Bacillus licheniformis CBBD302 cultivated in media supplemented with ammonia were analyzed, resulting in identification of 1443 differently expressed genes, of which 859 genes were upregulated and 584 downregulated. Subsequently, the nucleotide sequences of ammonia-inducible promoters were analyzed and their functionally-mediated expression of amyL, encoding an α-amylase, was shown. TRNA_RS39005 (copA), TRNA_RS41250 (sacA), TRNA_RS23130 (pdpX), TRNA_RS42535 , TRNA_RS31535 (plp), and TRNA_RS23240 (dfp) were selected out of the 859 upregulated genes and each showed higher transcription levels  in the presence of ammonia and glucose than that of the control. The promoters, PcopA from copA, PsacA from sacA, PpdpX from pdpX, Pald from ald, and Pplp from plp, except Pdfp from dfp, were able to mediate amyL expression and were significantly induced by ammonia. The highest enzyme expression level was mediated by Pplp and represented 23% more α-amylase activity after induction by ammonia in a 5-L fermenter. In conclusion, B. licheniformis possesses glucose-independent ammonia-inducible promoters, which can be used to mediate enzyme expression and therefore enhance the enzyme yield in fermentations conventionally fed with ammonia for pH adjustment and nitrogen supply.Ammonium hydroxide is conventionally used as an alkaline reagent and cost-effective nitrogen source in enzyme manufacturing processes. However, few ammonia-inducible enzyme expression systems have been described thus far. In this study, genomic-wide transcriptional changes in  PMF is 3 molecule and leads to the formation of an ammonium cation (NH4+) and a hydroxide ion (OH−). Ammonium is also one of the major sources of nitrogen for bacteria, yeasts, and plants  its NH4+ is taken up and incorporated to yield glutamine and/or glutamate  supplemented with 20 μg/ml kanamycin when necessary.The bacterial strains and plasmids used in this study are listed in Table B. licheniformis CBBD302 were pretreated as following steps. B. licheniformis CBBD302 were propagated in 50 ml LB medium in 250-ml shake flasks at 37°C with agitation of 200 rpm for 15 hr until the culture reached mid-log phase. The cells were collected by centrifugation at 5,000 × g for 5 min and resuspended in 5 ml fresh LB medium; and 500 μL was then transferred into 50 ml LBGN (LB medium supplemented with 1% glucose and 0.1% ammonium hydroxide). The mixture was incubated at 37°C and 200 rpm for 5 hr. The cells were collected by centrifugation at 5,000 × g for 5 min and immediately frozen in liquid nitrogen and stored at −70°C for RNA isolation. In parallel, B. licheniformis CBBD302 cells were prepared as a control grown under the same conditions, but in LBG .Before mRNA isolation, the cells of B. licheniformis ATCC 14580 genome  by Tophat 2  and subsequently sequenced on the Illumina sequencing platform. All obtained clean reads were mapped to the http://www.genome.jp/kegg) to identify biological functionality.Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment was conducted to investigate the main metabolic pathways related to the DEGs  .The putative binding sites for σ-factors and transcription factors (TFs) were analyzed using DBTBS  without a promoter sequence. Plasmid pHY000-amyL was transformed into B. licheniformis BL-109 yielding a recombinant strain used as the negative control.The selected promoter candidates were amplified with primers listed in Table u et al.  and the B. licheniformis strains were cultivated in 50 ml LB medium supplemented with 20 μg/ml kanamycin in 250-ml flasks at 37°C and 200 rpm for 15–18 hr until late exponential phase. A 10% inoculum was then added into 50 ml fermentation media and incubated at 37°C and 200 rpm for up to 120 hr. The carbon source in the fermentation medium was modified from previous studies by changing lactose to glucose  and 500 μL crude enzyme. After incubation at 70°C for 30 min, 100 μL of iodine reagent  was added. The absorbance at 550 nm (A550) was measured using a SP-2012 UV spectrophotometer . One unit of α-amylase activity was defined as the amount of enzyme required to liberate one µmol glucose per minute. The optical density (600 nm) of the cultures were also measured with an SP-2012 UV spectrophotometer. The mass of the fermentation broth pellets was used to estimate the cell mass. These assays were conducted in triplicate.The activity of α-amylase was assayed by the DNS method, as described previously  and 15,436,656 (only 1% glucose) high-quality clean reads  obtained after filtering raw reads and removing rRNA mapped reads. Of these, 97.07% and 97.13% of all reads were uniquely mapped to the reference genome of B. licheniformis ATCC 14580 and approximately 80.87% and 79.02% of the expressed genes had a sequencing coverage of 80–100%.To gain a global view of the transcriptional response to ammonia in The DEGs were extracted from the RNA-seq data and 1,443 DEGs were identified at a threshold of absolute value of log2 fold change >1 and an FDR <0.05. Eight hundred fifty nine of 1,443 genes were significantly upregulated and 584 genes were significantly downregulated  with high log2 fold change (≥3.5) and high FPKM values , when incubated with ammonia while showing low FPKM values (≤350) in the control, were identified from the 859 upregulated genes  were not associated with specific σ factors, two  were controlled by SigW and Pdfp was controlled by two σ factors, SigW and SigX. CodY is a global transcriptional regulator and it was reported to control more than 100 genes, and could activate them at specific periods . Pplp had the highest expression level among the six candidates and the enzyme activity was increased by 38% after induction by ammonia. The results showed that five glucose-independent, ammonia-inducible promoters were found in B. licheniformis.To test if the selected promoters could mediate gene expression induced by ammonia, a series of recombinant amylase expression plasmids—pHY001-amyL, pHY002-amyL, pHY003-amyL, pHY004-amyL, pHY005-amyL, and pHY006-amyL—were generated by replacing the original promoter PB. licheniformis RBA-5 harboring the best ammonia-inducible promoter Pplp was selected for large-scale fermentations. The activity of α-amylase in the media fed with ammonium hydroxide was approximately 23% higher than that with sodium hydroxide  ammonia could be used as a preferred nitrogen source for enzyme/protein production, a kind of neutralizer to adjust the pH of the fermentation broth; and (2) more importantly, that ammonia can act as an inducer to elevate the gene transcription levels, thus improving the expression levels of the target enzyme Fig. .The results of this study provide clear evidence that an ammonia-inducible expression system has been developed. Despite this increase in expression using ammonia-inducible promoters, further enhancement of expression is still possible. Gene regulation is a complicated process and many factors can result in low expression levels. For instance, special sequences like palindrome sequences found between promoter and genes sequences (Yuan et al., B. licheniformis grown in ammonia was characterized and five glucose-independent ammonia-inducible promoters were functionally identified. By using these ammonia-inducible promoters and ammonia as inducer, the enzyme expression level was significantly improved compared to the control. To the best of our knowledge, this is the first report that ammonia can be employed to mediate enzyme overexpression based on the ammonia-inducible promoters, and therefore enhance the enzyme yield using ammonia conventionally-fed for adjusting acidic pH and as a nitrogen supply.The transcriptomic profile of kuab037_Supplemental_FileClick here for additional data file.


Imprinting Control Regions (ICRs) are CpG-rich sequences acquiring differential methylation in the female and male germline and maintaining it in a parental origin-specific manner in somatic cells. Despite their expected high mutation rate due to spontaneous deamination of methylated cytosines, ICRs show conservation of CpG-richness and CpG-containing transcription factor binding sites in mammalian species. However, little is known about the mechanisms contributing to the maintenance of a high density of methyl CpGs at these loci.To gain functional insights into the mechanisms for maintaining CpG methylation, we sought to identify the proteins binding the methylated allele of the ICRs by determining the interactors of ZFP57 that recognizes a methylated hexanucleotide motif of these DNA regions in mouse ESCs. By using a tagged approach coupled to LC–MS/MS analysis, we identified several proteins, including factors involved in mRNA processing/splicing, chromosome organization, transcription and DNA repair processes. The presence of the post-replicative mismatch-repair (MMR) complex components MSH2 and MSH6 among the identified ZFP57 interactors prompted us to investigate their DNA binding profile by chromatin immunoprecipitation and sequencing. We demonstrated that MSH2 was enriched at gene promoters overlapping unmethylated CpG islands and at repeats. We also found that both MSH2 and MSH6 interacted with the methylated allele of the ICRs, where their binding to DNA was mediated by the ZFP57/KAP1 complex.Our findings show that the MMR complex is concentrated on gene promoters and repeats in mouse ESCs, suggesting that maintaining the integrity of these regions is a primary function of highly proliferating cells. Furthermore, the demonstration that MSH2/MSH6 are recruited to the methylated allele of the ICRs through interaction with ZFP57/KAP1 suggests a role of the MMR complex in the maintenance of the integrity of these regulatory regions and evolution of genomic imprinting in mammalian species.The online version contains supplementary material available at 10.1186/s13072-022-00462-7. Genomic imprinting is a gene regulatory mechanism of mammals based on differential acquisition of epigenetic marks during female and male gametogenesis . These mZFP57 lead to Transient Neonatal Diabetes type 1 and multi-locus imprinting disturbances  with the RRBS mode (–rrbs), we aligned the reads against the Mus musculus genome (mm10) using Bismark v.0.23.0 with default parameters  and the multiple mapping reads, and we used only uniquely mapped reads for the rest of the study.For ChIP-seq analysis, two nanograms of DNA from immunoprecipitated and input chromatin were used for Illumina library preparation. Libraries were generated and sequenced at IGA Technology Services , by using the NuGen Ovation Ultralow Library System v2 Kit and 150 bp paired-end sequencing on the Illumina NovaSeq6000 platform . After checking that there were no bad quality base calls and adapter contaminations in the raw data, we aligned the reads against the Mus musculus genome (mm10) using Bowtie2 short read aligner v2.3.5.1 with default parameters . We remop = 0.0001). We annotated the peaks using the ChIPseeker package . DNA methylation and binding profiles of MSH2 ( 2 replicates), ZFP57 and KAP1 are reported as in Figure 3a.


These changes are achieved through the implementation of complex gene regulatory networks. Furthermore, these regulatory networks are influenced by epigenetic mechanisms that drive cell heterogeneity and cell-type specificity, in a controlled and complex manner. Epigenetic marks, such as DNA methylation and histone residue modifications, are highly dynamic and stage-specific during neurogenesis. Genome-wide assessment of these modifications has allowed the identification of distinct non-coding regulatory regions involved in neural cell differentiation, maturation, and plasticity. Enhancers are short DNA regulatory regions that bind transcription factors (TFs) and interact with gene promoters to increase transcriptional activity. They are of special interest in neuroscience because they are enriched in neurons and underlie the cell-type-specificity and dynamic gene expression profiles. Classification of the full epigenomic landscape of neural subtypes is important to better understand gene regulation in brain health and during diseases. Advances in novel next-generation high-throughput sequencing technologies, genome editing, Genome-wide association studies (GWAS), stem cell differentiation, and brain organoids are allowing researchers to study brain development and neurodegenerative diseases with an unprecedented resolution. Herein, we describe important epigenetic mechanisms related to neurogenesis in mammals. We focus on the potential roles of neural enhancers in neurogenesis, cell-fate commitment, and neuronal plasticity. We review recent findings on epigenetic regulatory mechanisms involved in neurogenesis and discuss how sequence variations within enhancers may be associated with genetic risk for neurological and psychiatric disorders.Neural induction, both  Complex multicellular organisms are made up of trillions of highly specialized cells with distinct phenotypes and nearly identical genomes. This phenotypic heterogeneity is mainly explained by epigenetics, the field that studies mechanisms that can alter gene expression without modifying DNA sequence. Non-coding regions of the DNA, for example, enhancers, are important epigenetic mechanisms that orchestrate precise spatiotemporal gene expression patterns. Other epigenetic regulatory mechanisms include DNA methylation, histone post-transcriptional modifications, and chromatin remodeling . StudiesThere are several types of non-coding DNA regulatory elements including promoters, enhancers, silencers, and insulators. Enhancers are presumed to be the most numerically prevalent regulatory elements. Evidence has demonstrated that distant enhancers can activate transcription by physically contacting promoters through the formation of chromatin loops. Furthermore, enhancer activity is highly dynamic and tissue-specific, with a significant number of them being active in the brain . During The nervous system of higher-order mammals is composed of multiple neuronal cell types derived from neural stem/progenitor cells (NSPCs). Neurogenesis not only takes place during development but also in selected regions of the adult nervous system. Studies have demonstrated that NSPCs reside in the adult mammalian brain within two discrete niches: the subventricular zone lining the lateral ventricles and the subgranular zone of the dentate gyrus in the hippocampus . ConstitIn this review, we describe important epigenetic mechanisms involved in neurogenesis, namely, enhancers, DNA methylation, histone modifications, and chromatin architecture. We focus mainly on enhancers and describe their main features and regulatory mechanisms. We also describe experimental methods used to identify putative and active enhancers, infer target genes, and elucidate their regulatory function. We performed a comprehensive literature search and discuss prominent studies in the field of neural enhancers. Finally, we discuss emerging research focused on brain-enhancer genome-wide identification, target prediction, functional validation, and risk association for neurodegenerative diseases and brain disorders.cis-regulatory regions of the DNA that serve as a docking platform for TF binding and increase the likelihood of transcription of one or more distally located genes in an orientation-independent manner , giving rise to noncoding RNAs called enhancer-derived RNA (eRNA)  Kim et . Severalcis-regulatory elements (cCREs) according to biochemical annotations  in human datasets, resulting in almost one million potential enhancers in one or more cell types  have demonstrated that eukaryotic genomes are organized into megabase-sized physical compartments referred to as topological associating domains (TADs) that are largely invariant across cell types . TADs seBesides CTCF, several other factors regulate and stabilize enhancer-promoter loops within TADs, for example, the architectural protein YY1  Figure  and the Comprehensive genome-wide studies have established that enhancers exist in multiple regulatory states and transition dynamically between them in response to stimuli . These sInactive enhancers are sequestered in compact chromatin and therefore lack TF binding, histone modifications, and promoter interactions. Enhancers can also exist in a primed state before activation . Even thPoised enhancers depict similar features as primed enhancers however, they have been found to lie within bivalent chromatin . Poised Active enhancers induce strong expression of target genes and they may be identified through biochemical marks including: 1) enrichment of TF binding sites, 2) the ability to indirectly bind the transcriptional co-activator p300/CBP and the Mediator complex, 3) reside in accessible, nucleosome-depleted chromatin; assessed by H3K27ac and H3K4me1 overlapping modifications on flanking histones, 4) an interaction with promoters, and 5) an active transcription of eRNAs .One of the most basic methods that can be employed to identify enhancers is by analyzing DNA sequence conservation as well as the enrichment of TF binding motifs . HoweverThere are several genome-wide scale biochemical annotations used to infer enhancer activity based on their aforementioned features. For example, chromatin accessibility has been widely used to identify putative enhancers . DNA accin vitro loaded with sequencing adaptors, to release and tag protein-DNA complexes, simplifying the library preparation process and facilitating the mapping by having an increased resolution  techniques and its variants circular chromosome conformation capture (4C), and chromosome conformation capture carbon copy (5C)  Van Ste. Here, ccis-regulatory elements , the Ensembl regulatory build, the functional annotation of the mammalian genome (FANTOM) project, and the VISTA Enhancer Browser . The GenIn the last decade, high throughput sequencing techniques have allowed comprehensive mapping of regulatory elements such as enhancers in the nervous system . ResearcA straightforward way to identify cell-type-specific enhancers is using controlled and validated induction protocols. For example, in a recent study, ESCs were cultured for 72 h in a NSCs induction medium and active enhancers were evaluated at different time points  using RNA-seq, ATAC-seq and H3K27ac ChIP-seq . Researcin vivo chromatin accessibility patterns across time  into dorsal and ventral forebrains, researchers were able to capture gene-regulatory dynamics of human forebrain development and corticogenesis . Brieflyoss time . Interesoss time . iPSCs wcis-regulatory sites can accurately distinguish between cell types and lineages   is one ohylation .Generally, DNAm at the promoter region is associated with gene silencing, whereas DNAm in the gene body is related to gene activation . DNAm inde novo during neuronal maturation  are hypermethylated  enzymes and the development of sophisticated DNAm sequencing techniques allowed the elucidation of the cytosine demethylation pathway . TET prothylated . These mpression .The function of DNAm in adult neurogenesis has been an intensive research area since the brain is characterized by unique and high levels of CpH and hydroxymethylation . EpigenoHigh throughput sequencing and single-cell sorting technologies have enabled researchers to further address the DNA regulatory landscape of the brain. Recently, Eukaryotic DNA is tightly wound around octamers of four essential histone proteins  conforming chromatin. Histone sequences are highly conserved, however, their tails extend beyond the ring of DNA and are susceptible to post-translational modifications . These min vitro using adult rat hippocampal NPCs, researchers found that an HDAC inhibitor was able to induce neuronal differentiation  and histone deacetylases (HDACs). The addition of an acetyl group by HATs to histone H3 or H4 neutralizes the positive charge of the lysine residue, making the interaction between histones and DNA weaker, leading to transcriptional activation in most cases. Conversely, histone deacetylation increases chromatin compaction, hence HDACs have a repressive function on gene expression by removing histone acetylation. In a study performed ntiation . These rntiation . In anotntiation .Acetylation of lysine 27 in Histone H3 (H3K27ac)  is a higA total of 18 mammalian HDACs modulate histone deacetylation and they depict tissue specificity. HDAC1 and HDAC2 are upregulated in neocortical intermediate progenitors (IPs) in the SVZ of the developing brain and they regulate the spatial positioning of these progenitors by targeting Neurogenin2 . In the Another important histone modification is methylation. Histone methylation is more stable, due to its slow turnover. This modification can occur on basic residues  and it can consist of multiple methylations at a single amino acid . Unlike As previously described, chromatin is a dynamic scaffold that can be remodeled according to external cues, to regulate transcription. One of the main chromatin remodeling mechanisms is histone modifications. These modifications not only regulate the accessibility to DNA, but also recruit remodeling enzymes that can reposition nucleosomes. The majority of chromatin is present as heterochromatin, a condensed structure that is transcriptionally inactive . It is cA significant proportion of neurogenesis is regulated by dynamic histone modifications and 3D chromatin architecture . TherefoGWAS have identified loci  that harAs discussed earlier, neurons and neural precursors are among the cell types with more specific enhancer regulatory sequences . Furtherhttps://doi.org/10.1038/nrdp.2017.13]. To correlate PD GWAS risk loci with enhancer regulatory sequences, researchers performed genome-wide histone H3K27ac, CTCF occupancy, and RNA-seq assays using an established cell line  as a model of human substantia nigra neurons  to identify disease causal variants. For each cell type, researchers performed promoter capture Hi-C (pcHiC) and ATAC-seq genomic data coupled with RNA-seq. Results demonstrated that the majority of promoters interacted with more than one PIR showing that promoters can be regulated by multiple enhancers. Moreover, 20% of accessible PIRs in each lineage were cell-type-specific. Furthermore, GO analysis of genes contacting these cell-type-specific PIRs yielded neural-associated terms and immune functions using data from neuronal subtypes and astrocytes respectively. Using public datasets, the authors observed that PIRs are highly enriched for active chromatin features (including enhancers) inferred by ChromHMM in matched human brain tissues. PIRs also depict enrichment of H3K27ac modifications and CTCF binding sites. Additionally, neuronal subtypes depicted motif enrichment of known neuronal fate commitment TFs within PIRs. The authors validated the functional implication of PIRs in gene expression using CRISPR-Cas9 and CRISPRi. They later associated their PIRs with GWAS data for a variety of neuropsychiatric diseases, including AD and PD. A total of 6,396 unique GWAS SNPs were analyzed and 70% of them overlap with at least one PIR in one or more cell types. Autism spectrum disorder, mental process, and SCZ SNPs were enriched in PIRs across all cell types. Unipolar depression SNPs were only enriched in excitatory and hippocampal neurons, and AD, attention deficit hyperactive disorder, and bipolar disorder SNPs were enriched in motor neurons. PD SNPs displayed enrichment only in astrocyte PIRs. Accessible PIRs overlapping with neuropsychiatric SNPs contacted genes relevant to the etiology of the implicated disease (A similar study performed by  disease .cis-regulatory elements or enhancers, chromatin interactions, and regulation of transcription. Since these elements are highly cell-type-specific, researchers have used human postmortem brain regions, sorted cell subpopulations from resection tissue, iPSC-derived neuronal and non-neuronal cells, and LUHMES cell lines (The number of integrative multi-omic studies is growing as sequencing technologies become more accessible and more researchers are aiming at understanding the links between GWAS SNPs, ll lines . HoweverThe human brain is a very complex organ consisting of diverse and highly interconnected cells carrying the same DNA sequence. The genome encodes the instructions for the generation and function of all cells in our body and it also harbors causes of disease susceptibility. Diversity in cell fate and function is a result of dynamic gene expression patterns that are finely controlled by regulatory elements encoded within the same DNA. Among those regulatory elements, enhancers are the most common in mammalian genomes and they are the key players in the dynamic usage of the genome during development and throughout our lifespan. Only in the brain, hundreds of thousands of enhancers have been predicted to potentially regulate spatiotemporal gene expression . MountinHigh-throughput sequencing technologies have enabled genome-wide enhancer identification using biochemical signatures including chromatin accessibility, DNA methylation, histone modifications, binding of TFs and transcriptional coactivators, bidirectional transcription, and chromosomal conformation mapping . MoreoveAdvances in novel next-generation high-throughput sequencing technologies, genome editing, GWAS, iPSC-derived cell types, and brain organoid cultures are allowing researchers to study brain development and neurodegenerative diseases with unprecedented resolution. GWAS studies have identified thousands of risk loci associated with complex brain disorders. However, the interpretation of GWAS risk loci remains challenging since most of the dysregulation is observed in noncoding regions such as enhancers. Furthermore, elucidating the effect of GWAS SNPs requires understanding cell-type-specific interactions between enhancers and promoters. Assessing this interactome is not trivial since multiple enhancers can interact with the same promoter and only some of those interactions are functional in a specific cell type and time point. Thus, the effect a SNP has on transcription is also cell-type-specific. Given that gene regulation varies substantially across cell types, future efforts should focus on characterizing the epigenomic and transcriptomic landscapes with single-cell resolution.As discussed earlier, brain predisposition to disease in late life stages could be associated with the rapid evolution of neuronal and glial-specific enhancers, thus increasing the importance of studying these regulatory elements . Disrega


MYC oncoprotein deregulation is a common catastrophic event in human cancer and limiting its activity restrains tumor development and maintenance, as clearly shown via Omomyc, an MYC-interfering 90 amino acid mini-protein. MYC is a multifunctional transcription factor that regulates many aspects of transcription by RNA polymerase II (RNAPII), such as transcription activation, pause release, and elongation. MYC directly associates with Protein Arginine Methyltransferase 5 (PRMT5), a protein that methylates a variety of targets, including RNAPII at the arginine residue R1810 (R1810me2s), crucial for proper transcription termination and splicing of transcripts. Therefore, we asked whether MYC controls termination as well, by affecting R1810me2S. We show that MYC overexpression strongly increases R1810me2s, while Omomyc, an MYC shRNA, or a PRMT5 inhibitor and siRNA counteract this phenomenon. Omomyc also impairs Serine 2 phosphorylation in the RNAPII carboxyterminal domain, a modification that sustains transcription elongation. ChIP-seq experiments show that Omomyc replaces MYC and reshapes RNAPII distribution, increasing occupancy at promoter and termination sites. It is unclear how this may affect gene expression. Transcriptomic analysis shows that transcripts pivotal to key signaling pathways are both up- or down-regulated by Omomyc, whereas genes directly controlled by MYC and belonging to a specific signature are strongly down-regulated. Overall, our data point to an MYC/PRMT5/RNAPII axis that controls termination via RNAPII symmetrical dimethylation and contributes to rewiring the expression of genes altered by MYC overexpression in cancer cells. It remains to be clarified which role this may have in tumor development. In this article, we present data on physical and functional interactions among MYC, RNAPII, and PRMT5.Deregulated expression of the transcription factor MYC is a crucial event in a wide variety of cancer cells. Deregulation—usually due to overexpression—affects transcriptional networks controlling key cell functions such as proliferation, stemness maintenance, metabolism, and pre-mRNA splicing. Therefore, it is not surprising that MYC inhibition represents an attractive strategy against many cancer types MYC dimerizes with MAX through the bHLHZip domain. MYC-MAX dimers bind to promoter proximal regions of genes and interact with the transcription machinery. It is unclear whether MYC mainly acts as a universal amplifier of the transcriptional program running within a given cell, a specific activator of a distinct set of target genes, or both ,4,5,6,7.Besides MAX, MYC can associate with a wide range of proteins, including CDK9—the kinase subunit of p-TEFb—and SPT5, which bind to the RNAPII carboxyl terminal domain (CTD). The RNAPII CTD is crucial for the control of transcription, mRNA processing, and nucleosome modifications. It contains 52 tandem repeats of the consensus sequence N-Tyr1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7-C as well as several Arg (R) and Lys (K) residues that are targets of post-translational modifications—phosphorylation, ubiquitination, methylation—which regulate distinct steps of transcription ,9,10,11.KRAS-mutated Myc overexpressing lung cancer cells, Omomyc induces cell death [One of the best tools to interfere with Myc function, avoiding the use of gene KO techniques, is Omomyc, an MYC-derived 90 amino acid mini-protein. Omomyc constrains MYC activity in cancer cells and in vivo models by directly affecting MYC binding to DNA and MYC protein–protein interactions, as shown by our previous works ,22,23,24ll death , whereasll death . Therefore, to gain insights into deregulated pathways in Myc overexpressing cancer cells, Omomyc represents an extremely useful opportunity. We reported that MYC associates with PRMT5, an arginine methyl-transferase that monomethylates and symmetrically dimethylates histone and non-histone proteins ,28. PRMT5 acts on transcription through several histone methylations, which can either activate or repress transcription. In particular, repressor activity implicates symmetrical dimethylation of R8 and R3 on histones H3 and H4, respectively, which lead to enhanced DNA methylation and chromatin compaction. The functional interaction between MYC and PRMT5 occurs at multiple levels. Indeed, MYC upregulates PRMT5—which methylates Sm proteins—and the core small nuclear ribonucleoprotein particle assembly genes in lymphoma cells, maintaining splicing fidelity during lymphomagenesis . MoreoveThe findings that PRMT5 has a key role in transcription termination and interacts with MYC strongly suggested that MYC might be involved in transcription termination as well, through PRMT5. In the present work, we employ MYC inhibition by shRNA and by Omomyc to investigate MYC’s role in regulating RNAPII post-translational modifications, RNAPII distribution, and gene expression in cancer cells. Our study is suggestive of another control level exerted by MYC, which makes increasingly evident its role as master controller of gene expression, involved in RNAPII activation, elongation, termination, and pre-mRNA splicing.Brain Tumor 168 (BT168) glioblastoma stem cells (GSC) have been previously described by De Bacco et al. 2012 . Cells w5 cells were seeded in 35 mm dishes and infected the following day in the presence of 4 µg/mL polybrene. BT168FO cells were selected with 50–200 μg/mL hygromycin B , Ramos FO cells were selected with 400–800 µg/mL higromycin B. After selection, Flag-Omomyc and shMYC expression were assessed by western blots.The lentiviral plasmid pSLIK-FO has already been described . The lenFlagOmomyc (pCbsFlagOmomyc), FlagMYC (pCbsFlagMYC), and pSLIKshMYC plasmids were transfected using Lipofectamine 2000  according to the manufacturer’s instructions. A total of 50 nM PRMT5-siRNA or control siRNA  were transfected with DHARMAFect transfection reagent  according to the manufacturer’s instructions. Cells were harvested 48 h after transfection.6 cells were lysed on ice in 140 mM NaCl, 10 mM Tris pH 7.6–8.0, 1% Triton, 0.1% sodium deoxycoholate, 1 mM EDTA, containing protease inhibitors (Roche) and benzonase  for 25′ by vortexing and forcing them through a 27-gauge needle, at least 10 times [A total of 10–20 × 1010 times . After c10 times .Proteins were resolved in 6-8-10 or 12% polyacrilammide gels and transferred to PVDF  or nitrocellulose membranes  for 2 h at 250 mA on ice or overnight at 30 V. Filters were blocked in PBS/0.1% Tween-20  added with 10% non-fat dry milk, for 1 h30′ at room temperature (RT). Primary antibodies were incubated O/N at 4 °C, according to the concentration recommended by the manufacturer, in PBS/0.1% Tween-20 plus 2.5–5% non-fat dry milk. After three 10′ washes, filters were incubated for 1 h at RT with either goat-anti rabbit (1:5000) or goat-anti mouse (1:2000) horseradish peroxidase (HRP)-conjugated secondary antibodies (Merck). Blots were developed using SuperSignal West Pico or Femto Maximum Sensitivity Chemiluminescent Substrate . Images were captured with a Chemidoc XRS+  and quantified using ImageJ software. To avoid possible errors in western blots band quantification, we used a background subtraction method described in Gallo-Oller et al., J Immunol Methods, 2018 . http://myers.hudsonalpha.org/documents/, first accessed on 1 November 2011) by using MAGnify Chromatin Immunoprecipitation System protocol . Antibodies used: MYC , MAX , RNAPII , RNAPII phospho Ser5 , RNA Pol II phospho Ser2 , Flag . For RNA-seq, 2µg total RNA purified by PureLinkRNA Mini Kit  was used. ChIP-seq and RNA-seq libraries were prepared at Istituto di Genomica Applicata  according to Illumina TruSeq DNA and TruSeq RNA Sample Preparation Guides. Samples were sequenced through Illumina HiSeq 2000 e 2500.Samples for ChIP and ChIP-seq assays were prepared and analyzed according to Myers Lab ChIP-seq Protocol v041610 . In the case of multiple TSSs, those with the highest enrichment were chosen. Motif enrichment analysis was performed by Pscan-ChIP [https://bioinformatics.cineca.it/rap/, first accessed on 1 November 2011)—including quality controls, adaptor trimming and masking of low-quality sequences, tophat2, bowtie, and CuffLinks 2.2—was used to reconstruct the transcriptome (hg19 reference) and calculate expression values as FPKM (Fragment per Kilobase Million per genes). Data have been analyzed using the DESeq2 R package [p-value < 0.05 were taken as up-regulated (log2 fold change > 0) or down-regulated (log2 fold change < 0). Up- and down-regulated genes were separately used for Gene Ontology Enrichment Analysis using EnrichR [http://www.broad.mit.edu/gsea/index.html, first accessed on 1 November 2011) was used to determine whether an a priori defined set of genes shows statistical significance, according to the differences between −DOX and +DOX experimental conditions (phenotypes). In detail, the RNA-Seq dataset files—consisting of experiments in triplicate for each time point of DOX treatment—containing two labeled phenotypes (−DOX and +DOX) were prepared in TXT format: −DOX included all 0 h time points (1° phenotype), while +DOX included from 4 h to 48 h of DOX treatment (2° phenotype). The expression dataset was compared with several gene sets either exported from the GSEA-MsigDB database or homemade. The gene sets contained the gene set name and the list of included genes. A gene set file was in GMX or GMT format. GSEA software calculated an enrichment score (ES) describing the degree to which a gene set was overrepresented at the extremes (top or bottom) of the entire ranked list of the data set—where genes are ranked according to the expression difference between −DOX and +DOX conditions. The Enrichment Score (ES) was calculated by walking down the list. The value statistically increased when it found genes present in the gene set and decreased when genes were not present. The magnitude of the ES was dependent on the correlation of each gene with the phenotype. The proportion of false positives was evaluated by calculating the False Discovery Rate FDR-q value. Refseq IDs were mapped onto gene symbols using the biormaRt R tool [Data were processed as described by Galardi et al., 2016 . For ChIcan-ChIP . Seqmine package , conside EnrichR  and Gene EnrichR . Data an EnrichR . Comparit R tool . Analysep-values were calculated by paired Wilcoxon signed-rank tests. Regression lines were estimated using linear regression models. For genomic data, differential expression was assessed by CuffDiff2, as well as by Fold-Change thresholds, and Gene Set Enrichment Analysis  subdividing MYC targets and non-MYC targets into groups of 500 genes.Statistical analyses were performed by using GraphPad Prism version 5.0d and Excel . All histograms represent the mean ± SEM of data obtained in 3 or more independent experiments. Statistical significance was determined by one-way repeated-measures ANOVA or a paired t-test. The box plot To gain insights into a potential MYC role in transcription termination, we investigated its ability to influence symmetrical dimethylation of RNAPII R1810, a key modification that is catalyzed by PRMT5 and regulates termination, by gain and loss of function experiments. To this end, we employed Omomyc, an MYC-specific shRNA, the PRMT5 activity inhibitor EPZ015666 and a smart pool of siRNAs for PRMT5 (siP5). We transfected HEK293T recipient cells with a FlagMYC expression construct, in the presence or absence of an MYC shRNA and FlagOmomyc expression plasmids. Following or not immunoprecipitation with an RNAPII antibody, the protein extracts were analyzed by western blotting with antibodies specific for PRMT5, SMN, R1810me2s, and RNAPII a,b. We fmyc gene is under control of the immunoglobulin heavy chain promoter [To evaluate MYC action on RNAPII symmetrical dimethylation, we took advantage of two cancer cell types with high MYC basal levels: the glioblastoma stem cell (GSC) line named BT168 and the Burkitt’s lymphoma cell line Ramos, in which the promoter ,48. Thespromoter . No remaThe transition between initiation and productive elongation is elicited by Ser5 phosphorylation in RNAPII CTD, followed by the elongation-specific Ser2 phosphorylation. MYC binds to p-TEFB and regulates transcriptional pause release. Therefore, we asked whether Omomyc might also affect Ser2 phosphorylation, thus influencing the transcription elongation rate and mRNA expression. First, HEK293T cells were transfected with FlagMYC, FlagOmomyc, and pSLIKshMYC plasmids, either alone or in combination. Immunoblotting analyses with RNAPII Ser2P-specific antibodies showed that MYC ectopic expression strongly enhanced Ser2 phosphorylation a, left, To further examine how Omomyc may influence the glioblastoma stem cell transcriptome, we measured by RNA-seq the mRNA output changes consequent to 24 and 48 h Omomyc induction in BT168FO cells. Strongly expressed MYC target genes (FPKM ≤ 10 in at least one condition) are shown in To elucidate the meaning of differentially expressed gene sets, we performed functional enrichment analyses by means of GO, KEGG, Reactome, WikiPathways. While we found little significance at 24 h DOX treatment—probably due to the lower number of differentially expressed genes —various Muhar signature genes present a high level of MYC binding to the promoter, and their expression level in a given cell line correlates well with the MYC amount in that cell line. We thus investigated whether Omomyc influenced the expression of this annotated gene set. We found that as many as 91 out of 100 Muhar signature genes were expressed in BT168FO cells (at FPKM > 1), almost all significantly down-regulated by Omomyc, as shown by the negative score in the Gene Set Enrichment Analysis (GSEA) and by the heatmap and profile score of the signature genes (To investigate the effect of Omomyc on the expression of genes directly regulated by MYC, we resorted to the signature of 100 direct MYC targets—conserved in a variety of cancer cell lines—described by Muhar and coworkers . Such Mure genes d,e. MYC binds to most active promoters enhancing transcription pause release and elongation ,16. It aMuhar signature of MYC target genes (Muhar signature genes were significantly down-regulated by Omomyc induction. The correlation analysis for all MYC target genes (This trend was also present in the et genes d–g, withThe concept of the MYC oncogene as a “traditional” transcription factor has consistently changed in the last 10 years. Indeed, MYC has been found to work during the transcription process not only as a chromatin binding factor, directly reorganizing the cancer genome, invading chromatin regulatory loci, such as promoters ,19 and sMuhar signature [When considering the whole transcriptome affected by MYC inhibition in glioblastoma stem cells, we found that gene clusters well established to be controlled by MYC activity were affected by Omomyc. The differential expression observed following Omomyc induction is expected to be largely the consequence of MYC inhibition. Nevertheless, our gene expression analyses—performed after relatively long perturbations of 24 and 4 h—cannot discriminate between direct and indirect effects. Furthermore, there is no consensus on the direct regulatory functions of MYC, as several authors argue that MYC is a transcriptional activator and repressor of selected gene subsets, whereas others have suggested that MYC acts as a general transcriptional amplifier ,5,6,19. ignature , corroboAt the chromatin level, the increased density at promoters may be explained by the consideration that Omomyc restrains transcriptional pause release and may thus cause an accumulation of RNAPII at promoters. The impaired R1810 symmetrical dimethylation, which affects termination and may lead to RNAPII accumulation at termination regions of active genes , may acc


Nrf2 is a major transcription factor that significantly regulates—directly or indirectly—more than 2000 genes. While many of these genes are involved in maintaining redox balance, others are involved in maintaining balance among metabolic pathways that are seemingly unrelated to oxidative stress. In the past 25 years, the number of factors involved in the activation, nuclear translocation, and deactivation of Nrf2 has continued to expand. The purpose of this review is to provide an overview of the remarkable complexity of the tortuous sequence of stop-and-go signals that not only regulate expression or repression, but may also modify transcriptional intensity as well as the specificity of promoter recognition, allowing fluidity of its gene expression profile depending on the various structural modifications the transcription factor encounters on its journey to the DNA. At present, more than 45 control points have been identified, many of which represent sites of action of the so-called Nrf2 activators. The complexity of the pathway and the synergistic interplay among combinations of control points help to explain the potential advantages seen with phytochemical compositions that simultaneously target multiple control points, compared to the traditional pharmaceutical paradigm of “one-drug, one-target”. In the past two decades, the sequence of events leading to the activation and expression of genes controlled by the actions of the Nrf2 transcription factor has steadily expanded. While the rapidly growing interest in Nrf2 is driven by the physiological effects resulting from the induction or repression of the genes it controls, those downstream effects are outside the scope of this review, which focuses on the events that affect the ability of the Nrf2 molecule per se to be expressed, modified, processed, activated, and disposed of by various cellular mechanisms.NFE2L2 gene. A recent review [NFE2L2 promoter as containing six CpG methylation sites, indicated as Point 1 in NFE2L2 promoter caused by high glucose in HepG2 cells, suggesting a therapeutic approach to nonalcoholic fatty acid liver disease (NAFLD) at this control point [NFE2L2 promoter also contains transcription factor binding sites (Points 2–5) that include the antioxidant response element (ARE), which allows Nrf2 the ability to regulate its own transcription, as well as binding sites for the aryl hydrocarbon receptor/aryl hydrocarbon receptor nuclear translocator (AhR/ARNT), for NF-κB, and for MYC [NFE2L2 gene nor avoidance of Keap1-dependent depletion of Nrf2 in the cytosol. At this point, we begin to observe the complexities and redundancies in this exquisitely regulated pathway—if the normal supply chain is compromised, an emergency alternative or workaround may come into play. Once the Nrf2 protein enters the cytosol, it may be bound by the Keap1 molecule. Several controls may be exerted at the level of the Nrf2–Keap1 interaction. MiR-125b (Point 9) has been found to protect retinal pigment epithelial cells from oxidative damage by substantially inhibiting Keap1 expression and enhancing Nrf2 expression [SQSTM1) enables a p62/Keap1/Nrf2 signaling pathway (Point 15). A number of factors seem capable of activating autophagy via p62, including resveratrol [SQSTM1, so this increased availability of p62 may be an additional mechanism whereby Nrf2 can increase its own availability.The sequence of events shown in t review  describeol point . The NFE for MYC . The pre for MYC , as indi for MYC . Li et a for MYC  have sho for MYC  and oxyg for MYC . Another for MYC  found th for MYC  and thei for MYC , heart f for MYC , and isc for MYC . Another for MYC . The IRE for MYC . This mepression . Similarpression . These apression  activatepression  . Literally hundreds of papers have described electrophilic small molecules (known as Michael acceptor molecules) that react avidly with several cysteines of the Keap1 protein, resulting in regulation of the Nrf2 and/or NF-κB pathways. Most of these molecules are naturally occurring phytochemicals or synthetic molecules based on these naturally occurring compounds. Recently, Liang et al. compared 50 such natural compounds , and sev® for the treatment of multiple sclerosis—all show this ability to deplete glutathione. Curcumin (from turmeric) has been implicated in causing severe liver injury, especially when combined with piperine, which is increasingly used in dietary supplements to dramatically increase the absorption of curcumin [From the 1999 landmark discovery of the ability of Keap1 to repress the activity of Nrf2 by Itoh et al.  until a curcumin ,50. CDDOcurcumin . Carnosicurcumin ,53,54, pcurcumin . InteresCREBBP in humans and is frequently referred to as p300/CBP because p300, which is encoded by EP300 in humans, shares a high degree of homology and overlapping enzymatic activities with CBP [NFE2L2 gene, but it is not clear whether it enhances p300 to other EpRE/ARE promoters [CDKN2A gene—has been proposed as a checkpoint for oxidative stress responses by inhibiting CBP-dependent acetylation of Nrf2 at Point 28 [NFE2L2 versus acetylation of the Nrf2 protein, the discrepancy may be due to comparing “apples-to-oranges”, and more specific experimental designs may be necessary to reconcile the conflicting observations. Another covalent modification of Nrf2—methylation of Arg-437—is catalyzed by protein arginine methyltransferase-1 (PRMT1) at Point 32 [HMOX1 gene promoter. The study presented evidence that methylation of Nrf2 by PRMT1 is required for the recruitment of p300/CBP at the HMOX1 ARE promoter, noting that among the four signature Nrf2-regulated genes examined, certain genes  were affected more than others  by the absence of methylation [Returning to with CBP . One or romoters . Interesromoters . Thus, bromoters . CBP actromoters , suggestromoters  like theromoters ,50, as dromoters  found thromoters ,72. FurtPoint 28 . At PoinPoint 28 . In keepPoint 28 . Using mPoint 28 . It is nPoint 28  have repPoint 28  concludePoint 32 . This mohylation . This isXPO1 gene in humans.To bind efficiently to the EpRE/ARE elements and activate gene transcription, Nrf2 must form a heterodimer with a small Maf protein, and MafF, MafG, and MafK seem to be largely interchangeable for this role at Point 33 . In the The primary mechanism for determining the duration of Nrf2’s presence in the nucleus is regulated from outside the nucleus and involves the activity of PI3K, which is activated by many phytochemical Nrf2 activators, such as carnosic acid at Point 22 . SalazarEarly research in the Nrf2 field tended to be presented as a “binary” phenomenon—either Nrf2 was inducing its protective array of genes, or it was not. Phytochemicals and drugs either activated, or they did not. The degree of activation was not (and still is not) able to be quantified in terms that meaningfully translate between various studies carried out in different laboratories. Nrf2 regulates a huge repertoire of genes. Clearly, many different evolutionary pressures have driven the creation of the many control points. Management of stress is required for every tissue and organ, but the origins of the stress and the systems that may need to be protected may vary enormously from one organ to the next. Thus, it would not be surprising that triggers for Nrf2 activation may function best when under local organ control. It is highly unlikely that hundreds of genes would all need to be regulated at the same time, and to the same degree throughout the body. We now understand that redox balance is critical, so globally upregulating genes to alleviate traumatic brain injury, for example, might create redox imbalance in other organs that have not been injured. We are beginning to understand how the pathway might accommodate these situations and more.The example of Wang et al.  discusseA multifunctional, far-reaching transcription factor such as Nrf2 will inevitably encounter conflicted scenarios where the best solution to a problem may not be obvious or simple. Some effects of Nrf2 activation might be helpful, while at the same time others may be harmful. That is, the most desirable gene expression profile for Nrf2’s effects may be very context-dependent. The complexity of the activation pathway may invite evolutionary solutions that could, for example, negate or even reverse regulatory effects that would in certain circumstances be counterproductive. The literature provides several examples of “good cop, bad cop” scenarios. Some of the most obvious transformations to take place in a single cell type occur in the macrophage, as these cells can be pro-inflammatory or anti-inflammatory, or participants in the cleanup of post-inflammatory damage, or in tissue remodeling. These seemingly contradictory roles of the macrophage have recently been reviewed by Jensen et al.  in the c® , provides another potential level of control over Nrf2’s transcriptional efficiency, as its effectiveness may also be determined by promoter sequence variants, as well as by small-molecule inhibitors or by CRM1, which can export BACH1 from the nucleus. Modifications that favor BACH1 while impeding Nrf2’s efficacy may conceivably reverse the regulatory effect from induction to repression. escribed ). These escribed ,97,99 anescribed ,101, bot [CYP1A1 , CBS [101A1 [CBS , and MATS [MAT1A . CystathS [MAT1A ,106,107.S [MAT1A  and recuS [MAT1A . CBS is S [MAT1A ,111. MATS [MAT1A ,113. SodS [MAT1A  have shoS [MAT1A . Thus, aFor decades the dominant paradigm for drug discovery has been to find a single compound to induce, activate, repress, inhibit, or antagonize the presumed single target responsible for causing the disease under study. Without question, this paradigm has had some phenomenal successes, but many scientists who pride themselves on being able to “think outside the box” have not managed to find their way out of this one. A serious look at NFE2L2 gene and around translation of the Nrf2 protein. Much more activity centers around Keap1 and the sequestration and destruction of Nrf2, involving Points 9–20. Another hub centers on post-translational modification of Nrf2 and its association with binding partners (Points 21–33). Only then can Nrf2-driven gene regulation occur. Each regulated gene may also be controlled by promoter methylation and histone methylation or acetylation reactions to provide each gene with its own unique regulation. Another hub centers on shutting down Nrf2 and removing it from the nucleus for degradation (Points 34–45). Interestingly, some individual compounds or compositions have been shown to have at least some effect on Nrf2-regulated genes, even though they appear to be acting through a single hub—but that is not to say that their action is optimal, targeted, or without collateral damage (such as sulfhydryl depletion due to electrophilicity). The most comprehensive action we have seen by a single compound is by carnosic acid acting to modify Keap1 as a mild electrophile (Point 16), to activate PI3K to phosphorylate Nrf2 (Point 22), and to activate the p-AKT/p-GSK-3β/FYN pathway (Point 39), which inhibits nuclear expulsion of Nrf2, prolonging its activity in the nucleus. It is because of the ability of carnosic acid to act at three key hubs that we have based our PB123 and PB125 compositions on it.In examining The network hub that has by far attracted the most attention from researchers is the Keap1 hub. As discussed above, electrophilic modification of Keap1 effectively allows Nrf2 to enter the nucleus rather than be diverted to the proteasome. The collateral damage due to excessive or poorly targeted electrophilicity of this class of Nrf2 activators has been ignored by many but is of great concern to some ,119,120,Another very promising approach has sought non-covalent activators that compete with Nrf2 for binding to Keap1. These molecules are referred to as “iKeap1” molecules, and some have shown high-affinity binding for Keap1 in ways that block its ability to bind, ubiquitinate, and degrade Nrf2. The application of artificial intelligence, machine learning, and the ability to deduce in silico three-dimensional protein structures and binding affinities   has greaIn the past two decades, there have been 24,000 published studies involving Nrf2, with more than 4000 of them published in 2022. Clearly, this pathway is of huge importance, and we appear to be far from a full understanding of its reaches and ramifications. We hope to see greater emphasis on subtleties that are currently lacking with regard to how subsets of the vast repertoire of Nrf2-regulated genes might be fine-tuned by other levels of sensors, and how tissues, organs, and cell types have evolved ways to provide specialized regulation depending on their unique situations. We certainly hope to see more human clinical trials to utilize and extend what has been learned in the laboratory regarding the potential therapeutic benefits that this transcription factor has to offer.


Arabidopsis NODULIN HOMEOBOX (NDX) is a nuclear protein described as a regulator of specific euchromatic genes within transcriptionally active chromosome arms. Here we show that NDX is primarily a heterochromatin regulator that functions in pericentromeric regions to control siRNA production and non-CG methylation. Most NDX binding sites coincide with pericentromeric het-siRNA loci that mediate transposon silencing, and are antagonistic with R-loop structures that are prevalent in euchromatic chromosomal arms. Inactivation of NDX leads to differential siRNA accumulation and DNA methylation, of which CHH/CHG hypomethylation colocalizes with NDX binding sites. Hi-C analysis shows significant chromatin structural changes in the ndx mutant, with decreased intrachromosomal interactions at pericentromeres where NDX is enriched in wild-type plants, and increased interchromosomal contacts between KNOT-forming regions, similar to those observed in DNA methylation mutants. We conclude that NDX is a key regulator of heterochromatin that is functionally coupled to het-siRNA loci and non-CG DNA methylation pathways. Arabidopsis NDX was previously reported as a regulator of euchromatic gene expression. Here the authors show that NDX functions at pericentromeric regions and regulates heterochromatin homeostasis by controlling siRNA production and non-CG methylation. Arabidopsis, euchromatin is located along chromosome arms containing most transcriptionally active genes, whose expression depends on tissue/cell type, developmental phase, and environmental conditions. Heterochromatin is depleted in protein coding genes and typically associates with telomeres, (peri)centromeres, transposable elements, silenced rDNA, and small heterochromatic islands interspersed along euchromatic arms. Key functions of heterochromatin include: (i) protection of the genome against unscheduled transposon activities that would lead to genomic instability, (ii) regulation of cell differentiation and cell-type identity by hindering transcription factor-mediated cellular reprogramming, (iii) controlling mitotic cell division by ensuring kinetochore assembly at pericentromeres and sister chromatin cohesion.Functionally, the eukaryotic genome is divided into transcriptionally active euchromatin and silent heterochromatin . In 2. DNA methylation in plants occurs in three sequence contexts  driven by different genetic pathways4. MET1 is responsible for CG methylation, the chromomethylases CMT3 and, to lesser extent, CMT2 catalyze CHG methylation, while DRM1/2 and CMT2 perform CHH methylation. De novo methylation of CHG/CHHs is established by the RNA-dependent DNA methylation pathway (RdDM) involving the generation of specific 24nt heterochromatic small interfering RNAs (het-siRNA)2. In the upstream phase of RdDM, precursor transcripts are produced by RNA Polymerase IV (Pol IV) that mature into functional het-siRNAs by RNA-dependent RNA polymerase 2 (RDR2) and DICER-Like 3 (DCL3) endoribonuclease6. In the downstream phase, het-siRNAs are incorporated into the central component of the RNA-Induced Transcriptional Silencing Complex (RITSC), ARGONAUTE4 (AGO4) or its homologs . RITSC is then recruited to target loci by scaffold transcripts produced by another plant-specific RNA polymerase, Pol V, which ultimately tethers the DRM2 methylase to perform de novo cytosine methylation7.Several molecular pathways interact to regulate heterochromatin status, including DNA methylation, histone modifications, and RNA silencingArabidopsis, CHG and CHH methylation is mainly associated with heterochromatin and is functionally linked to histone H3 lysine 9 methylation. CHG/CHH methylation reinforces H3K9me1/2 and vice versa: CMT3/CMT2 DNA methylases read the H3K9me2 mark, while SUVH4/5/6 histone methylases (see below) read the mCHG/mCHH signal9. This mechanical coupling creates a self-amplifying loop between CHH/CHG methylation and histone methylation to silence transposable elements. H3K9me1/2 is catalyzed by KYP/SUVH4, SUVH5, and SUVH6 lysine-methyltransferases, which recognize mCHG/mCHH sites in constitutive heterochromatin11. (The abundance of H3K9me3 modification is low in Arabidopsis.) The H3K9me1/2 signal is recognized by specific reader proteins, of which AGDP1  has only recently been identified13 and further members await exploration.In 14. This suggests that the two repressive histone modifying systems act autonomously on heterochromatin formation. H3K27me1 is typically located in chromocenters and deposited by ATXR5/6, while H3K27me2/3 form heterochromatic patches in chromosome arm-associated gene loci, catalyzed by the Polycomb Repressive Complex 2 (PRC2). Polycomb Group (PcG) proteins also form the Polycomb Repressive Complex 1 (PRC1) that has H3K27me3-reader and histone H2A E3 ubiquitin ligase activities15 and mediates gene repression through histone H2A lysine 119 mono-ubiquitination (H2AK119ub1). Several factors have emerged in recruiting PRC1 to chromatin, such as the PRC2 component CLF16, the CAF-1 histone chaperone17, the AL6 PhD finger protein18, the LHP1 chromodomain protein16, and more recently, the homeodomain protein NDX (NODULIN HOMEOBOX), which is the only transcription factor to date that interacts with PRC119.Another crucial repressive histone mark is H3 lysine 27 methylation (H3K27me) that occurs independently of DNA methylation pathways20. It contains an atypical homeobox domain (HD) and two additional NDX-A/B domains, of which the HD and NDX-B domains were shown to bind double-stranded and single-stranded DNA in vitro22. The specific role of these domains is not known in detail. NDX has been shown to associate with the PRC1 core components RING1A/B, making a functional link with the E3 ubiquitin ligase module of the complex19. Previously, NDX was shown to regulate the expression of FLOWERING LOCUS C (FLC), a central integrator of flowering transition, by stabilizing an R-loop structure at the 3′-end of the locus21. This region serves as a terminator of sense (FLC) transcription and a promoter of antisense (COOLAIR) transcription, the latter being inhibited by an unknown mechanism that suppresses FLC transcription and accelerates flowering transition. A recent study identified FCA (an RNA binding protein) and FY (a component of the cleavage polyadenylation specificity factor complex) to recognize and resolve the R-loop in FLC and a subset of other loci24. However, the role of NDX in these processes was not studied in a mechanistic detail and was omitted from the working model of co-transcriptional chromatin silencing by R-loop resolution23. The genome-wide association of NDX with R-loops and its global effect on ncRNA transcription remains a fundamental question that needs to be addressed. In addition, NDX may also affect gene expression by modifying spatial chromatin organization, as enhanced DNA looping interactions were observed at FLC in an ndx mutant21. A similar mechanism has been hypothesized on the regulation of ABI4 through spatial interactions between NDX and PRC119.NDX is a nuclear transcription factor that appeared early in evolution and is present in vascular plants26, (ii) mediates long-range promoter-promoter interactions between developmentally regulated genes27, and (iii) orchestrates estrogen-induced enhancer-promoter looping interactions28. In Drosophila, the NDX-related zeste protein22 was shown to be required for long-range communication between promoters and for insulator bypass during gene activation30. In addition, a recent study in Arabidopsis showed that removal of the PRC1 component LHP1 from chromatin (through APOLO lncRNA-mediated R-loop formation) induces 3D chromatin conformational changes31.The possibility of NDX-mediated genome structural changes seems realistic given that in animals PRC1/RING1B (i) causes chromatin compaction through its non-enzymatic functionHerein, we characterized the global role of NDX in chromatin-based gene regulatory processes using cytological, molecular, and high-throughput approaches. Our results show that NDX is a key regulator of heterochromatin accessibility and chromatin packing in pericentromeric regions that are functionally coupled to non-CG methylation pathways.Arabidopsis seedlings expressing N-terminally and C-terminally tagged NDX fusion proteins (flag-NDX/ndx1-1(FRI)/flc-2 and NDX-GFP/ndx1-1(FRI)/flc-2, respectively) expressed from their endogenous promoter32. The two ChIP-seq profiles were highly correlated . Enrichment of NDX was validated by ChIP-PCR at selected genomic loci . This non-random distribution suggests that NDX is mainly associated with gene-poor heterochromatic regions and is rare in transcriptionally active euchromatin.To understand the genome-wide regulatory roles of NDX, we first analyzed its genomic distribution. For this, we performed chromatin immunoprecipitation sequencing (ChIP-seq) in 10-day old oci Fig. . Importamed Fig. , indicatons Fig. , in mark33, which marks nuclear periphery  coupled to fluorescence correlation spectroscopy (FCS) and fluorescence recovery after photobleaching (FRAP). CLSM performed in live root tips (expressing NDX-GFP) showed strong green fluorescence near the nuclear periphery  and in the nucleolus Fig. . Nucleol2/sec) and the nucleolus , (ii) in total nuclei , and (iii) in randomly selected regions outside the nucleus, used for background subtraction (Fbckgr). Recovery curves were obtained by normalizing the background-subtracted signal to the mean prebleach signal and, at the same time, considering the decrease of total fluorescence due to the initial high-intensity laser pulse and bleaching upon post-bleach imaging34. For the mobile fraction of NDX, we obtained a very slow recovery rate that recapitulates the slow kinetics and tight chromosome binding of nuclear core histone (H3/H4) proteins35. GFP fluorescence did not return to the initial pre-bleach value, leaving 40–60% of NDX in the slowly exchanging (static) fraction  sequencing39. The specificity of S9.6 immunoselection was monitored by RNaseH treatment, which degrades the RNA strand of RNA-DNA hybrids  and 85% of NDX peaks (n = 1910) showed no overlap  and 2.3% of DRIP peaks  still overlapped  and suggests that the NDX-stabilized R-loop model described for the FLC locus21 may still be true in some cases, though not in general. Accordingly, secondary ssDNA structure prediction showed that NDX binding sites have a lower propensity to form ssDNA than DRIP peaks or common NDX/DRIP binding sites  to those targeted by the CMT2 pathway (CMT2 TEs) or both pathways (intermediate TEs)  Fig. . The aboEs) Fig. . Of note49. Interestingly, NAD TEs associated with NDX and DRIP peaks overlap significantly , suggesting their possible functional interaction on a subset of nucleolar transposons.R-loops were mostly associated with 5′-UTRs, but also with promoters, specific transposons (e.g. NAD/En-Spm/MuDR/Copia), and euchromatic histone marks located in active chromatin Fig. . It is nArabidopsis . Interestingly, states 1/2 and 8/9, the most extreme chromatin states, lie furthest apart on the linear scale of the genome50, consistent with the reciprocal chromosomal distribution of NDX peaks and R-loops  that colocalized with NDX in a heterochromatic patch of H3K9me252.Functionally distinct small RNAs (sRNAs) were previously classified into nine functional groups and linked to different chromatin statesoci Fig.  that codets Fig. . Our metets Fig. . NDX enrets Fig. . In contndx T-DNA insertion line (ndx1–4) to assess global changes in the production of sRNAs , however, the size distribution and the ratio of dominant sRNA fractions (24nt/21nt) were similar  that showed significantly increased or decreased sRNA levels in the ndx1–4 mutant  in an NAs Fig. . Our analar Fig. , left. Oype Fig. , right. ant Fig. . About 853. The small effect of ndx1–4 mutation on miRNA transcription and the lack of association between NDX ChIP peaks and miRNA loci preclude the possibility that NDX is a central regulator of miRNA expression. In contrast, differential expression of class 6–9 siRNAs is probably due to the direct binding of NDX to these sRNA loci, which follows from their particularly strong association , we found only 10 up- and 2 downregulated miRNAs from all known 326 precursor hairpin RNAsion Fig. . Represendx1–4 mutant relative to Col-0 .  When siRNAs were sized into 21, 22, 23, 24 nt classes, 24 nt siRNAs (representing het-siRNAs generated by Pol IV) were specifically upregulated in the ndx1–4 mutant at both RdDM TEs and CMT2 TEs, as opposed to 21–22 nt siRNAs  that are related to TE expression , showed mildly increased siRNA levels by northern blot hybridization , as evidenced by the higher intensity of DRIP peaks in the ndx1–4 mutant, calculated by AUC analysis  changes in the abundance of R-loops, we globally mapped RNA-DNA hybrids in the ant Fig. . The DRIsis Fig. . Elevatesis Fig.  and DRIPsis Fig. . Based oTo assess the global effect of NDX loss on nuclear transcription, including euchromatin- and heterochromatin-derived transcripts, we performed an mRNA transcriptome analysis.p < 0.01) involving 864 up- and 1120 downregulated genes in the ndx1–4 mutant compared to Col-0 control  NRPD1b, NRPE3b, and NRPB/D/E9a, which represent the structural and regulatory subunits of Pol IV and Pol V, the core trans factors of RdDM; (2) RDR1, which participates in non-canonical RdDM57; (3) IDN2, required for siRNA accumulation and binding to dsRNA and lncRNA58; (4) AGO9, which is normally expressed in the ovule to interact with siRNAs transcribed from pericentromeric retrotransposons59; (5) ROS1, which demethylates several genomic targets to restrict non-CG methylation activity60. These RdDM genes are typically silent in seedlings (or expressed at low level) but become induced at high levels in the ndx1–4 mutant. Downregulated chromatin factors include HTA4 (histone H2A), HON4 (linker histone like protein), and HMGB1 (high mobility group B1), which are involved in the assembly of nucleoprotein complexes. RT-qPCR validation of representative genes is shown in Supplementary Fig. ndx1–4, however, causative relationships remain to be explored.In addition, we sought to find chromatin regulators that are differentially expressed in ndx1–4 and Col-0 samples revealed 2449 hypomethylated and 1597 hypermethylated regions (DMRs) in the CHH/CHG sequence context .Since loss of NDX influences heterochromatin behavior (siRNA accumulation at hundreds of genomic loci and TEs), we performed bisulfite sequencing (BS-seq) to reveal the involvement of NDX in DNA methylation . As DNA methylation pathways show significant redundancies, appropriate classification is not obvious in terms of NDX function. For instance, MET1 (the main CG methylase) and DRM2 (the main RdDM methylase) are both required to maintain CHH methylation in CMT2 targeted heterochromatin. The potential crosstalk between DRM2, MET1, and NDX remains to be explored by combining classical and molecular genetics.To explore hierarchical relationships between the DNA methylome changes of ndx1–4 mutant, we asked whether the observed molecular phenotype is related to underlying chromatin structural changes. We therefore examined the genome-wide chromatin conformations of ndx1–4 and Col-0 plants using in situ Hi-C, and sequenced ~200 million valid reads for each genotype. Normalized contact matrices resembled published Hi-C maps64 and showed similar patterns for ndx1–4 and Col-0 at first glance  and enhanced (red) Hi-C interaction network, highlighting that intrachromosomal contacts are mostly decreased, while interchromosomal contacts tend to increase in the ndx1–4 mutant  and compared them to ndx1–4. The results show that the Hi-C pattern of ndx1–4 was similar to that of the met1 and ddm1 mutants, however, overall interaction frequency changes were milder in ndx1–4 .Given the marked changes in siRNA accumulation, RNA transcription, R-loop formation, and DNA methylation in the ant Fig. . Anotherons Fig. . Interes1–4 Fig. . Several1–4 Fig. ; however1–4 Fig. . Therefoant Fig. . We propFinally, it should be noted that the chromatin binding of NDX does not scale with 3D chromatin compactness, i.e., NDX shows preferential enrichment in pericentromeric heterochromatin that is not due to the condensed state or high local DNA concentration of this chromatin type. To demonstrate this, we compared the Hi-C compactness of pericentromeric, Knob, and chromosomal arm regions in terms of NDX binding  located in transcriptionally active chromosome arms21. In this work, we show that NDX is primarily a heterochromatin regulator that functions in pericentromeric regions to control the production of het-siRNAs and deposition of repressive CHH/CHG DNA methylation. There are multiple lines of evidence to support these claims: (i) ChIP-seq measurements using two different tags in independent transgenic lines  show that NDX is strongly associated with pericentromeric regions; (ii) quantitative microscopy in live cells consistently show that NDX is a chromatin-binding factor with very slow nuclear dynamics, stably incorporated into peripheral heterochromatin; (iii) NDX preferentially associates with pericentromeric het-siRNA loci involved in non-CG methylation pathways, with significant sRNA transcriptional changes; (iv) CHH/CHG hypo-methylation of pericentromeric regions in ndx1–4 significantly overlaps with NDX binding sites; and (v) loss of NDX function results in extensive 3D chromatin structural changes in pericentromeric regions.The homeodomain protein NDX was previously described as a transcriptional and/or epigenetic regulator of two euchromatic genes , we found a significant correlation in the number of siRNA targets homologous to R-loops compared to random sequences 65, which further strengthen the link between NDX and DNA methylation pathways. It seems relevant in this regard that heterochromatin has been shown to induce significant chromosome compaction at centromeres in Sc. pombe, providing basic mechanical constraints for proper genome structure and function72.Another important question concerns the mechanism of action of NDX and potential causal relationships between structural and functional changes induced by loss of NDX. In Based on our data, the following model is proposed to summarize the function of NDX in heterochromatin homeostasis: (i) inactivation of NDX results in chromatin decompaction at highly condensed pericentromeric regions (by an as yet unknown mechanism), (ii) relaxed heterochromatin structure leads to het-siRNA accumulation and DNA methylation changes, (iii) consequently, a subset of chromatin regulatory genes and transposons become derepressed or repressed. Increased global R-loop formation is likely due to the collateral effect of NDX loss and chromatin conformational changes. Whether the NDX-mediated nuclear chromatin organization is related to the CMT2 pathway /flc-2), and NDX-GFP (genotype: NDX-GFP/ndx1-1(FRI)/flc-2). The NDX fusion proteins were expressed from their endogenous promoter. All transgenic plant lines were described previously73. Seeds were sterilized and sown on agar-MS plates, kept at 4 °C in dark for 2 days. After stratification, plates were placed to 21 °C, long day  conditions for 10 days. For immunofluorescence, NDX-GFP seedlings were grown for 10 days .The following 74. In the FCS measurements, we recorded images and carried out autocorrelation analysis (at room temperature) within selected femtoliter-sized confocal volumes using an Olympus FluoView 1000 confocal microscope combined with FCS module75. NDX-GFP molecules passing through the confocal volume were excited with the 488 nm line of an Ar-Kr laser with minimized laser intensity (~0.9 µW). The emitted photons were detected from 500 nm to 550 nm. Fluorescence autocorrelation curves were calculated online by an ALV-5000E correlator card, which recorded the time course of the signal and calculated the autocorrelation function real-time. Data acquisition time was set to 8 × 10 s at each selected point. Autocorrelation analysis was performed by the QuickFit 3.0 software  applying a 3D normal diffusion model for two-component fitting. The triplet state formation and slower dark states due to protonation were considered in the model function76.FRAP measurements were performed using an Olympus FluoView 1000 confocal microscope, based on an inverted IX-81 stand with an UPlanAPO 60 × 1.2-numeric-aperture (NA) water immersion objective. GFP was excited by the 488-nm argon-krypton laser line and fluorescence was detected through a 500–550 nm band-pass filter. For the optimal bleach depth, laser power was set to maximal intensity (~900 µW) with 500 ms duration. Cells were selected randomly in the root tip region of the plant and were bleached (in rectangular ROIs for 25 seconds) separately either in the nucleolus or in the nuclear periphery, where the NDX-GFP protein was highly expressed. Post-bleach images were acquired in 30 s intervals using low laser intensities. FRAP curves were normalized using Phair’s double-normalization protocolndx1–4 plants were harvested, and cross-linked under a vacuum chamber by phosphate buffered saline (PBS) containing 1% formaldehyde for 2 × 5 min. Excess formaldehyde was quenched by adding glycine to a final concentration of 0.125 M. Chromatin was purified and resuspended in Nuclei lysis buffer  followed by Proteinase K treatment (Thermo Fisher Scientific) and crosslink-reversal in the same step, at 65 °C, overnight. Total nucleic acid was prepared by phenol-chloroform extraction and precipitated with 2-volumes of ice cold isopropanol and 1/10 volume 4 M ammonium-acetate. Samples were then divided into two equal parts: half of the samples were treated with 8 µl of RNaseH  at 37 °C, overnight. Nucleic acid preps were diluted 4-fold in a ChIP dilution buffer  and DNA was fragmented by sonication to an average fragment size of 250–500 bp . Dynabeads Protein A magnetic beads (Thermo Fisher Scientific) were pre-blocked with PBS/EDTA containing 0.5% BSA. The RNA-DNA hybrid specific S9.6 monoclonal antibody was prepared from the Hb-8730 mouse hybridoma cell line (Atcc) using protein A/G purification and stored in PBS/0.05% (w/v) Sodium Azide buffer. To immobilize the S9.6 antibody, 50 µl pre-blocked Dynabeads Protein A slurry was incubated with 10 μg of S9.6 antibody in IP buffer  at 4 °C for 4 h, with rotation. Six micrograms of fragmented genomic DNA were added to the mixture and gently rotated at 4 °C, overnight. Beads were washed two times with 1 ml of ChIP lysis buffer , 1 ml of high-salt ChIP lysis buffer , 1 ml of wash buffer  and 1 ml of TE buffer  at 4 °C. Elution was performed in elution buffer  for 15 min at 65 °C. After purification by a NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel), DRIP samples were analyzed by quantitative real-time PCR (qPCR) performed with a LightCycler 480 SYBR Green I Master mix (Roche) and a QuantStudio 12 K Flex Real-Time PCR System (Thermo Fisher Scientific). Primer sequences are listed in Supplementary Data 2 g of 10-day-old seedlings of Col-0, and ndx1-1 (FRI)/flc-2), NDX-GFP (NDX-GFP/ndx1-1(FRI)/flc-2), and ndx1–4 seedlings (no tag control) were harvested, and cross-linked under a vacuum chamber for 2 × 5 min in PBS containing 1% formaldehyde. Excess formaldehyde was quenched by adding glycine to a final concentration of 0.125 M. Nuclei were isolated and lysed in 300 µl of Nuclei lysis buffer  followed by a 1-minute centrifugation. The pellet was resuspended in Nuclei lysis buffer and the chromatin was fragmented by sonication to an average fragment size of 250–500 bp . Immunoprecipitation and reverse crosslinking were performed as described previously39. For ChIP, 2 μg of antibodies against flag and GFP epitopes were used . Dynabeads Protein A magnetic beads (Thermo Fisher Scientific) were pre-blocked with PBS/EDTA containing 0.5% BSA. To immobilize the anti-flag or anti-GFP antibody, 50 µl pre-blocked Dynabeads Protein A slurry was incubated with 2 μg of anti-flag or anti-GFP antibody in IP buffer  at 4 °C for 4 h, with rotation. Six micrograms of fragmented chromatin were added to the mixture and gently rotated at 4 °C, overnight. Beads were washed two times with 1 ml of ChIP lysis buffer , 1 ml of high-salt ChIP lysis buffer , 1 ml of wash buffer  and 1 ml of TE buffer  at 4 °C. Elution was performed in elution buffer  for 15 min at 65 °C. Reverse cross-linking was carried out by adding 200 mM NaCl and 5 mM EDTA to the eluted product, incubated at 65 °C, overnight. After RNA and protein digestion by RNaseA and Proteinase K, DNA was isolated by a NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel). QPCR validations were performed by quantitative real-time PCR (qPCR) performed with a LightCycler 480 SYBR Green I Master mix (Roche) and a QuantStudio 12 K Flex Real-Time PCR System (Thermo Fisher Scientific).2 g of 10-day old flag-NDX (flag-NDX/ChIP-seq and DRIP-seq libraries were prepared according to Illumina’s TruSeq ChIP Sample Preparation protocol except that the uracil tolerant KAPA HiFi HotStart DNA Polymerase (Kapa Bio) was used. Briefly, the enriched DRIP and ChIP DNA samples were end-repaired and indexed adapters were ligated to the inserts. Purified ligation products were then amplified by PCR. Libraries were sequenced using 150 nt paired end read with Illumina HiSeq 2500  and 150 nt paired end read with Illumina NextSeq500 (Genomic Medicine and Bioinformatics Core Facility of the University of Debrecen).A. thaliana reference genome  using the bowtie277 algorithm. Samtools78 was used for making.bam files and indexing. Low mapping quality and PCR-duplicated reads were omitted from downstream analysis (http://broadinstitute.github.io/picard/). Deeptools79 bamCoverage was used to create RPKM (Reads Per Kilobase per Million mapped reads) normalized coverage files (.bedgraph and.bigwig). Read densities were calculated for 20 bp bins  and IP/input bigwig ratios were made by bigwigCompare. Heatmaps were generated for 10 bp bins with computeMatrix and were plotted by plotHeatmap functions of deepTools. For metagene profiles, mean normalized read coverage were calculated in 20 bp windows with computeMatrix and were plotted using plotProfile (deepTools). MACS280 was used with default settings to identify ChIP peaks in input normalized flag-NDX, NDX-GFP, and DRIP peaks in input normalized and RNaseH-corrected Col-0 and ndx1–4 samples, respectively. The identified peaks are listed in Supplementary Data Sequenced reads were aligned to the 81. Random peak sets were generated using the bedtools shuffleBed function. Observed/expected (random) ratios were plotted over genomic categories as a heatmap (in R project). The analyzed genomic categories were downloaded from Araport11 (.gff3 file). List of sRNA loci and sRNA clusters51, mircoRNAs (https://www.mirbase.org/), chromatin states50 were downloaded from the corresponding publications.Enrichment of ChIP and DRIP peaks over functional genomic categories was performed by bedtools intersectBed to calculate overlap ratiosn = 300 each) and then resized each peak to 400 bp. We also selected 400 bp long random regions as control (n = 300). We used the command line version of mfold software82 to predict secondary structures for the nucleotide sequences of each peak. Overall ssDNA forming propensity was defined as the average proportion of individual bases adopting a single-stranded conformation, considering all computed models.Propensity to form single-stranded DNA was estimated for NDX binding sites and RNA-DNA hybrids. We took a random sample of flag-NDX peaks, DRIP peaks and common (NDX/DRIP) peaks  using phenol-chloroform extraction. The quality of total RNA samples was checked by an Agilent bioanalyzer (RIN > 9). Small DNA libraries were prepared according to Illumina’s NEBNext® Multiplex Small RNA Library Prep protocol. sRNA-seq libraries were sequenced using an Illumina NextSeq500 instrument with 1 × 50 bp reads. The results were analyzed using the sRNAnalyzer pipeline83 as follows: Illumina adapters were trimmed by Cutadapt and reads were then size selected in the 19–25 nt range. Using sRNAnalyzer, size selected reads were aligned to the miRbase database (https://www.mirbase.org/) and a recently published comprehensive sRNA locus database51. Differential sRNA expression between Col-0 and ndx1–4 samples was determined by DEseq2 ) >log2(1.5)) and results were plotted in R. For sRNA target site prediction of differentially expressed sRNA reads over the identified DRIP-seq peaks, the psRNATarget algorithm was applied using default settings70.Total RNA was purified from Col-0 and 84.For sRNA qRT-PCR, we treated 5 μg total RNA with DNaseI  according to the manufacturer’s instructions. RNA samples were precipitated in absolute ethanol and resuspended in sterile water. One microgram of total RNA was reverse transcribed using an sRNA-specific primer and U6- or PP2AA3 reference RNA specific primer according to the manufacturer’s instructions . First, the stem-loop RT primer was hybridized to the sRNA molecule and then reverse transcribed in a pulsed RT reaction. Next, the RT product was PCR amplified using an sRNA-specific forward primer and a universal reverse primer. Specific primers for LTR/Copia28 and MULE1 transposon-derived sRNA molecules and reference RNA were designed according to Varkonyi-Gasic and Hellens (2010). qPCRs were performed by a LightCycler 96 Real-Time PCR machine (Roche) using Master Mix NEB M3003. Data was processed with a LightCycler® 96 software (Version 1.1.0.1320). The specific qPCR primers for sRNAs and reference RNA detection can be found inndx1–4 seedlings using the phenol-chloroform extraction method. The quality of total RNA samples was checked by an Agilent bioanalyzer (RIN > 9). cDNA libraries were prepared according to Illumina’s TruSeq RNA Library Preparation Kit v2 protocol. NGS libraries were sequenced using an Illumina MiSeq instrument with 1 × 50 bp reads . We used Salmon to map RNA-seq reads and get transcript quantities for genes and transposable elements. Reads were mapped to the A. thaliana reference transcriptome (Araport11) to quantify gene expression whilst reads were mapped to transposable elements (Araport11) themselves to estimate their expression. using Salmon. Transcript quantities were corrected for GC bias to reduce isoform quantification errors. Differentially expressed genes and differentially expressed transposable elements (ndx1–4 vs. Col-0 wild type) were identified by DESeq2. The level of significance was defined by the adjusted p-values with independent hypothesis weighting85 at p(adjusted) <0.01 for differentially expressed genes and at p(adjusted) <0.05 for differentially expressed transposable elements (DESeseq2). For data visualization, RNA-seq reads were aligned to the TAIR10 reference genome using HISAT2 allowing for reporting spliced alignments. We used deepTools bamCoverage to create RPKM (Reads Per Kilobase per Million) normalized bedgraph files. For rt-qPCR validation of differential mRNA expression changes, contaminating DNA was eliminated from total RNA preps by DNaseI digestion . DNA-free RNA samples were reverse transcribed with random hexamers using SuperScript IV reverse transcriptase (Thermo Fisher Scientific). Real time qPCR was performed with a LightCycler 480 SYBR Green I Master mix (Roche) using a QuantStudio 12 K Flex Real-Time PCR System (Thermo Fisher Scientific). mRNA expression levels were normalized to UBQ10 gene expression.Total RNA was purified from Col-0 and p < 0.01 were called significant.Singular enrichment analysis (SEA) of differentially expressed genes was performed by agriGO. Plant GO slim terms with a probability of For sRNA northern blot hybridizations, total RNA was extracted from 30 mg 10-day old seedlings. Plant material was homogenized, resuspended in an extraction buffer . The extracts were treated with phenol pH 4,3, phenol-chloroform and chloroform, precipitated in ethanol and resuspended in sterile water. 30 µg of total RNA was separated by 12% PAGE . The RNA was transferred onto Hybond-NX membranes and fixed by chemical crosslinking. The membrane was sequentially hybridized with 32P-ATP end-labeled  cDNA oligos (IDT) that detect siR1003 siRNA and U6 snRNA, respectively. Quantification of band intensities was performed by ImageJ such that siR1003 expression was normalized to U6 levels.ndx1–4 seedlings using phenol/chloroform extraction. 50, 100, and 200 ng of gDNA preps were slotted onto nitrocellulose membranes with and without RNaseH treatment  using a Bio-Dot SF Microfiltration Apparatus. Membranes were crosslinked with UV(2000) two times, and blocked in milk-TTBS  for 1 h at room temperature. The S9.6 antibody was diluted in milk-TTBS at 1:1500 and membranes were incubated overnight at 4°C. Membranes were rinsed in milk-TTBS 3 times, each time for 10 min. The secondary antibody (goat anti-mouse-HRP) was added for 1 h at room temperature, followed by washes in milk-TTBS for 5 min and 1× TTBS 3 times, each time for 10 min. The S9.6 signal was detected by ECL reagent on the film. Equal loading of samples was determined by methylene blue staining of the membranes.Genomic DNA was purified from 14-old Col-0 and 86 was used to align the bisulfite-treated reads to the TAIR10 reference genome. For the methylated sites, the methylation level is calculated using the following formula: ML = mC/(mC+umC), where ML represents the methylation level, mC and umC represent the number of methylated and unmethylated cytosines, respectively. Methylation levels were determined for CpG, CHH, and CHG sequence contexts as percentage of methylated cytosines in their contexts and integrated with JBrowse. Differentially methylated regions (DMRs) were identified by the DSS-single (DSS) pipeline considering the variance among biological replicates. To compare the DNA methylome changes of ndx1–4 plants to a large collection of DNA methylation mutants functioning in different pathways, we used the hcDMR pipeline61. High-confidence DMRs were identified by comparing the methylation changes of each mutant to the methylation levels of 54 control libraries. HcDMRs were clustered with the S-MOD method , allowing the identification of hierarchical relationships between ndx1–4 and DNA methylation mutants. SRA IDs of the mutants used in the analysis are listed in Supplementary Data Genomic DNA was prepared by standard phenol-chloroform extraction from 14-old seedlings (Col-0 and ndx1–4). 1 μg of gDNA was sent for whole genome bisulfite sequencing (BS-seq) to Novogene Ltd. Two independent biological replicates were analyzed from each background. DNA samples were fragmented into 200–400 bp using Covaris S220 and end-repaired, dA-tailed and ligated to sequencing adaptors containing only methylated cytosines. Then the DNA fragments were sodium-bisulfite treated with EZ DNA Methylation Gold Kit (Zymo Research) after which cytosines without methylation changed to U (after PCR amplification to T), while cytosines with methylation remained unchanged. 1 ng/l of PE150 library was prepared and sequenced on an Illumina NovaSeq 6000 instrument resulting in 15–17 million raw reads. The Bismark softwarendx1–4 seedlings were crosslinked with 1% formaldehyde as described in DRIP-seq experiments. According to the Arima User Guide for Plant tissue (https://arimagenomics.com/), 1 g of crosslinked plant tissue was used as a starting material for isolation of nuclei. Plant samples were ground in liquid nitrogen using a mortar. 20 ml of PTNI buffer was then added , 0.25% Triton X-100, 0.1% mercaptoethanol) and samples were purified two times by using a double-layer Mirachlot (Merk). Nuclei were washed several times in PTNI buffer as described in the Arima-HiC User Guide. Proximity ligated in situ Hi-C libraries were then constructed according to the Arima User Guide. Hi-C samples were fragmented to an average size of 400 bp . NGS libraries for Illumina sequencing were prepared using the Accel-NGS 2 S Plus DNA Library Kit and Accel-NGS 2 S Indexing Kit (Swift Biosciences). To estimate the proper number of PCR cycles for library amplification, Arima-QC2 values were determined using the KAPA Library Quantification Kit (Roche). NGS library amplifications were performed until the estimated PCR cycle numbers  using the Kapa Library Amplification Kit (Roche). NGS libraries were then sequenced on an Illumina NextSeq 500 platform  using a NextSeq 500 High Output v2 kit (Illumina). Raw Hi-C libraries (.fastq files) were processed by the Juicer toolbox  using default parameters, except that appropriate restriction enzyme cutting sites - provided by the Arima-HiC kit (GATC and GANTC) - were introduced into the script files. Hi-C reads were mapped to the A. thaliana TAIR10 reference genome and MAPQ ≥ 30 reads were retained for further analysis. Hi-C alignment statistics is summarized in Supplementary Data 87 for visual exploration and analysis of the interaction maps. Quantitative differences in Hi-C interactions (Col-0 (wild type) vs. ndx1–4 mutant) were identified at 25 kb resolution by the Slitherine pipeline (https://gitlab.pasteur.fr/gmillot/slitherine). Slitherine runs the Serpentine tool88 to smooth local noise in Hi-C interaction maps and identifies statistically significant differences between Hi-C contact matrices. Regions that have significantly different Hi-C interactions in wild type vs. ndx1–4 mutant are listed in Supplementary Data 67, with the difference that average Hi-C interaction frequencies were used within the specified genomic ranges to calculate compactness (instead of the sum of Hi-C interactions).Hi-C experiments were performed by the Arima HiC Kit (Arima Genomics). 10-day old Columbia wild type (Col-0) and Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Reporting Summary