin . SAFH coHi-C experiments conducted in growing and senescent cells showed that although TADs are conserved after senescence induction, the domains within SAHF lose insulation strength and interactions inside the TADs are reduced whereas interactions between distant heterochromatic regions are enhanced, thus the regions contained inside the SAHF experience local remodeling of their interactions landscapes . WhetherOIS triggers extensive heterochromatin reorganization inside the nucleus and SAHF formation. Some cells are able to bypass senescence after SAHF disruption  and it hLaminopathies are a group of heterogeneous genetic diseases caused by mutations of the genes encoding nuclear lamins that cause lamin mislocalization, abnormal nuclear morphology and defects in chromatin organization which has led to the postulation of distinct non-mutually exclusive structural and transcriptional mechanisms responsible for laminopathies .LMNA that results in a form of Lamin A with an internal deletion of 50 amino acids (Heterochromatin detaches from the nuclear lamina in cells derived from patients with Hutchinson-Gilford Progeria Syndome (HGPS), a premature aging disorder caused by a mutation in no acids . Furtherno acids .Hi-C matrices of HGPS cells show a striking global loss of chromatin compartments and segregation, in agreement with the absence of heterochromatic clusters observed under the microscope . InteresDrosophila S2 cells after siRNA-mediated knockdown of the B-type lamin Dm0, which causes detachment and transcriptional activation of LADs (LMNA in humans resulted in alterations in nuclear morphology and muscle defects resembling the phenotype of muscular laminopathies (Drosophila and mammals.Chromatin segregation is also affected in  of LADs . Expressopathies . This suBesides its role in gene expression silencing, heterochromatin plays an important role in 3D genome organization instructing the global positioning of the genome and the formation of chromatin compartments via strong interactions between heterochromatic regions and LLPS, though an extensive characterization of the factors able to induce and regulate heterochromatin LLPS is still needed. Heterochromatin factors also mediate long-range interactions independent of CTCF and cohesin, providing a mechanism of chromatin folding that regulates gene expression. Further work on the interplay between heterochromatin-driven organization and other known structural proteins may uncover new principles of genome organization that expand our current understanding of the forces driving chromatin segregation and structure.AP-P and MF-M designed the reviewe’s content. AP-P wrote the manuscript with help, comments and editing by MF-M. All authors contributed to the article and approved the submitted version.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.


BRD4 knockdown changes the distribution of P-TEFb among nuclear soluble and chromatin and downregulates the elongation activity of RNA polymerase II. Inhibition of JNK kinase but not other MAP kinases impedes the interactions between BRD4 and P-TEFb. RNA-seq and ChIP assays indicate that BRD4 both positively and negatively regulates gene transcription in cells treated with UV stress. These results reveal previously unrecognized dynamics of BRD4 and P-TEFb after UV stress and regulation of gene transcription by BRD4 acting as either activator or repressor in a context-dependent manner.The bromodomain-containing protein BRD4 has been thought to transmit epigenetic information across cell divisions by binding to both mitotic chromosomes and interphase chromatin. UV-released BRD4 mediates the recruitment of active P-TEFb to the promoter, which enhances transcriptional elongation. However, the dynamic associations between BRD4 and P-TEFb and BRD4-mediated gene regulation after UV stress are largely unknown. In this study, we found that BRD4 dissociates from chromatin within 30 min after UV treatment and thereafter recruits chromatin. However, P-TEFb binds tightly to chromatin right after UV treatment, suggesting that no interactions occur between BRD4 and P-TEFb within 30 min after UV stress.  During mitosis, most transcription factors are dissociated from chromosomes; however, BRD4 persistently associates with chromatin in interphase and chromosomes during mitosis to transmit epigenetic memory across cell divisions , indicatBRD4 has been reported to be a drug target for several cancers . Indeed,As DNA damage alters chromatin structure and activates the specific signaling pathways that block cell-cycle progression , DNA damBRD4 knockdown results in the higher level of P-TEFb in both nuclear soluble fraction and chromatin fraction. BRD4 knockdown reduces UV-stimulated elongating activity of RNA polymerase II. The JNK pathway is involved in the interactions between BRD4 and P-TEFb. Our data further indicated that BRD4 both positively and negatively regulates transcription of the specific genes in a context-dependent manner after UV stress.In this study, we investigated the dynamic associations of BRD4 and its associated protein complex P-TEFb with chromatin after UV stress. We observed that BRD4 dissociates from chromatin within 30 min after UV treatment and then recruits chromatin later on. However, P-TEFb binds tightly to chromatin immediately after UV treatment. in vivo, we performed differential salt extraction experiments using concentrations from 50 to 200 mM. After a preliminary screen, we chose NaCl at 100, 150, and 200 mM for further investigation  and other reagents  have been used to monitor the binding of proteins to chromatin . To obtatigation . Our resWe took advantage of our established protein isolation condition and investigated the dynamic distributions of BRD4, P-TEFb, RNAPII, and phosphorylated CTD at serine (Ser) 2 and Ser 5 of RNAPII. We observed an increase in BRD4 in the nuclear soluble fraction and a decrease in BRD4 in the chromatin fraction at 30 min after UV treatment, indicating that UV stress disrupts the association between BRD4 and chromatin within 30 min . InteresThe CTD of the largest subunit of RNAPII plays a critical role in gene regulation and is composed of multiple heptad repeats , each containing two main phospho-acceptor sites: Ser2 and Ser5. Phosphorylation of Ser5 occurs primarily at the 5′ region of genes, whereas Ser2 phosphorylation accumulates on the elongating polymerase and therefore is more abundant at the 3′ region of genes . We founin vivo  levels in the chromatin fraction in BRD4-depleted cells were higher than those in control shRNA-treated cells after UV treatment. However, compared with control shRNA-treated cells, P-TEFb levels in the chromatin fraction in the BRD4-depleted cells was not significantly increased from 30 min to 2 h after UV treatment . UV-responsive genes were identified by comparing RNA-seq data between UV-treated and untreated cells (GSE137850). Genes with fold change higher than 1.5-fold were regarded as significant and defined as UV-responsive genes . To explTo further investigate BRD4-regulated UV-responsive genes, hierarchical clustering was performed . In geneGATA3 that was upregulated after UV treatment and downregulated by BRD4 knockdown  into both the nuclear soluble and chromatin fractions after ol cells . As the BRD4 is released from chromosomes upon exposure to anti-mitotic drugs in a manner dependent on the activation of the JNK pathway . The JNKin vitro  and gene bodies (Ser2-CTD) was not influenced by BRD4 knockdown (C. elegans (Although the binding of P-TEFb onto BRD4-negatively regulated gene nockdown . RNAPII  elegans . It will2 humidified incubator at 37°C.HeLa-S3 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin (Invitrogen). The cells were maintained in a 5% COThe following antibodies were used in this study: polyclonal anti-rabbit against BRD4 (ab75898), polyclonal anti-rabbit against RNA polymerase II , polyclonal anti-rabbit against RNA polymerase II CTD repeat YSPTSPS (phospho S5) (ab5131), polyclonal anti-rabbit against RNA polymerase II CTD repeat YSPTSPS (phospho S2) (ab5095), polyclonal anti-rabbit against CDK9 , polyclonal anti-rabbit against Cyclin T1 , polyclonal anti-rabbit against HEXIM1 (ab25388), polyclonal anti-rabbit against Histone H3 (ab1791), and monoclonal anti-mouse against TFIIB (ab819).BRD4, pLKO.1-MAPK1, pLKO.1-MAPK8, pLKO.1-MAPK14), as well as two helper vectors, cytomegalovirus (CMV) R8.91 and VSV-G, were transiently transfected into 6 × 106 cells/10 cm dish of 293T cells using Lipofectamine 2000. At 12 h after transfection, fresh medium was added. Viral supernatants were collected, filtered, and used to infect target cells after 48 h. The primers used for constructs are shown in A recombinant construct  for 10 min on ice. The cells were pelleted by centrifuging at 1200 rpm for 5 min and resuspended in 200 μl of buffer A. 2.5 μl of 10% NP-40 was added. The pellet was resuspended by pipetting up and down and centrifuged for 5 min at 2000 rpm. The pelleted nuclei were resuspended in saponin buffer  for swelling on ice for 5 min. The nuclear soluble fraction was collected by centrifuging at 2000 rpm for 5 min. The pellets were resuspended in buffer C  supplemented with 0.05% NP-40. The sample was stirred for 30 min at cold room. The chromatin-bound proteins were collected by centrifuging at 13,000 rpm for 5 min.Cells were washed with PBS, scraped, and transferred to a Falcon tube. PBS was removed, and the cells were resuspended and incubated in 800 μl of buffer A , and the bound materials were eluted by FLAG peptides or in a sample buffer. The separated protein fractions were subjected to SDS-PAGE and transferred to a PVDF membrane (Invitrogen). Immunoblotting was performed with the indicated antibodies.Nuclear extract (NE) (600 μg) from cells overexpressing either Flag or Flag-BRD4 shRNA were treated with 60 J/cm2 ultraviolet (UV) light. The cultured cells were collected at 0, 0.5, 1, and 2 h after UV treatment. Then, the cells were harvested and dissolved in Trizol for total RNA extraction and treated with DNase I (Ambion) to remove any potential contaminated DNA fraction. The following library generation and RNA-seq were conducted by WuXi AppTec and RiboBio. The reads were aligned to human hg19 genome transcriptome (Ensembl v73) using RSEM    with par(v2.2.5) . Only un(v2.2.5)  using th(v2.2.5) .Total RNA was extracted from cells using the Trizol reagent (Invitrogen), and RNA was treated with DNase I (Ambion) for 30 min at 37°C. cDNA was synthesized using random primer and reverse transcriptase (Toyobo). cDNA amplification was monitored with SYBR premix Ex Taq (TAKARA) in a CFX96 real-time PCR system (BIO-RAD) according to the manufacturer’s instructions. The transcript levels were normalized to HPRT1. The primers used for qRT-PCR are shown in 6) were cross-linked with 1% paraformaldehyde for 10 min at 37°C and lysed in the buffer containing 50 mM Tris (pH 8.0), 10 mM EDTA, 1% SDS, and protease inhibitors. Chromatin was sheared by sonication to generate 200- to 1000-bp DNA fragments and used for immunoprecipitation steps. The indicated antibodies were incubated with Dynabeads A/G (Invitrogen) with rotation for 30 min at room temperature. The chromatin preparations were then incubated with the complex of antibody/magnetic beads overnight at 4°C. The immune complexes were washed twice each with low-salt buffer, high-salt buffer, LiCl buffer, and TE buffer. Antibody-bound chromatin was reverse-crosslinked. The ChIP DNA samples were purified and used for qPCR. The PCR primers used for ChIP are listed in ChIP was performed using the indicated antibodies. Briefly, cells (1 × 10GSE63385.RNA-seq data described in this study have been deposited at the Gene Expression Omnibus (GEO) under accession cGSE63385.The dataset presented in this study can be found in an online repository. The name of the repository and accession number can be found below: Gene Expression Omnibus (GEO) – HY initiated the study and designed the experiments and conceived and supervised the entire study. YS conducted most of the experiments. GH and JJ performed the bioinformatics analysis. MY, XW, WL, AH, JC, and KO contributed to the work. HY, YS, and GH wrote the manuscript. All authors contributed to the article and approved the submitted version.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.


Pelteobagrus fulvidraco, a widely distributed freshwater teleost. We successfully obtained 1981 and 2034 bp sequences of sf-1 and cyp17α promoters, and predicted the putative binding sites of several transcription factors, such as Peroxisome proliferator-activated receptor alpha (PPARα), Peroxisome proliferator-activated receptor gamma (PPARγ) and Signal transducer and activator of transcription 3 (STAT3), on sf-1 and cyp17α promoter regions, respectively. Overexpression of PPARγ significantly increased the activities of sf-1 and cyp17α promoters, but overexpression of PPARα significantly decreased the promoter activities of sf-1 and cyp17α. Overexpression of STAT3 reduced the activity of the sf-1 promoter but increased the activity of the cyp17α promoter. The analysis of site-mutation and electrophoretic mobility shift assay suggested that the sf-1 promoter possessed the STAT3 binding site, but did not the PPARα or PPARγ binding sites. In contrast, only the PPARγ site, not PPARα or STAT3 sites, was functional with the cyp17α promoter. Leptin significantly increased sf-1 promoter activity, but the mutation of STAT3 and PPARγ sites decreased leptin-induced activation of sf-1 promoter. Our findings offered the novel insights into the transcriptional regulation of sf-1 and cyp17α and suggested leptin regulated sf-1 promoter activity through STAT3 site in yellow catfish.The present study was performed to clone and characterize the structures and functions of steroidogenic factor 1 (sf-1) and 17α-hydroxylase/lyase (cyp17α) promoters in yellow catfish  Steroid hormones are involved in the regulation of many physiological processes, such as embryonic development, sex differentiation, metabolism and reproduction in vertebrates , and thePeroxisome proliferator-activated receptor alpha and gamma (PPARα and PPARγ) are the two important transcription factors that modulate the expression of many target genes involved in numerous physiological processes, including steroidogenesis ,11,12. CLeptin belongs to a member of the cytokines and plays a vital role in reproduction . Leptin Pelteobagrus fulvidraco, an omnivorous freshwater teleost, has been cultured widely in China and other Asian countries due to the successful technology of artificial propagation. However, further investigation remained into the regulation of steroidogenesis during the propagation. It is well-known that the application of luciferase assay is a well-established technique in molecular biology for analysis of cloned promoter DNA fragments [Yellow catfish ragments ,25. ThusIn the present study, the transcription start sites (TSS) of sf-1 and cyp17α were identified, and 1981 bp and 2034 bp sequences were cloned for sf-1 and cyp17α promoters, respectively. Several core promoter elements closed to TSS were identified on the sf-1 promoter, such as one CCAAT box from −38 bp to −27 bp and three SP1 binding sites located at−130 bp to −120 bp, −118 bp to −108 bp and −16 bp to −7 bp, respectively . One PPAOn the cyp17α promoter, we predicted one TATA box located from −150 bp to −136bp, one GC box located at −55 bp to −42 bp and one SP1 binding site located at −53 bp to -43 bp, one PPARα binding site (at −881 bp/−864 bp), one PPARγ binding site (at −89 bp/−70 bp) and one STAT3 binding site (at −1669 bp/ −1660 bp), respectively .Deletion of the sequences from −1981 bp to −1421 bp of sf-1 promoter significantly enhanced the relative luciferase activity. There is no significant difference after the deletion of the sequences from −1421 bp to −921 bp. The deletion of the sequences from −921 bp to −489 bp significantly reduced the relative luciferase activity of sf-1 promoter A. DeletiTo reveal the mechanism of PPARα, PPARγ, and STAT3 regulating sf-1 and cyp17α in yellow catfish, we transfected PPARα, PPARγ, and STAT3 plasmids into HEK293T cells for 24 h, and found that they were successfully over-expressed in HEK293T cells .To study the activities of sf-1 and cyp17α promoters induced by PPARα, PPARγ, and STAT3, we co-transfected PPARα, PPARγ, and STAT3 plasmids along with the promoter constructs into HEK293T cells for 24 h and performed the 5’-deletion assay of sf-1 and cyp17α promoters, respectively. Overexpression of PPARα resulted in a marked reduction in sf-1 promoter activity by 39%, compared to the control. The sequence deletion between −1981 bp and +254 bp presented no significant influences on PPARα-induced promoter activity, indicating that negative response element to PPARα existed on −489/+254 bp region of sf-1 promoter A. OverexOverexpression of PPARα markedly reduced the promoter activity of cyp17α compared to the control. The inhibitory effect by PPARα was completely abolished when the sequence was deleted from −1402 to −687 bp. However, deleting the sequence between −687 bp and −330 bp recovered the inhibitory effect by PPARα, indicating that there are two negative response elements at −1402/−687 bp and −330/+68 bp, and one positive response element at −687/−330 bp region of cyp17α promoter to PPARα A. ComparTo investigate the response of promoters induced by leptin, we used 200 ng/mL leptin to incubate HEK293T for 24 h and performed the 5′ deletion assays. Leptin incubation significantly increased sf-1 promoter activity. In the leptin-treated groups, the sequence deletion between −1981 bp and −921 bp of sf-1 promoter showed no significant influences on luciferase activity. However, further deletion from −921 to −489 bp significantly down-regulated leptin-induced sf-1 promoter activity A. LeptinTo further elucidate whether the regions of sf-1 and cyp17α promoters possessed PPARα, PPARγ, and STAT3 response elements, we performed the site-directed mutation at these regions of sf-1 and cyp17α promoters. Overexpressed PPARα reduced sf-1 promoter activity by 42% compared to the control, and its inhibitory effect was completely abolished when this site (−413/−396 bp) was mutated, suggesting that sf-1-PPARα (−413/−396 bp) site might inhibit PPARα-induced sf-1 transcription A. The sfOverexpressed PPARα significantly reduced the cyp17α promoter activity by 48% compared to the control, whereas mutation of cyp17α-PPARα (−881/−864 bp) site significantly enhanced sf-1 promoter activity, suggesting that cyp17α-PPARα site (−881/−864 bp) down-regulated PPARα-induced cyp17α transcription A. OverexTo further determine whether the leptin-induced increase in sf-1 expression could be mediated by the STAT3 or PPARγ, we performed the site-directed mutation at the regions of sf-1 promoter and used leptin to incubate. Leptin incubation significantly increased sf-1 promoter activity. However, compared to the wild type pGl3−1981/+254 vector, the leptin-induced stimulatory effect was completely abolished after the mutation of sf-1-PPARγ (−1396/−1388 bp) site, sf-1−STAT3 (−1106/−1096 bp) site or sf-1-PPARγ&STAT3 (−1396/−1388 bp and −1106/−1096 bp) sites, respectively .We further used EMSA assays to explore whether PPARα, PPARγ, and STAT3 directly interact with sf-1 and cyp17α promoters. We made a series of probes by using biotin to label the sf-1-PPARα (−413/−396 bp), sf-1-PPARγ (−1396/−1388 bp), sf-1-STAT3 (−1106/−1096 bp), cyp17α-PPARα (−881/−864 bp), cyp17α-PPARγ (−89/−70 bp), and cyp17α-STAT3 (−1669/−1660 bp)-sequence, respectively. The results indicated that the sf-1-PPARα (−413/−396 bp) and sf-1-PPARγ (−1396/−1388 bp)-sequence did not compete for binding with the nuclear protein Lane 3, A,B, indiIn mammals, studies have indicated that the transcription of steroidogenic enzymes and steroid hormone receptors were mediated by transcription factor PPARs and STAT3 ,26, but In eukaryotes, the identification of the core promoter is the first step for exploring the mechanism of transcriptional initiation . In the Identification of transcription factor binding sites (TFBS) plays an important role in deciphering the mechanisms of gene regulation . PPARα aFish leptin is largely diverged from mammalian and it is well-known that the amino acid identity between yellow catfish leptin and human leptin is markedly low almost 23%). However, as far as we know, fish leptin is hardly available commercially at present. Therefore, recombinant human leptin has widely been used to investigate the function of fish leptin ,23,37,38%. HoweveIn summary, the present study cloned and identified the different structure and function of sf-1 and cyp17α promoters. We found that three transcription factors  regulated the transcription activities of sf-1 and cyp17α promoters and that the transcriptional activation of sf-1 was mediated by STAT3 and PPARγ under leptin signal.Ethical statement: All animal experiments followed the ethical guidelines of Huazhong Agricultural University (HZAU) and were approved by the Animal Experimentation Ethics Committee of our university  .The yellow catfish individuals used for DNA and RNA extraction were bought from a local commercial farm . HEK293T cell lines were obtained from the Cell Resource Center of Fishery College of HZAU. Dulbecco’s Modified Eagles Medium (DMEM), fetal bovine serum (FBS) and 0.25% trypsin-EDTA were purchased from Gibco (USA). Recombinant human leptin (HPLC class), Penicillin, streptomycin and other reagents were purchased from Sigma (USA).We identified the 5’ complementary DNA (cDNA) sequences and the (TSS) of sf-1 and cyp17α genes of yellow catfish by RNA ligase-mediated rapid amplification of 5’cDNA ends (RLM-5′RACE) method. The protocols of promoter cloning following those in Xu et al. . For genOn the basic of the distance from its TSS, we named the plasmids as pGl3−1981/+254 sf-1 promoter and pGl3−2034/+68 cyp17α promoter, respectively. Using the templates of pGl3−1981/+254 of sf-1 vector, we produced the plasmids of pGl3−489/+254, pGl3−921/+254, pGl3−1421/+254, and pGl3−1981/+254 of sf-1. Similarly, the plasmids of pGl3−330/+68, pGl3−687/+68, pGl3−1402/+68, and pGl3−2034/+68 of cyp17α were generated through using pGl3−2034/+68 of cyp17α vector as the template.http://www.genomatix.de/) and the JASPAR database (http://jaspar.genereg.net/). We conducted sequence alignments with the Clustal-W multiple alignment algorithm.We compared nucleotide sequences of sf-1 and cyp17α promoters with DNA sequences from the GenBank database. In order to analyze sf-1 and cyp17α promoters, we predicted putative TFBS by MatInspector online  to transiently transfect the plasmids into HEK293T cells following the manufacture’s protocols. We used all reporter plasmids in equimolar amounts in Opti-MEM , and 20 ng pRL-TK were co-transfected as the control. At 4 h after the transfection, we replaced the transfection medium by DMEM (10% FBS) with or without 200 ng/mL leptin. The leptin concentrations were selected after the publications in our laboratory ,37. AfteWe used western blot to determine the protein expression of yellow catfish PPARα, PPARγ, and STAT3 in HEK293T cells based on the methods described in our studies ,45. The To identify the corresponding binding sites on the regions of sf-1 and cyp17α promoters, we used QuickChange II Site-Directed Mutagenesis Kit  to perform site-directed mutagenesis analysis. pGl3-sf-1-1981 and pGl3-cyp17α-2034 were used as templates, respectively. The primers for mutagenesis are shown in We conducted EMSA assays to explore the functional binding sites of PPARα, PPARγ, and STAT3 on the regions of sf-1 and cyp17α promoters, based on the methods of Xu et al. . We tranp < 0.05.We used SPSS 19.0 software for all these statistical analysis. All of these data were expressed as means ± SEM (standard errors of means). Before statistical analysis, we evaluated all data for normality using the Shapiro–Wilk test. In order to test the homogeneity of variances, we performed Bartlett’s test. We analyzed data with one-way ANOVA and Duncan’s multiple or Student’s t-test where appropriate. Significant level was


NF-Y is a trimeric Transcription Factor -TF- which binds with high selectivity to the conserved CCAAT element. Individual ChIP-seq analysis as well as ENCODE have progressively identified locations shared by other TFs. Here, we have analyzed data introduced by ENCODE over the last five years in K562, HeLa-S3 and GM12878, including several chromatin features, as well RNA-seq profiling of HeLa cells after NF-Y inactivation. We double the number of sequence-specific TFs and co-factors reported. We catalogue them in 4 classes based on co-association criteria, infer target genes categorizations, identify positional bias of binding sites and gene expression changes. Larger and novel co-associations emerge, specifically concerning subunits of repressive complexes as well as RNA-binding proteins. On the one hand, these data better define NF-Y association with single members of major classes of TFs, on the other, they suggest that it might have a wider role in the control of mRNA production. The ongoing ENCODE consortium represents a useful compendium of locations of TFs, chromatin marks, gene expression data. In previous reports, we identified modules of CCAAT-binding NF-Y with individual TFs. Here, we analyzed all 363 factors currently present: 68 with enrichment of CCAAT in their locations, 38 with overlap of peaks. New sequence-specific TFs, co-activators and co-repressors are reported. Co-association patterns correspond to specific targeted genes categorizations and gene expression changes, as assessed by RNA-seq after NF-Y inactivation. These data widen and better define a coherent model of synergy of NF-Y with selected groups of TFs and co-factors. Eukaryotic genomes contain thousands of protein coding and non-coding genes, and the understanding of their expression is a central issue in biology. In fact, this process regulates development, differentiation and, in some cases, transformation of cells. Gene expression is initiated by production of primary RNAs by RNA Polymerases, RNA Pol II in the case of mRNAs and most regulatory non-coding RNAs. The first event driving transcriptional activation by RNA Polymerase II is the recognition and binding of Transcription Factors (TFs) to specific DNA regulatory elements (promoters and enhancers). At least two additional classes of proteins are minimally required: co-activators and General Transcription Factors (GTFs). While TFs and co-activators are selective for classes of promoters and enhancers, GTFs are believed to partake in the activation of all genes . At a hiThe CCAAT box is a widespread DNA element in mammalian promoters –5, with An effort to order functional elements of the human genome is made by the ongoing ENCODE consortium ,15. Thisin vitro studies [NF-YA and NF-YB genomic locations were analyzed by ENCODE in the Tier 1 K562, HeLa-S3 and GM12878 cell lines: in our initial report, location analysis was matched with partial characterization of co-association with 78 available TFs in K562 cells . The pea studies . Binding studies –19. Thes studies –23. The  studies ; (ii) Ch studies ; (iii) s studies ; (iv) st studies .In the second round of ENCODE analysis, we identified classes of TFs which bind the same promoters and enhancers, in some cases with a precise arrangement of sites . We cataIn this report, we pursued the studies on the NF-Y regulome on the ever-growing ENCODE datasets, incorporating expression and chromatin configuration data, as well as RNA-seq results generated by us after NF-Y inactivation.st 2019) as “bed narrowPeak” file type. Peaks available only on the hg38 assembly were converted to hg19, resulting in an initial number of 728 experiments. Since in some cases different experiments were available for the same TF, we filtered this initial dataset as follows. Duplicate experiments for the same TF in the same cell line were processed as previously described [We considered all available ENCODE ChIP-seq datasets from K562, GM12878 and HeLa-S3 cell lines. Coordinates of “Optimal IDR thresholded peaks” were retrieved from the ENCODE repository  or to binding sites of TFs co-associating with it across the genome.Local enrichment evaluates instead over-representation of TFBS motifs with respect to genomic regions flanking those derived from the ChIP-seq. In particular, the higher the probability to find the motif close to provided peak summits, the lower the obtained p-value. A globally enriched motif usually is locally enriched, as well. A motif locally but not globally enriched indicates the binding of a factor colocalizing with the one analyzed by ChIP-seq, but only in a limited subset of regions.−10, in order to keep only the most robust correlations. For experiments on the K562 cell line, the cell-specific background of PscanChIP was employed, while for GM12878 and HeLa-S3 cell, for which a cell specific background was not available, enrichment was assessed with respect to the “mixed background” option. Regions were scanned by PscanChIP with the JASPAR 2020 Redundant matrix collection, and the CCAAT-box matrix employed to evaluate its enrichment was MA0060.1, as in previous work [For both measures, the enrichment was considered significant when the relative p-value was lower than 10−10. Thereafter, each factor positive for the presence of CCAAT was first verified for the actual co-presence of NF-Y peaks, and then precise distances were computed using each of the corresponding matrices present in JASPAR 2020 Redundant version.PscanChIP predicts the presence of a positional bias between peak summits and the matrix of the factor (when available). We considered as positive scores those whose p-values lower than 1010 of the p-values was used for producing the clustered heatmaps. Overlaps with observed values lower than expected ones were further multiplied by -1, to distinguish between factors with overlap significantly higher, or lower, than expected in the presentation of data.The computation and statistical evaluation of peak overlap was performed as recently described . BrieflyOverlap regions between two factors within NF-Y peaks were computed with the same criterium as above, employing the peaks summits overlapping NF-YB for both factors and using the number of NF-YB peaks for the computation of the p-values associated with the overlap.10 of the p-value was greater than 100. In particular, we considered NF-YB significantly overlapping with a factor if more than 10% of its peaks are shared with the factor.Overlaps were finally considered significant when the -LogHeatmaps were obtained by clustering the log-transformed p-values, as described above, using Pearson correlations and the centroid method.−5. Enriched pathways with a p-value higher than 10−5, or a background number of genes higher than 200 were discarded. To reduce the redundancy of pathways and improve the legibility and interpretation of the resulting plot, pathways characterized by the same genes were merged into the most general one. The final plot was produced by employing, and custom editing, the UpsetR [Peaks summit of TFs of e UpsetR  package.Cell line-specific chromatin states employed were retrieved from the RoadMap Epigenome repository ; in partpybedtools, pandas and numpy, whereas R packages used were gplots, UpsetR, tidyverse collection and DESeq2. Conversion of coordinates between different genome assemblies was performed with the LiftOver tool available at the UCSC Genome Browser [Analyses were performed with both Python (2.7) and R (3.2.5) in-house scripts. Employed Python libraries were  Browser .6 cells/well were seeded in antibiotic-free medium in a 6-well plate. Cells at 25–30% confluence were transfected with 50 nM siRNA  using 3.75 μL Lipofectamine 3000 Reagent (ThermoFisher) in 1.5 mL final volume of Optimem (ThermoFischer) per well. 16 hours post-transfection, cells were detached by trypsin treatment, pooled and split in new wells. 72 hours post-transfection cells were harvested for protein extracts and RNA preparation. Three independent inactivation experiments were performed. The RNAs were isolated using TRI-reagent (Merck) and further purified with RNeasy Mini Kit (Qiagen) following the RNA clean-up protocol. RNAs were then quantified with Nanodrop and RNA integrity assessed with Agilent Tapestation. Total protein extracts were prepared in RIPA buffer and used for Western blotting. The membrane was probed with primary antibodies and secondary HRP-conjugated secondary antibodies (Sigma Aldrich). Primary antibodies: anti-NF-YA , anti-NF-YB (GeneSpin), and anti-Vinculin (Sigma Aldrich) as loading control.HeLa cells were grown in DMEM high glucose with L-glutamine (EuroClone) supplemented with 10% FBS, 100 U/mL penicillin and 100 μg/mL streptomycin. The day before transfection, 0.15 × 102FC|>1. Promoters of DEGs were further analysed for Transcription Factors Binding Sites (TFBS) enrichment using Pscan [2FC values of genes. In particular, for each of the two states (UP and DOWN) a pairwise analysis on a Fisher test was run given the number of genes regulated by two factors TF(a) and TF(b) and the total number of genes regulated by the single factor. The obtained p-values were -Log10 transformed and employed to build a heatmap by hierarchical clustering with Pearson distance and centroid as clustering method. RNA-seq raw data and processed data are available at GEO under the accession number GSE151237.Total RNAs were poly-T purified, randomly fragmented and transformed in cDNA with NEB library preparation protocol. Library preparation and sequencing were performed by Novogene. Sequencing was performed with the following requirements: paired end with 150 nt read length and at least 30 million tags for each sequencing. FASTQ files were retrieved, and sequencing quality was evaluated by FastQC software. Tags were mapped with RSEM 1.2.11 against human transcriptome (GRCh37/hg19). Differentially expressed genes (DEGs) were evaluated with DESeq2 , with thng Pscan  with theThe general workflow of our analysis of ENCODE data is outlined in Before we proceed with the description of the findings, a few additional issues need clarifications.www.encode.org. This also entails differences in the nomenclature of some of the TFs and in peaks distribution (number and locations) for most. While this does not change–and in most cases reinforces–the global picture of the 154 TFs previously analyzed, it does lead to the elimination of a few factors from the previous lists (See below).The ENCODE consortium has progressively fine-tuned processing of data: our previous analysis–as of October 2014 –was performed on the available datasets denoted as “SYDH” at the UCSC Genome Browser . SubsequENCODE contains ChIP-seq data of many cell types. NF-Y locations were analyzed in three cell lines, K562, GM12878 and HeLa-S3: thus, we initially considered only factors whose ChIP-seq data are available in these cells; thereafter, for all factors showing significant overlap with NF-Y in any of these lines, the CCAAT box was searched in the respective peaks for all other lines.ENCODE has also analyzed NF-YC in the hepatic HepG2 cells. We did analyze this dataset and came to the conclusion that CCAAT is indeed enriched in the peaks (Not shown). However, we were puzzled by the very high number of peaks, as compared to the Tier 1 lines, as well as to other NF-Y ChIP-seq experiments reported independently from the ENCODE consortium. Most importantly, parallel analysis of the TFs analyzed in HepG2 shows an overlap of peaks with the vast majority of the factors (~80%). This was in striking contrast to the observation previously–and currently–made in Tier 1 cell lines, for which the overlap is around 20%. For these reasons, we felt that further refinement is required for the NF-YC HepG2 data, as they currently stand, before we could include them in our pipeline.Having gathered data with these stringent criteria, we initially performed two types of experiments: (i) evaluation of the enrichment of the CCAAT matrix in the peaks of ChIP-seq experiments of all TFs and cofactors, using the PscanChIP software. This method classifies motif enrichment as “Global” or “Local” and signals a positional bias of motifs–CCAAT in this case–with respect to peaks summits . (ii) Coloci is proposed  family, such as ATF2/3/4 and MAFG/F; the related MAFK shows borderline overlap  Positional bias of some notorious interactors, such as FOS and JUNs, were apparently negative in a cell-type dependent way: for example, FOS was positive in K562, but not in HeLa-S3, and viceversa, JUND in HeLa-S3, but not in K562. This led us to evaluate the p-values of all factors higher than the original PscanChIP threshold, but still significant. As shown in A second important information derived by the PscanChIP analysis is the identification of positional bias within sites. That is, whether the CCAAT box is found as having a spacing preference with respect to the summit of the individual TF . In all, 30 factors showed positional bias, using a rather stringent criteria of positivity, p-value <10Finally, we extended the PscanChIP analysis of the 68 positive factors to all other ENCODE cell lines for which ChIP-seqs are available. Because of the absence of NF-Y data in these lines, verification based on peaks overlap was impossible. By and large, however, the data on the presence of CCAAT in peaks are consistent with Tier 1 cell lines, both for “global”  as well as “local”  connections. In particular, we note cell-type differences in the positivity for CCAAT of these latter TFs.In accordance with our pipeline, we extended the general peaks overlap analysis to all proteins present in ENCODE, independently from the enrichment of the CCAAT box motif in their peaks. We calculated overlaps, described by the presence of the peak summits of the factor within 150 bp on either side from the summit of NF-YB peaks. Essentially, there are three reasons for computing these data. (i) Although the window of PscanChIP and peaks overlap analysis is the same– 150 bp–the former assay must contain completely the relatively long (16 bp) NF-Y matrix, whereas in peaks overlap analysis, the summits–made of single nucleotides–are more likely to score positive in the same interval. (ii) The observation of “broad” peaks width of some proteins  over a sizeable length of DNA: the calculated punctiform summit might in these cases imperfectly reflect the actual binding area. The CCAAT motif could indeed be enriched, but simply missed because of the stringent spacing parameters of PscanChIP. (iii) NF-Y might be indirectly recruited to DNA in the absence of CCAAT, but in the presence of another TF: in this case, this TF might go completely unnoticed in the PscanChIP analysis, in which we considered only enrichment for the CCAAT box.loci where peaks bound by NF-YB and the said factor overlap. The results, shown in We applied the same threshold as in our previous study : signifiFactors with significant overlap with NF-YB peaks, but not with significant CCAAT enrichment, according to PscanChIP are listed in alphabetical order. The asterisk in POLR2H indicates that a tagged protein was used for analysis. Measurement of the “overlap score” with NF-YB peaks is detailed in  and in MThe different levels of co-association stemming from the two types of analysis result in classification of four Groups (previously three) . With reTFs with “global” enrichment for CCAAT as primary or secondary motif and overlap of the NF-Y/TF peaks >20% of the factor peaks in at least one dataset  connections–or rather lack of–are intriguing: in mouse ES cells, in fact, there is overlap between CTCF and NF-Y upon neuronal differentiation, but not in growing cells , suggest is CUX1 . Finally is CUX1 ,63–is a  is CUX1 –67: yet nucleosome and protein DNA complex genes, activation on DNA replication and mitosis targets. CCAAT and E2Fs sites are the most enriched in promoters of genes overexpressed in cancer: originally observed in profiling experiments [duo in systematic analysis of RNA-seq TCGA data of epithelial cancers [Factors of eriments , we–and  cancers –75. Furt cancers . Note thExcept for TBP, in Group 2, and GTF2B (TFIIB), all GTFs present in ENCODE are in this group: the TFIID subunit TAF1, involved in Initiator recognition , the P-Tx,y) is proportional to the significance of the overlap between factors x and y. NF-YA peaks are clustered with DEAF1/CEBPZ/DDX20/ASH1L/FOS/RBM25/PKNOX1/PBX2 . Results of K562 cell line are summarized in an UpSet-like plot  terms related to protein trafficking and processing, Golgi-to-ER retrograde transport and endoplasmic reticulum. (iii) Terms related to DNA damage response, activation of ATR in response to replication stress, double-strand break repair, HDR through homologous recombination, p53 pathway, regulation of p53, p53 regulation of cell cycle genes, p53-independent DNA damage response (pathway 15). (iv) Antigen processing and interferon signaling (pathways 17 and 18). A robust set of genetic and biochemical data support the role of NF-Y in these pathways [translation, spliceosome/mRNA splicing, UCH proteinases, cellular senescence, sumoylation and apoptosis. Additional pathways, with fewer factors (10 to 15) are expected from previous work: lipid metabolism , metabolism of amino acids and polyamines [RMTs methylate histone arginines (RFX5 and MTA3), ABC transporter disorder (E2F8), cargo trafficking to the periciliary membrane (PKNOX1), ER to Golgi anterograde transport , Lysosome (MITF), tRNA processing (ATF2) and metabolisms of vitamins (CBF2A2T2/RAD51/USF1).Looking at the data vertically, many enriched pathways are–predictably–retrieved full of regulatory proteins co-bound (>20): (i) various terms related to cell-cycle, such as pathways ,19,49,85lyamines ,87. A feWe then performed an identical analysis on the GM12878 and HeLa-S3 datasets. Although fewer factors are present, the ones globally associated with many pathways are common in the three cell lines  Tables: bona fide NF-Y targets among down-regulated genes: HIST1, HLA-A/B/C, RRM2 . We performed pairwise analysis and then gathered all data in heatmaps to give a graphical representation of groups of factors involved in a collective effort of repression or activation. ENCODE analyzed several chromatin features of Tier 1 cell lines, such as DNase I hypersensitive sites, DNA methylation, several histone PTMs, and also characterized the respective transcriptomes by RNA-seq. Histone PTMs were further processed to defined chromatin “states”, both by ENCODE and the RoadMap consortium. The latter defined 18 distinct chromatin states . This anResults were split according to two major categories of co-association with NF-Y: only CCAAT enrichment, or both CCAAT enrichment and significant overlap. For the last category, we evaluated additional chromatin states of peaks overlapping with NF-YB. The data shown in in vitro–and possibly visualization by Cryo-EM–of higher order complexes. As ENCODE further expands with more physiological approaches, such as CRISPR-Cas9-mediated inactivations, we look forward to eventually reconstruct NF-Y interactions with all factors on all loci, via the pipeline devised here.The ever-growing emporium of data generated by the ENCODE Project invited an update on NF-Y “friends” on the genomes of three cancer cell lines. We confirmed the 2014 data, doubled the number of TFs and cofactors associated to NF-Y in a significant number of sites, and modified our previous classification, adding a novel Group. Many classes of TFs are represented, often with single members prevailing. Importantly, new classes of proteins enter the NF-Y circle, such as RNA-binding proteins–notably involved in alternative splicing–and subunits of repressive complexes . Functional experiments and analysis of chromatin features help partition specific factors and target genes categorization. The conclusions reached here represent the basis for prioritization of biochemical dissections of NF-Y/TFs interactions, through modelling of available 3D structures, as well as reconstruction S1 Table−10, in at least one cell line are reported.TFs scored as positive, i.e. whose p-value<10(XLSX)Click here for additional data file.S2 TableFactors belonging to (XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(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 TablePscan output.(XLSX)Click here for additional data file.S9 TablePscan output.(XLSX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 FigA. Genome-wide representation. B. Analysis restricted to NF-YB-bound regions.(PDF)Click here for additional data file.S3 FigA. Genome-wide representation. B. Analysis restricted to NF-YB-bound regions.(PDF)Click here for additional data file.S4 FigA. Genome-wide representation. B. Analysis restricted to NF-YB-bound regions.(PDF)Click here for additional data file.S5 FigList of factors with significant overlap and/or CCAAT enrichment in A. GM12878 and B. HeLa-S3 cell lines, in ascending order based on the number of associated pathways. Blue bars represent the number of shared pathways among different factors, starting from one. Highlighted dots stand for positive intersection between the factor and the individual pathway. In green, cell-cycle related pathways; in purple, metabolism pathways; in blue, p53-related pathways; in yellow, endomembrane-system related pathways.(PDF)Click here for additional data file.S6 FigOutput of Pscan on promoters of up- (left) and down- (right) regulated genes after NF-YB inactivation, showing most enriched TFBS.(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 FigRelative distribution of chromatin states across factors ChIP-seq peak regions. A-B plots include factors with significant overlap with NF-YB and significant CCAAT enrichment. A. distribution of regions co-bound with NF-YB; B. distribution of all regions of factor. C. Distribution of regions of factors with significant CCAAT enrichment but not overlap.(PDF)Click here for additional data file.S9 FigRelative distribution of chromatin states across factors ChIP-seq peak regions. A-B plots include factors with significant overlap with NF-YB and significant CCAAT enrichment. A. distribution of regions co-bound with NF-YB; B. distribution of all regions of factor. C. Distribution of regions of factors with significant CCAAT enrichment but not significant overlap.(PDF)Click here for additional data file.


Plants are autotrophic organisms that self-produce sugars through photosynthesis. These sugars serve as an energy source, carbon skeletons, and signaling entities throughout plants’ life. Post-transcriptional regulation of gene expression plays an important role in various sugar-related processes. In cells, it is regulated by many factors, such as RNA-binding proteins (RBPs), microRNAs, the spliceosome, etc. To date, most of the investigations into sugar-related gene expression have been focused on the transcriptional level in plants, while only a few studies have been conducted on post-transcriptional mechanisms. The present review provides an overview of the relationships between sugar and post-transcriptional regulation in plants. It addresses the relationships between sugar signaling and RBPs, microRNAs, and mRNA stability. These new items insights will help to reach a comprehensive understanding of the diversity of sugar signaling regulatory networks, and open onto new investigations into the relevance of these regulations for plant growth and development. These fmiRNA genes are transcribed by RNA polymerase II in the nucleus. This generates long primary transcripts of miRNA , which are converted into a precursor miRNA (pre-miRNA) by endonuclease DICER-like1 (DCL1). After a complex processing involving the C2H2-zinc finger protein SERRATE (SE), DCL1 and the double-stranded RBP HYPONASTIC LEAVES1 (HYL1), the miRNA is loaded onto ARGONAUTE1 (AGO1) to integrate the RNA-induced silencing complex  and miRNA activity (ago1-25) exhibited a glucose-hyposensitive phenotype at the early seedling stage, and the expression of several miRNA target genes was deregulated, mainly via hexokinase-independent pathway. miRNA156 is one of the best characterized miRNAs in terms of sugar-dependent regulation. It is conserved in land plants and contributes to diverse physiological processes such as leaf development, heat stress memory, developmental transition, apical dominance, and flowering , the expression levels of nine known mature miRNAs and 12 novel mature miRNAs have been found influenced by sugar abundance in the stem  and miRNAs exists in plants. SUTs are key players in sucrose phloem loading and sugar allocation within plants /Suppressor of MEK 1 (SMEK1) complex and C-TERMINAL DOMAIN PHOSPHATASE-LIKE 1 and 2 (CPL1 and CPL2) dephosphorylate and activate HYL1, while Mitogen-activated Protein Kinase (MPK) phosphorylates and inactivates it  is required for miRNAs and small siRNAs to accumulate, by stabilizing pri-miRNAs through its RNA-binding activity and enhancing DCL1 activity . Beyond perature . FCA alsperature . The RBPperature , while Mperature . Many otperature . HoweverIncw1) that displays two transcripts – Incw1-S  and Incw1-L (large) – according to the respective lengths of its 3'untranslated regions  expression in rice suspension cells involves controlling both its transcription rate and mRNA stability , alcohol dehydrogenase 2 (ADH2), glyceraldehyde 3-phosphate dehydrogenase (G3PD), and sucrose synthase P-2 (SSP2) were consistently 1.6‐ to 2.6-fold longer in sucrose-supplied rice cells (Arabidopsis seedlings (Low-β-amylase1 (lba1) is a missense mutation of UP-FRAMESHIFT 1 (UPF1) RNA helicase, involved in nonsense-mediated mRNA decay (NMD). Its Arabidopsis mutant exhibited lower sugar induction of the AtβAmy transcript, which was restored by complementation of the lba1 mutation with wild type UPF1, further supporting the link between sugar signaling and the fate of the mRNA (Lycopersicon esculentum), potato (Solanum tuberosum), and tobacco , GLUTAMINE-DEPENDENT ASPARAGINE SYNTHETASE1 (ASN1), and DORMANCY-ASSOCIATED GENE2 (DRM2). Moreover, the glucose hypersensitivity of the sr45-1 mutant is alleviated when SnRK1 is disrupted (5-phosphatase 13 (5PTase13) in Arabidopsis, which encodes an inositol polyphosphate 5-phosphatase involved in regulating SnRK1 stability negatively in vitro (AtTZF1/AtCTH/AtC3H23 (a tandem-arrayed CCCH-type zinc finger motif involved in stress‐ and hormone-mediated growth), was also identified as a sugar-sensitive gene in Arabidopsis  is a serine/arginine-rich splicing factor that participates in 5' and 3' splicing site selection of introns and can bridge the 5' and 3' components of the spliceosome. The SR45 splicing factor regulates glucose signaling during early seedling development in bidopsis , more libidopsis . The sr4isrupted . SR45 coin vitro . This liin vitro . AtTZF1/bidopsis . AtTZF1 ignaling . HoweverPost-transcriptional regulation is an essential component of gene expression regulation in plants. Numerous findings have unveiled and characterized various factors involved in post-transcriptional regulation. The present review provides a first comprehensive picture of the relationship between sugar  and post-transcriptional regulation factors in plants, including RBPs, miRNAs, and mRNA stability of sugar-related genes. More work needs to be carried out to figure out the functions and mechanisms related to the involvement of post-transcriptional regulation in sugar-related processes, e.g., whether regulatory mechanisms found in human cells or yeast are also conserved in plants. Considering the frequently observed connection between mRNA abundance and sugar, some recently developed technologies for RNA editing (CRISPR-Cas13), RNA binding , and RNA folding  will support future studies. Besides, many aspects of RNA decay still need to be studied in depth, such as the spliceosome and the editosome , which play a crucial role in post-transcriptional regulation. Although some reports about the interaction between sugar-related RBPs and miRNAs exist, further investigations are still required to gain a comprehensive understanding of the way sugar signaling operates through each of these post-transcriptional regulation mechanisms and how they crosstalk to regulate plant growth and development. The hub role of hexokinase, SnRK1, and/or TOR kinase but also the relevance of the trehalose signaling pathway in the different post-transcriptional regulation networks could be two main future lines of research. This further knowledge will also pave the way for discovering a new and complex sugar regulatory network in plants.All authors listed have made direct contribution to the work, and approved it for publication. MW, LZ, FJ, SS, and JC have written different part of the manuscript. M-DP-G, LO, and LH have contributed to the section sugar and RNA binding proteins and JL to the section of sugars and miRNA.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.


Neuropsychiatric disorders are a collective of cerebral conditions with a multifactorial and polygenetic etiology. Dysregulation of epigenetic profiles in the brain is considered to play a critical role in the development of neuropsychiatric disorders. SET domain, bifurcate 1 (SETDB1), functioning as a histone H3K9 specific methyltransferase, is not only critically involved in transcriptional silencing and local heterochromatin formation, but also affects genome-wide neuronal epigenetic profiles and is essential for 3D genome integrity. Here, we provide a review of recent advances towards understanding the role of SETDB1 in the central nervous system during early neurodevelopment as well as in the adult brain, with a particular focus on studies that link its functions to neuropsychiatric disorders and related behavioral changes, and the exploration of novel therapeutic strategies targeting SETDB1. Moreover, epigenetic dysregulations of gene expression have been reported to interfere with proper neuronal development and functions. Thus, epigenetics holds the promise of identifying processes that mediate the pathogenesis of psychiatric disorders.Mental and neurological disorders are global problems which affect one in four people at some point during their lives . Although the etiology is largely unknown, research suggests their multifactorial inheritance involves both genetic and environmental components. Epigenetics mediates gene-environment interactions and converges at the level of gene expression into long, stable and heritable changes. Recent genome-wide association studies on neuropsychiatric disorders have identified genes encoding for epigenetic proteins as among the most significant risk factorsEpigenetics refers to heritable changes of phenotype, but not genotype. It regulates gene expression via the modulations of chromatin mechanics without altering DNA sequences, including DNA and RNA methylations, covalent modifications of histones, and various non-coding RNAs. There are complex interactions between different types of epigenetic mechanisms. For example, DNA methylation is typically found in genomic sequences of condensed chromatin, and often co-occurs with loss of active histone marks and gain of repressive marks. Most recent studies on global 3D genome organization introduced a new layer of epigenomic regulation on gene transcription. The cooperative work of all types of chromatin marks are critical for the maintenance of 3D genome stability, and vice versa; chromosome conformation changes affect gene expression genome-wide, accompanied by corresponding alterations on associated epigenetic marks.SETDB1  was first identified on human chromosome 1q21 and encodes a protein functioning as a histone methyltransferase (HMT) specific to histone H3 at lysin 9 (H3K9)4. Mouse SETDB1, a 92% homolog to human, was identified as an interacting partner of transcription factor ERG, thus named as ESET (ERG-associating protein with SET domain)5. Both human and mouse SETDB1 proteins  in vivo and forms various repressive protein complexes both at euchromatic and heterochromatic regions. Together with other epigenetic marks, especially DNA methylation, SETDB1-mediated H3K9 methylation participates in many chromatin events, which include transcriptional silencing, local heterochromatin formation, X-inactivation, and genomic imprinting. Moreover, distinct from other HMTs, SETDB1 was found to regulate higher-order chromosome conformation in neurons, and coordinate the expression of functionally related genes clustered in a single genomic locus9.In mammalian cells, there are multiple histone methyltransferases (HMTs) specific for H3K9, including SUV39-H1/2, G9A, G9A-like protein, GLP, PRDMs, SETDB1 and SETDB210 and Karanth et al.11, respectively). However, we and others have provided multiple lines of evidence indicating SETDB1 plays a critical role in the central nervous system under both normal and disease conditions16. Therefore, this review not only includes a general discussion of current knowledge about the molecular functions of SETDB1, but also draws attention to its role in regulating neuronal chromatin organization and disease-associated behaviors.Most current studies on SETDB1 have been conducted in embryonic stem cells and cancers .We first provide a brief summary on SETDB1-associated repressive chromatin complexes, followed by an elaboration of its function in regulating higher-order chromosome conformation. We then discuss the complex phenotype, after genetic deletion of 17. The PhD domain of KAP1 functions as an intramolecular E3 ligase for sumoylation of the adjacent bromodomain and facilitates the binding of SETDB118. Along with all the associating proteins, the SETDB1/KAP1/ KRAB-Zfp complex promotes the formation of a microenvironment of heterochromatin at euchromatic sites, which contributes to the gene silencing and position-effect variegation in mammalian cells20.The Krüppel-associated box (KRAB) domain-containing zinc-finger proteins (KRAB-Zfp) are a superfamily of transcription repressors. KRAB domain-associated protein 1 (KAP-1) is a scaffold protein that can assemble large repressive epigenetic machinery and is one of the best studied binding partners of SETDB1. Interactions of KAP-1 and SETDB1 are shown in Fig. ZNFs) in human cells21. Such deposition is not correlated with gene transcription, but may contribute to the genome stability via suppression of homologous recombination among the large number of ZNFs22. The SETDB1/KAP1 complex is well known for its essential role in silencing of endogenous retroviruses (ERVs), which have been observed in embryonic stem cells (ESCs)24, mouse embryonic fibroblasts24, committed B-lineage cells25, as well as in the developing brain13. Interestingly, ZNFs evolutionarily co-emerge with waves of ERVs and are considered as an adaptive mechanism to fight against viral invasion. Therefore, one can speculate that the SETDB1/KAP1 complex may have dual functions during evolution by not only silencing ERV in order to maintain genomic stability, but also limiting ZNF gene expansion to allow a certain level of retrotransposon activity to introduce new genomic diversity.SETDB1 and KAP1 have also been shown to bind to the 3′ end of zinc finger genes  and HP1 were identified as binding partners of the SETDB1/MBD1 complex27. ATF7IP enhances the catalytic activity of SETDB1 and promotes tri-H3K9 methylation in vivo28, potentially by protecting SETDB1 from proteasomal degradation29. The SETDB1/MBD1/ATF7IP complex was shown to be involved in the X-inactivation. Knockdown of ATF7IP, or genetic depletion of MBD1 and SETDB1 induced the activation of Xi-linked reporter gene in mouse cells30.Cross-talk between H3K9 methylation and DNA methylation has been long recognized, although the underlying mechanisms remain elusive. An early study demonstrated that SETDB1 was recruited by methyl-CpG-binding domain protein 1 (MBD1) to form a stable S-phase-specific complex with the large subunit of chromatin assembly factor CAF-1, providing an intriguing mechanism for the coordination of DNA methylation and histone H3K9 methylation for the heritable maintenance of heterochromatin assembly during DNA replicationp53BP2 and RASSF1A in vivo31. Evidence indicated that in Drosophila dSetdb1 recruit Dnmt2 and Su(var)205, the Drosophila ortholog of HP1, to mediate DNA methylation32. In brain, loss of Setdb1 in mature neurons was demonstrated to induce robust transcriptional activation of the clustered protocadherin (Pcdh) genes, which was accompanied by significant DNA hypomethylation on gene promoters as well as distal regulatory sequences9.In addition to the binding of MBD1, SETDB1 also affects DNA methylation by recruiting DNA methyltransferases (DNMT). SETDB1 directly interacts with DNMT3A/B in cancer cells, and represses the expression of Setdb1 deficient ESCs and cells lacking the DNA methyltransferases, indicating that distinct genes are targeted by SETDB1-mediated H3K9 methylation and DNA methylation33. Future studies are needed to understand the causal relationship between these two important epigenetic events.Taken together, these studies provide promising evidence indicating SETDB1 works together with DNA methylation synergistically in transcriptional repression Fig. . However12. Indeed, SETDB1 is often involved in the formation of long-range chromatin loops for transcriptional repression16. The Grin2B gene, which encodes the NMDA receptor subunit NR2B, was identified as a SETDB1 target in adult brain 12. Two active loops were further identified connecting the Grin2b promoter with two enhancer sites approximately 500 kb downstream of the TSS. These two active loops compete with the SETDB1-mediated repressive loop and coordinate Grin2b expression in neurons in an activity-dependent manner16.3D chromosomal conformation regulates transcription by moving regulatory elements into spatial proximity with gene promoters. Although there is abundant evidence indicating the role of SETDB1 in transcription repression, genome-wide analysis of SETDB1 binding profiles indicates that most SETDB1-binding signals are in the intergenic sequencesain Fig. . A loop Setdb1 knockout mice9. A significant loss of long-range chromatin contacts was detected at defined genomic loci, accompanied by disintegration of large topologically associating domains (TADs), particularly enriched for a ~1.2 Mb TAD domain covering the whole Pcdh cluster  occupancy. However, active loops connecting enhancers and Pcdh gene promoters were not affected. Instead, a novel SETDB1-tageting site was identified, located upstream of the Pcdh cluster and anchoring the upper hinge of the TADcPcdh domain. Long-repressive loops radiating from this site toward the whole cPcdh locus were largely disrupted in the knockouts -specific Setdb1 knockout in mice resulted in severe deficits in neural development, with postnatal lethality around day 1013. Intermediate basal progenitors were significantly reduced in the cortical plate in knockouts, which may be due to the impaired proliferation together with enhanced apoptosis. Logically, the loss of progenitor cells would certainly lead to reduced neurogenesis in general; however, the phenotype in differentiated neurons is rather complex. Loss of SETDB1 in mice only reduced the cell numbers of early-born deep layer neurons, while the late-born upper layer neurons remained largely unaffected, if not increased. Starting around E18.5, up-regulation of astrogenesis became another evident phenotype in the Setdb1 knockout brain. A significant increase in GFAP positive astrocytes was observed both in vivo and in vitro. Taken together, these observations suggest an essential role of SETDB1 during brain development.The brain has a complex epigenetic landscape that changes dynamically during early life, potentially rendering this organ more prone to disease relevant disturbances. Epigenetic programming, including 3D genome configuration, is now recognized as a major determinant of neural stem cell fate13. However, genes involved in neuronal activities were largely down-regulated, and therefore unlikely to be the direct targets of SETDB1. How can the direct gene targets of SETDB1 in the developing brain be determined? Employing an anti-SETDB1 ChIP-seq is one means of approaching the question, but this is technically challenging, especially in the complex neural system. Likewise, H3K9me3 ChIP-seq data from brain tissue are generally lacking in high-resolution “sharp peaks”, making it difficult to identify clear genomic target sequences. Derepression of ERVs was observed in the Setdb1 knockout developing brain, and ERV-induced aberrant transcription was shown to account for some of the increases in abnormal genes13. However, neuronal genes do not seem to be affected in this manner. Therefore, how SETDB1 and possibly ERV regulate neurodevelopment remains elusive.On the molecular level, loss of SETDB1 in mice resulted in dysregulation of multiple neural and non-neural genes, including genes enriched for neurogenesis, differentiation, signal transmission, viral reproduction, ossification and spermatogenesis12. Although conditional knockout of Setdb1 in the forebrain of postmitotic neurons was well tolerated and animals survived until adulthood with generally normal neurological activities, knockout neurons displayed immature features of spine deficits, disturbed epigenome, and transcriptome, suggesting a substantial role of SETDB1 in adult brain9. In this section, we will survey current research advances and potential implications of the engagement of SETDB1 in multiple neuropsychiatric disorders.In addition to its key role in neurodevelopment, there is also accumulating evidence indicating SETDB1 regulates brain functions in adulthood. We were able to show that SETDB1, although at a low level, is constitutively expressed in mature neuronsHTT gene with an extensive increase in CAG repeats36. Mutant HTT protein leads to a number of molecular abnormalities, including alterations of gene transcription and chromosome organization37. In 2006, Ryu and colleagues reported an increase of SETDB1 and H3K9me3 in striatal neurons from HD patients and R6/2 mice. Combined treatments of mithramycin and cystamine reversed abnormal expression in R6/2 neurons and, accompanied by a reduction in mutant HTT, improved motor coordination and extended survival38. This phenotype was repeated in different HD mutant cell lines and in a Drosophila HD model39. The same group also reported that SETDB1 regulated rDNA transcription via physical interaction with the transcription factor upstream binding factor 1 (UBF1), and recruited RNA polymerase 1 to the promoter region of rDNA40. The increase of SETDB1 in HD neurons resulted in hyper-methylation of UBF1, which in turn led to nucleosome condensation and rDNA transcriptional repression. This collective evidence prompted Dr. Ryu to propose that SETDB1-mediated H3K9me3 might contribute to the pathology of HD, and small compounds selectively targeting SETDB1 could serve as a new strategy for the treatment of HD. In collaboration with Dr. Ae Nim Pae’s group, two novel peptide-competitive small molecules were identified, compounds VH01 and VH06. These compounds specifically inhibited SETDB1 in vitro and reduced the levels of SETDB1 and H3K9me3 in HD striatal neurons without observed cytotoxicity41.Huntington’s disease (HD) is a heritable brain disorder caused by mutations in the 29. Normal HTT binds directly to ATF7IP both in hESCs and neurons, and hinders the interaction of ATF7IP/SETDB1 complex with other heterochromatin regulators, thus maintaining low levels of H3K9me3 in normal hESCs. In induced pluripotent stem cells (iPSCs) from patients with HD, mutant HTT inhibited its interaction with the ATF7IP-SETDB1 complex and triggered excessive H3K9me3; while the knockdown of ATF7IP reduced H3K9me3 and alleviated gene dysregulation in neural counterparts14. This discovery implicates a novel target of drug development aside from SETDB1, as the inhibition of its binding partner ATF71P may also ameliorate abnormal gene expression in HD brains.Research by Dr. Vilchez and colleagues provides an interesting mechanism explaining how HTT affects SETDB1 in human ESCs. As mentioned above, ATF7IP enhances and stabilizes SETDB1 by shielding it from proteasomal degradation45. Chase and colleagues reported increased expression of H3K9 HMTs, including SETDB1 and increased H3K9me2 levels, in postmortem parietal cortical brain tissue and lymphocytes from patients with SCZ compared to healthy controls46. Similarly, results were obtained from a different cohort of patients with SCZ and the levels of H3K9me2 in lymphocytes were higher in men with SCZ than controls, but not women47.Schizophrenia (SCZ) is a highly heritable neurodevelopmental disorder, which is characterized by abnormal gene regulation due to dysregulation in restrictive chromatinSetdb1 transgenic model. Genetic variants of GRIN2B are associated with a risk of developing SCZ and, as mentioned earlier, GRIN2B is one of the direct targets of SETDB1 in the brain12. In addition, the newly identified SETDB1-target non-coding element upstream of the Pcdh cluster was a near-perfect match to a SCZ risk haplotype 9. This haplotype , independent of the other 107 genome-wide loci, significantly contributes to heritability of SCZ, with a small INDEL as the index SNP (rs111896713)48. A robust SETDB1 peak conserved in human and mouse neurons was located right at the index SNP, suggesting SETDB1 may participate in the pathophysiology of SCZ via disruption of chromatin contacts associated with the SCZ risk loci. It will be of interest to conduct more comprehensive studies on SCZ-related behaviors in Setdb1 animals, which could provide a suitable tool to better link the neuropsychological mechanisms with the molecular perturbations underlying the disease, and a viable tool to test novel targets for its therapeutic potential.The implication of SETDB1 in SCZ is substantiated by animal studies using a 49. H3K9 methylation has been reported to be differentially regulated by acute and chronic stress in rodents, and can be reversed after repeated treatment with the antidepressant fluoxetine50. The link between H3K9 methylation and depression is further supported by our studies on Setdb1 mice, which displayed antidepressant-like effects when over-expressed in mature forebrain12. In addition to exhibiting a reduced level of behavioral despair and anhedonia at baseline, these Setdb1 mice showed accelerated recovery from depression-like states when challenged with sub-chronic stress in the learned helplessness paradigm. In addition, forebrain conditional knockout of KAP1, the crucial binding partner of SETDB1, also resulted in an elevated anxiety phenotype and stress-related learning and memory deficits51.Major depressive disorder (MDD) is a recurrent and disabling mental disease affecting 11% of the population worldwide. Dysregulation of epigenetic regulatory machineries, particularly enzymes that regulate the level of di- and tri-methylation of H3K9, contributes to the neurobiology of depression and antidepressant actionsGrin2b that encodes NMDA receptor NR2B) might be involved in the progression of neuropathology of symptoms of depression under negative environmental impacts. Therefore, it would be of interest to continue functional studies in Setdb1 mice using stress-induced depression models. These models could serve as an excellent means of testing small molecules with specificity towards SETDB1, which may hold the potential for the development of future antidepressants.Although SETDB1 showed a protective effect with antidepressant potential in the mouse model, at present there is a lack of clinical evidence linking SETDB1 with MDD. Considering its essential role during early development, it is unlikely that genetic disruption on SETDB1 directly contributes to pathogenesis of MDD. However, dysregulation of SETDB1 activity and its downstream pathway  gene clusters. In a classic model, DNA methylation and a long noncoding antisense RNA (lncRNA-ATS) work synergistically at the PWS imprinting center (PWS-IC) and tightly regulate the imprinted gene expression at this locus.Gene imprinting is an epigenetic phenomenon that causes a pattern of monoallelic expression, which is determined by its parental origin. Disruption of gene imprinting has been observed in Angelman syndrome (AS) and Prader-Willi syndrome (PWS). AS and PWS share the same pathogenic genomic region on chromosome 15q11–13, with the loss of contribution from the maternal allele in AS and the loss of signal from the paternal side in PWS. Several genes are exclusively expressed from the paternally inherited allele of this region including 52. Their data indicated that SETDB1 together with ZNF274 contribute to the maternal silencing of SNORD116 gene expression in iPSCs. Interestingly, such allele specificity existed only for ZNF274 binding and H3K9 methylation occupancy, but not SETDB1 or KAP-1. Knockdown of SETDB1 or ZNF274 activated the maternal expression of SNORD116 genes in the PWS-iPSCs. However, knockdown of KAP-1, which is supposed to connect ZNF274 and SETDB1, had no effect on SNORD116 expression.By using iPSCs from both patients with PWS and AS with paternal and maternal large deletion of 15q11-13 respectively, Cruvinel and colleagues studied the repressive chromatin complex on the PWS region in an allele specific mannerSNORD116 regulation, the fact that SETDB1 knockdown can activate the maternal expression of the SNORD116 gene in PWS-iPSCs to a level comparable to the active paternal counterpart (1–35% lower) is encouraging. It was reported that deletion of the SNORD116 cluster alone causes the majority of the clinical manifestations of PWS53. Therefore, SETDB1 may also be considered as a therapeutic target for the treatment of PWS by using region-specific epigenomic editing, for example via the AAV-mediated CRISPR/dCAS9 system, to disrupt SETDB1 function selectively at the SNORD116 locus. It is worth noting that the SETDB1 binding site is away from the bi-partite PWS-IC. Therefore, such strategy should not introduce unwanted effects on other imprinted genes in this region, particularly on the AS causal gene UBE3A.Regardless of the incomplete understanding of 54. The incidence of a second variation, Pro529Leu, was significantly increased in patients with ASD compared with controls, and was inherited both maternally and paternally; patients carrying this alteration displayed a variety of clinical neuropsychiatric deficits54. In addition, chromosomal microarray analysis of copy number variations (CNV) identified a de novo 260 Kb-deletion at 1q21.3 encompassing the SETDB1 gene in patients with ASD55. Together, these findings provide evidence supporting the role of SETDB1 in the etiology of ASD.Autism spectrum disorder (ASD) is a complex group of neurodevelopmental disorders that include genetic and environmental causes and affect communication and behavior. One genetic component, identified by Cukier and colleagues, is an ASD-specific nonsynonymous mutation, Pro1067del, which falls into the catalytic SET domain of SETDB1 protein and is inherited maternally56. In other words, one gene may contribute to multiple diseases to a varying extent. As reviewed here, accumulating evidence indicates SETDB1 may be one such gene. Functioning as a histone H3K9 methyltransferase, its general role is to repress transcription of individual genes, DNA repeats, or multiple genes in a cluster. Through binding with KAP-1 and hundreds of KRAB-Zfps, or coordinating with the DNA methylation machinery, SETDB1 can target various genomic loci in different cell types, or at different stages during brain development. Consistent with this assumption, different Setdb1 knockout lines displayed distinct phenotypes, such as the severe neurodevelopmental deficits during the embryonic stage, and the cognitive and mood deficits in adulthood.Recent studies have shown that major neuropsychiatric disorders often share common polygenetic familial etiology57. The model of SETDB1 binding to psychiatric risk sites and regulating the Pcdh gene cluster provides one example that fits perfectly with such a hypothesis. Together with other evidence mentioned in this review, we believe that the disruption of SETDB1 expression or its enzyme activity in the central nervous system may affect various aspects of brain functions via 3D genome disorganization. Confronted with a challenge from different types of environmental stressors, SETDB1 may contribute to the pathological processes of multiple psychiatric disorders. Considering the reversible property of SETDB1 as an epigenetic enzyme, it holds the potential for the treatment of psychiatric disorders.In addition to its role in gene transcription, recent findings show SETDB1 has an unanticipated effect on 3D genome organization in neurons. Advances in genetics allow examination of the 3D genome as a new mechanism for understanding the relationship between gene dysregulation in psychiatric disorders and disease risk variants. These variants are often located on non-coding genomic sequences remote from gene promoters. Changes in 3D genome organization would disrupt multiple chromatin interactions between regulatory loci and gene promoters, thus affecting a large group of disease-associated gene transcription coordinately