Candida albicans between a unicellular yeast form and a multicellular hyphal form is crucial for its ability to cause disease. Interestingly, both morphological forms support distinct functions during proliferation in the human host. We previously identified ORF19.217 (C2_08890W_A), encoding a zinc-finger transcription factor of the C2H2 family, in a systematic screen of genes whose overexpression contributes to C. albicans’ morphological changes. Conditional overexpression of ORF19.217 with the strong tetracycline-inducible promoter (PTET) resulted in a hyperfilamentous phenotype. We examined growth of the orf19.217 knockout-mutant in different hypha-inducing conditions and found that the mutant still formed hyphae under standard hypha-inducing conditions. To further investigate the function of Orf19.217 in C. albicans, we combined genome-wide expression (RNA-Seq) and location (ChIP-Seq) analyses. We found that Orf19.217 is involved in regulatory processes comprising hyphal morphogenesis and iron acquisition. Comparative analysis with existing C. albicans hyphal transcriptomes indicates that Orf19.217-mediated filamentation is distinct from a true hyphal program. Further, the orf19.217 knockout-mutant did not show increased sensitivity to iron deprivation, but ORF19.217 overexpression was able to rescue the growth of a hap5-mutant, defective in a subunit of the CCAAT-complex, which is essential for iron acquisition. This suggested that Orf19.217 is involved in regulation of iron acquisition genes during iron deprivation and acts in a parallel pathway to the established CCAAT-complex. Interestingly, the orf19.217-mutant turned out to be defective in its ability to form filaments under iron-deficiency. Taken together our findings propose that the transcription factor Orf19.217 stimulates expression of the hyphal regulators EFG1 and BRG1 to promote filamentous growth under iron deprivation conditions, allowing the fungus to escape these iron-depleted conditions. The transcription factor therefore appears to be particularly important for adaptation of C. albicans to diverse environmental conditions in the human host. In regard to the newly identified functions, we have given the regulator the name Irf1, Iron-dependent Regulator of Filamentation.The alternate growth of  Candida albicans is a part of the human microbiome and predominantly colonises the gastrointestinal tract of healthy individuals  the reductive system, (ii) a siderophore uptake system and (iii) the haemoglobin iron uptake system  yeast extract, 2% (w/v) peptone and 1% (w/v) dextrose) at 30°C. The induction of hyphae was performed on the surface of solid SPIDER medium (1% (w/v) Difco nutrient broth, 2% (w/v) mannitol, 0.2% (w/v) dibasic potassium phosphate) at 37°C. Sensitivity assays were performed in YPD medium at 30°C and 10 mM BPS  was added to the medium to create iron deficient conditions. For iron complemented conditions, an additional 100 μM FeCl3 (Sigma) was supplemented to the medium. For this experiment, precultures were grown in YPD medium containing 10 mM BPS. To assess iron deficiency-induced filamentation, 150 μM BPS was added to YPD medium and cultures were incubated at 30°C for 4 to 5 days. For quantification of synthesised riboflavin, cultures were grown in Low Fluorescent medium  yeast nitrogen base (without amino acids), 0.25% (w/v) ammonium sulphate, 0.1% (w/v) complete supplement mixture  and 2% (w/v) glucose) at 30°C. For induction of PTET, the medium was supplemented with 6 µg/mL anhydrotetracycline . Solid plates were prepared with 2% (w/v) agar.Strains used in this study are listed in irf1 and hap5 knockout strains were constructed in the SN76 background strain, by combining gene replacement, using HIS1 and ARG4 as marker genes as described by Cas9 encoding sequence and the respective synthetic-guide RNAs , TF115 (ΔΔtec1), TF132 (ΔΔahr1) and TF156 (ΔΔefg1) were transformed with either pNIM1  with an oil-immersed 100x objective (1.4 N/A). For chitin staining, cells were incubated with 2 µg/mL of calcofluor white for 5 min and washed twice with PBS prior to observation. Fluorescent signal was collected with the single band pass filter A (Leica), and images were captured with a Hamamatsu ORCA II-ER cooled CCD camera. Macro photographs of individual colonies were taken from the bottom of the Petri dish using a 40x lens. Images were processed with ImageJ.irf1 knockout strain and the IRF1 overexpression strain. Overnight cultures were used to inoculate 5 mL fresh LoFloglu medium, supplemented with 6 µg/mL anhydrotetracycline, to an OD600 of 0.1 and grown at 30°C for 72 h. The OD600 of the stationary phase cultures were measured and the supernatants were cleared by centrifugation. Two hundred µL of the cleared supernatants were transferred to a microtiter black transparent 96 well plate and fluorescence emission between 420 and 720 nm was recorded immediately after excitation at 450 nm in an Infinite M Flex microtiter plate reader (Tecan). As a reference the spectrum of a 0.5 mg/L riboflavin solution in LoFloglu medium was measured.Riboflavin secretion was quantified in the TET-IRF1-HA) or the unmodified protein (CEC5387) from the TET-promoter were grown overnight in YPD medium at 30°C and then used to inoculate 50 mL of fresh YPD medium to an OD600 of 0.2. TET-promoter induction was achieved by adding 6 µg/mL ATc to the medium, and strains were allowed to grow for 4 h at 30°C. The experiment was performed in triplicate and all subsequent steps were completed as described by C. albicans genome sequence assembly 22 (candiagenome.org) using Hisat2 (Strains expressing either Irf1 with a C-terminus hemagglutinin (HA) tag  using the corresponding parental strain (CEC4664) as a reference and (ii) for strains overexpressing IRF1 from the strong TET-promoter (PTET-IRF1) (CEC5387) after 2 h and 4 h of induction using the control strain harbouring the empty vector (CIp10) (CEC5348) as a reference. All strains were grown overnight in YPD medium and diluted in fresh YPD medium to OD600 of 0.1 and grown for 4 h at 30°C. For TET-mediated overexpression the medium was supplemented with 6 µg/mL ATc, and an aliquot of the cells was taken after 2 h of growth. Cells were mechanically disrupted by bead beating with glass beads and total RNA was isolated using the RNA extraction kit from Qiagen. Libraries were prepared with the TruSeq stranded mRNA library kit (Illumina) followed by sequencing on a Hiseq2500 system (Illumina). After quality control assessment and adapter trimming, corresponding reads were mapped on the assembly 22 C. albicans genome sequence (candiagenome.org) using Hisat2  for the g Hisat2 . DEseq2 g Hisat2  was usedromoter PTET-IRF1irf1 knockout strain (CEC6056), the IRF1 overexpression strain (CEC5387) and a corresponding control strain (CEC5348) were grown overnight in YPD medium and used to inoculate 16 mL of YPD medium or YPD medium supplemented with 150 μM BPS. To induce the TET-promoter, an additional 6 µg/mL of ATc was added to the medium. The cultures were grown for 4 h at 30°C and then total RNA was extracted using the RNA extraction kit from Qiagen. Reverse transcription was performed for 2 μg total RNA with the QuantiTect Reverse Transcription kit (Qiagen). The qPCR was performed in MicroAmp Optical 96-Well Reaction Plates (Applied Biosystems) using the MX3000 Real-Time PCR System and software (Stratagene) with the 2X Takyon Rox SYBR MasterMix dTTP blue (Eurogentec) following the manufacturer instructions. Relative transcript levels of IRF1, CFL4, CFL5, FTR1, EFG1, AHR1, TEC1 and BRG1 were determined following the ΔCT method using ACT1 as a calibrator.The The corresponding oligonucleotide sequences are available in The Irf1-HA peak regions identified by ChIP-Seq were analysed using MEME-ChIP  with stahttps://elbe.hki-jena.de/fungifun/; The functional classification of identified target genes was performed by gene ontology analysis, using FungiFun2  and SGD (yeastgenome.org), respectively. Clustal omega version 1.2.4 ; the empty vector (CIp10) was integrated as a control (CEC6056). For morphological investigation, strains were grown in yeast-inducing conditions, i.e., YPD medium at 30°C for 4 hours, in the absence or presence of anhydrotetracycline (ATc), which is used to induce the TET-promoter, and then examined by microscopy. Accordingly, no morphological abnormalities were observed in the control strain upon addition of ATc hyphal growth of C. albicans.Although our results confirm that overexpression of IRF1 was overexpressed (IRF1 leads to the secretion of riboflavin (vitamin B2)  and 4 h (later time point) overexpression. Transcriptional changes included 279 upregulated and 88 downregulated genes for the earlier time point and 305 upregulated and 226 downregulated genes for the later time point . Surprisingly, only moderate concordance of modulated genes was detected between the early and late time points of IRF1 overexpression  in strains expressing either HA-tagged or unmodified  program , we iden program Table S2S. cerevisiae binding motif database (Yeastract) revealed that the identified binding motif is identical to that of Cmr3 (YPR013C), the Irf1 closest homolog in S. cerevisiae. However, protein sequence analysis revealed that the two proteins share only 19.2% identity, which is mainly conserved in the zinc finger coding sequences , cell surface , interaction with the host , filamentous growth , pathogenesis, and, interestingly, iron ion transport  and iron assimilation by chelation and transport (SIT1 and CFL5). In addition, a large number of genes encoding transcription factors (16 out of 206 target genes) were identified, including positive regulators of hyphal morphogenesis such as BRG1, TEC1, AHR1 and EFG1, but also negative regulators such as TCC1, RFG1 and NRG1  analysis was performed, considering only genes classified as direct target genes of Irf1 (obtained by ChIP-Seq). Identified target genes of Irf1 encode proteins that play a role in cell adhesion  or late (4 h) time points in the transcriptome analysis and could be assigned to the GO category filamentous growth , and components of the Ras/cAMP/PKA pathway , both involved in the regulation of morphogenesis .To gain a better understanding of the underlying mechanisms of the morphological change upon overexpression of analysis Table S2or genes . ECE1 enractions . Surpris of ECE1 , suggestogenesis , and comIRF1.A combination of different signalling pathways therefore seems to be involved in the establishment of the hyperfilamentous phenotype of IRF1 contributes to the production and secretion of riboflavin (IRF1 overexpression (2 hours), a total of 85 genes matched the genes regulated under iron deficiency, with the expression of 69 genes being equivalently activated under both conditions, and 9 genes being equivalently repressed (IRF1 overexpression (4 hours), 72 genes coincided, but in contrast to the early time point, some genes showed opposite regulation, 29 genes activated by IRF1 were repressed under iron deficiency, and 23 genes inhibited by IRF1 were activated under iron deficiency  and components of the reductive pathway  exhibited strong activation upon overexpression of IRF1 at both time points , haemoglobin utilisation (RBT5), riboflavin biosynthesis (RIB3), filamentous growth  and cell adhesion  . As previously observed, overexpression of IRF1 induced the formation of hyphae on YPD medium as indicated by the fuzzy colony edges  and proteins involved in cell adhesion , which could explain the observed cell chain phenotype of the irf1-mutant. However, a detailed analysis of the cell surface properties is needed to confirm the involvement of Irf1 in these processes.Other identified target genes of Irf1 encode components of the cell wall, in particular the cell wall of hyphae . SZ is a Pasteur Network Affiliate Program Fellow. Work in the laboratory of Cd’E is supported by the French Government’s Investissement d’Avenir program . AHC was the recipient of a post‐doctoral fellowship from the European Commission , from the French Government’s Investissement d’Avenir program  and from the National Council of Science and Technology, Mexico. VB was supported by a grant from the Pasteur-Paris University (PPU) International PhD program and the “Fondation Daniel et Nina Carasso”.We thank all members of the Unité Biologie et Pathogénicité Fongiques, particularly Laxmi Rai and Thierry Mourer, for their support and numerous insights during the course of this project. We are also thankful to Juliana Pipoli Da Fonseca for supporting our ChIP-Seq and RNA-Seq experiments at the Biomics platform at Institut Pasteur and to Gaëlle Lelandais for sharing her insights into NGS derived data analysis.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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However, cistrome dynamics in plants remain largely underexplored. Here, we compare the binding of GOLDEN2-LIKE (GLK) transcription factors in tomato, tobacco, Arabidopsis, maize and rice. Although the function of GLKs is conserved, most of their binding sites are species-specific. Conserved binding sites are often found near photosynthetic genes dependent on GLK for expression, but sites near non-differentially expressed genes in the glk mutant are nevertheless under purifying selection. The binding sites’ regulatory potential can be predicted by machine learning model using quantitative genome features and TF co-binding information. Our study show that genome cis-variation caused wide-spread TF binding divergence, and most of the TF binding sites are genetically redundant. This poses a major challenge for interpreting the effect of individual sites and highlights the importance of quantitatively measuring TF occupancy.Non-coding  Unlike microbes and mammals, cistrome dynamics in plants remains unclear. Here, using GOLDEN2-LIKE (GLK) transcription factor as an example, the authors find that most GLK binding sites are species-specific and the binding divergence is caused by cis-variations through inter-species transformation experiment. The genome-wide map of all these regulatory interactions is often referred to as the cistrome. Its dynamics enabled species and individuals with similar genes to generate different transcriptional programs and, as a consequence, different phenotypes, and contributed greatly to species’ adaptive and phenotypic plasticity3. With the help of a wide range of high-throughput sequencing techniques, such as SELEX-seq, ATAC-seq, DAP-seq, and ChIP-seq6, it is now possible to map TF binding or identify its footprint genome-wide, which gave rise to a series of cross-species TF comparisons in yeast and animal models. These studies have shown that homologous TFs with conserved biological functions shared very few binding sites in different species. For example, a pioneer study found that a pseudohyphal development-related TF binds only ~20% of the same target genes in comparison among three Saccharomyces sensu stricto species7. ChIP-seq of two conserved hepatic bZIP and HB TFs in the liver tissues of five vertebrates  also found that <10% of the binding sites are conserved8. The recent large-scale TF analysis conducted by the ENCODE projects also found a low degree of conservation of TF footprints between humans and mice10, suggesting that the animal cistrome is highly dynamic during evolution.Transcription factor (TF) binding to 13. Surprisingly, it was found that many most TF-binding sites have no apparent transcriptional effect. Further studies in higher eukaryotes with large genomes have confirmed that TFs can bind to an unexpectedly large number of sites and most of them appear to have little impact on the nearby gene transcription, suggesting high redundancy and system robustness of the transcription regulatory network18. Interestingly, the conserved TF-binding sites identified in the cross-species comparisons often have the strongest impacts on nearby gene expression19. In addition, TF-binding sites with strong regulatory potential are often located in super-enhancer regions, which are genome hotspots targeted by multiple co-binding TFs15. Therefore, it has been suggested that multiple TFs jointly contribute to the transcription output in a quantitative manner. Individual TF-binding sites may be insufficient to explain transcription, and a cluster of binding sites in the enhancer is key to achieving a precise and robust transcription regulation in the mammalian genome20.To measure the impact of TF binding on transcription, exhaustive ChIP-chip experiments have been performed in yeast to identify the binding sites of all known TFs21. Sub-functionalization of GLKs in monocots such as rice and maize has been reported22. They also play a role in fruit development, where GLK1 is switched off, and the GLK2 adapts a latitudinal gradient expression pattern resulting in uneven coloration of the fruit tissue24. Two redundant copies of GLK exist in most diploid angiosperm genomes that have been sequenced to date25. Their double loss-of-function mutants in Arabidopsis, tomato, and rice showed a pale-green leaf phenotype and down-regulation of photosynthesis genes26. Except for a few well-known target genes, the genome-wide binding profiles of GLKs have yet been determined, which makes it an ideal candidate for a cross-species cistrome comparison.Despite the wealth of data from animal and yeast models, the modes of cistrome evolution and their relative importance in plants remain underexplored. To study this, one could compare the binding of a well-known TF with conserved biological function in multiple plant species. Photosynthesis is arguably one of the most important and conserved biological processes in plants, and GOLDEN2-LIKE (GLK) TFs are well-known transcription activators controlling chloroplast biogenesis and developmentIn this study, we use ChIP-seq to map the GLK-binding sites in five representative plant species and find that most of the GLK-bound genes are species-specific. The conserved GLK-bound genes are often associated with photosynthetic function, and their expression is more susceptible to the GLK mutation. Our results reveal widespread cistrome divergence during plant evolution and the redundant nature of plant TF-binding sites.Arabidopsis , tobacco (Nicotiana benthamiana), rice (Oryza sativa), and maize (Zea may), as well as the leaf and immature green fruit tissues of tomato (Solanum lycopersicum)  were used for subsequent analysis  and those encoding subunits of photosystem I/II. We found that these genes and their homologs in the other four species all have 5’ end proximal GLK1 and GLK2 ChIP-seq peaks  and four hundred thousand GATTCC perfect matches in the maize genome. To overcome the limitation of motif search, machine learning models based on natural language processing have been developed to distinguish the TF bound and unbound regions30. One advantage of these models is that it is an ensemble of black boxes that consider multiple short sequences inside the ChIP-seq peaks, which could include motifs of the TF and its co-binding factors or other sequences that influence DNA conformation or epigenome. Such machine-learning tools have already been applied to generate accurate classifier models for 104 maize TFs30.Previous motif enrichment and protein-binding microarray analyses of ies Fig. . It shouk-mer grammar tool to train machine-learning classifiers to differentiate DNA sequences in GLK-bound regions and the background, which are ATAC-seq regions without GLK binding. The result showed that the k-mer models can indeed predict GLK binding in five species with high accuracy  together with different epigenetic features, such as chromatin accessibility, DNA methylation, and histone modifications Fig. . The GLK31. Consistently, we observed that the GLK1 and GLK2 ChIP-seq peaks overlap in promoters of photosynthetic genes  is bound by GLKs in Arabidopsis and tobacco  in tomato and rice, while cco Fig. . HoweverGO-term enrichment analysis showed that the GLK-bound genes in each species were indeed enriched for photosynthetic terms such as response to light stimulus (GO:0009416) and light harvesting (GO:0009768) Fig. . We alsoArabidopsis, tomato, and tobacco) and the monocots (maize and rice) have diverged ~150 MYA. To study how GLK bindings diverged, we used OrthoFinder to assign all GLK-bound genes in five species into ortholog groups and examined how many common and unique ones could be found in each species. The result showed that very few of them are conserved in all five species. For example, 129, 100, 151, and 205 of the Arabidopsis GLK ChIP-seq targets are conserved in 5, 4, 3, and 2 species, respectively. The remaining 375 genes have no ortholog GLK ChIP-seq targets in other species. We also repeated the conservation analysis using gene lists generated by different ENCODE2 peak calling pipelines at various stringencies. If the conservation analysis is affected by false positives or weak binding sites, the conservation rate would increase when a more stringent cut-off is used. However, when the threshold is raised, the number of identified target genes and the conservation rate remained unaffected or declined, suggesting that our analysis is robust  of them are down-regulated in the mutant. We then examined the percentage of DEGs in each conservation group . Next, we combined the binding strength and gene expression data and found that the conserved genes such as those annotated with photosynthesis functions often have a stronger ChIP-seq signal, as well as a larger reduction in gene expression in the mutant than the non-conserved ones .Since the conserved GLK ChIP-seq targets are more likely to be differentially expressed in the mutant, could it suggest that the binding sites in non-DEGs or non-conserved target genes are false positive or have no biological function? First, we noticed that over 40% of the conserved ChIP-seq target genes are not DEGs, including some well-known photosynthetic genes Fig. . For exaPsbTn is strong in Arabidopsis (rank 102nd) and weak in tomato (rank 1083rd), although it is differentially expressed in both . This shows that the binding sites in non-DEGs are under similar selection pressure and could contribute to plant fitness in a way that we have not yet understood. Hence, we shall not overgeneralize the relationship between regulatory potential and binding site conservation or binding strength.To use a different approach to evaluate the potential function of TF-binding sites, one could measure the nucleotide diversity of the site in a given population. If the site is under negative selection, it could suggest that it is important and contribute to the fitness of the species, despite the nearby gene is not differentially expressed in the TF mutant. To test this for GLK, we used the 1001 aks Fig. . We alsoaks Fig. . As expeaks Fig. , and thecis-variation). Alternatively, the evolution of the TF protein (trans-variation) could alter its binding preference. To test this, we have transformed Arabidopsis with maize GLK1 and performed ChIP-seq to identify its binding sites in a heterologous genome environment.Given the large TF-binding divergence, an intriguing question is whether it is caused by the genome sequence variation   Fig. . The coners Fig. . The groLKs Fig. . FinallyIII Fig. . Taken tArabidopsis TF DAP-seq data collection. Unsurprisingly, the differentially expressed genes (DEGs) have stronger ChIP-seq signal, a higher gene expression level in the leaf, and closer GLK-binding sites . As the promoter regions bound by the TFs are also duplicated during those events, it provides a unique opportunity to estimate whether the interaction between GLK and its targets occurred before or after one of the duplications. We found that the tomato GLKs bind to a pair of genes encoding LIGHT HARVESTING COMPLEX A4 (LHCA4) in synteny blocks in chromosomes 3 and 6. Both genes are down-regulated in the mutant  and are not located in the previously reported sub-genome regions that resulted from the recent genome triplication37, suggesting that the GLK bindings in LHCA4 have been preserved after the paleo-duplication for over 100 MYA. However, genes could be lost or rearranged after genome duplication, and as a result, most of the tomato genes are no longer located in synteny blocks. The duplicated genes could still be identified as homologous pairs that both are targeted by GLK. We calculated the percentage of homologous GLK ChIP-seq targets in the five conservation groups and found that the most conserved group 5 indeed has more homologous target genes, suggesting that the conserved binding sites are ancient, and the species-specific ones are acquired more recently.The tomato genome has experienced the ancient eudicots gamma duplication (~120 MYA) and the recent ant Fig. . These t38. Most of its duplicated genes could still be traced back to subgenomes 1 and 2. We then compared the GLK ChIP-seq signal of the duplicated ChIP-seq target genes using the Kendall rank correlation coefficient test. Kendall’s tau is 0.174 (p = 0.06322), suggesting that the correlation is very weak, and duplicated GLK binding sites have already changed after such a short evolutionary period. We also hypothesize that the rapid change in TF binding might allow the duplicated genes to be expressed in a different tissue, which could further give rise to neofunctionalization. To test this, we calculated the expression correlation of the maize GLK ChIP-seq target gene pairs in the two subgenomes using the maizeGDB’s tissue gene expression data. The result showed that the pairs that retained GLK binding have the highest expression correlation while losing GLK binding led to significantly lower expression correlation, confirming our hypothesis . Transgenic Arabidopsis plants were grown on Murashige and Skoog (MS) salts with 25 μg/mL hygromycin B, and 0.8% (w/v) agar. Plants were grown at 22 °C under long-day conditions (16-h light) in a growth chamber. Rice cultivar Nipponbare was used as the wild type. Rice plants were grown at 28 °C with long-day conditions in the greenhouse. The tomato glk1/glk2 double mutant generated by CRISRP/Cas9 in microTom background was kindly provided by Prof. Yu Pan, and we have confirmed the deletions by Sanger sequencing  were grown in a growth chamber with a 12 h-light (25 °C) and 12 h-dark (20 °C) cycle.The Arabidopsis GLKs, we fused their cDNAs with the C-terminal GFP tag and transformed them into the glk1/glk2 double mutant for ChIP-seq  GLKs ChIP-seq data were obtained from a previous study6. The maize GLK1-HA construct was also transformed into the Arabidopsis glk double mutant for ChIP-seq.For the Arabidopsis, rice, tomato, and Nicotiana benthamiana, as well as pericarp samples from immature green tomato fruits at 27 DPA, were harvested and cross-linked with 1% (w/v) formaldehyde. The nuclei were then isolated by filtration and centrifugation, and the chromatin was sonicated to sub-kb fragments using Bioruptor. Anti-GFP  and anti-HA  antibodies were used for ChIP. The protein A/G Dynabeads (10 μL) were incubated with 2 μL antibody for 1–2 h in low salt buffer . The beads were then washed with low salt washing buffer  before adding to the sonicated chromatins, were incubated overnight with the Dynabeads with antibody, and washed twice with low salt washing buffer , high salt buffer , LiCl buffer  and TE. The immunoprecipitated DNA was tagged on-bead with TS-Tn5 for 45 min. The beads were then washed twice with high salt buffer and TE. Samples were reverse crosslinked overnight at 65 °C. DNA was purified with Qiagen MiniElute and PCR amplified. The libraries were sequenced with a Hiseq X 150 bp paired-end read mode.Fully expanded leaves from Total RNA was extracted with RNeasy Mini Kit (Qiagen). Messenger RNAs were isolated with Oligo d(T)25 magnetic beads (New England Biolabs) and used for Illumina TruSeq library preparation. The libraries were sequenced on Hiseq X with 150 bp paired-end mode.Arabidopsis thaliana TAIR10, Nicotiana benthamiana version 2.5.1, Solanum lycopersicum SL4.0, Oryza sativa MSU7.0, and Zea mays RefGen_v4) using Bowtie 2 (version 2.3.2). For paired-end, 150 bp reads, the 100 bp from 3’ end were trimmed with the bowtie2 parameter −3 100. Unmapped and low mapping quality reads were filtered with SAMtools (version 1.9) using the parameters “-F 4 -q 20”. Duplicated reads were removed with the subcommand “rmdup” in SAMtools. Pearson correlation coefficients were calculated with the command “multiBigwigSummary” in deepTools to evaluate the reproducibility of the biological replicates. Only biological replicates with a correlation coefficient > 0.8 were kept for further analyses. The ENCODE2 ChIP-seq pipeline with MACS2 (version 2.2.1) and IDR (version 2.0.4.2) were used for peak calling. Peak summit positions were retrieved by using the “–call-summits” function in MACS2. The regions ±75 bp from the summit position with signal fold-change in NarrowPeak format were then supplied to IDR, and the regions passed the IDR 0.01 and summit signal foldchange cut-off were kept and resized back to 150 bp. They were then associated with genes based on summit distance to the TSS. Depending on genome size, the ChIP-seq peak summit overlaps with the putative promoter region 1.5, 2.5, 2, 1.5, and 2.5 kb upstream of the TSS were associated with genes in Arabidopsis, tobacco, tomato, rice, and maize, respectively. We also used the ENCODE2 spp-IDR-TIP pipeline from PhantomPeakQualTools (version 1.14) at two different cut-offs (p = 0.05 and p = 0.01) for comparison.ChIP-seq reads were aligned to plant reference genomes . Read counts were calculated with HTSeq (version 0.11.0). Differential gene expression analysis was performed using DESeq2.http://systemsbiology.cau.edu.cn/agriGOv2/). Gene functional category enrichments were performed using MAPMAN. The target genes in the five species were compared using OrthoFinder (version 2.2.7) to determine the homologous relationship based on protein sequence similarity. We used a conservation score to quantify how conserved a group of genes (e.g. the eudicot shared GLK target genes in one MAPMAN category) based on the events (observed) and the pair-wise comparisons performed (expected). De novo motif discovery and motif search were performed using HOMER (http://homer.ucsd.edu/homer/motif/). For the nucleotide diversity analysis, we first identified the GLK motif position in the Arabidopsis GLK ChIP-seq peaks using HOMER (cut-off: motif match score > 6). We also identified the unbound GLK motif hits in the open chromatin regions (ATAC-seq peaks) without GLK ChIP-seq signal as background. The nucleotide diversity score of each motif position is retrieved from the variant collections identified by the 1001 Arabidopsis genome project (https://1001genomes.org/data/GMI-MPI/releases/v3.1/).Functional GO enrichment analysis was performed by a web-based toolkit for the agricultural community agriGO v2.0 (k-mer grammar tool (https://bitbucket.org/bucklerlab/k-mer_grammar/src/master/) to generate machine-learning models to distinguish sequences of GLK-binding sites from sequences in open chromatin regions without GLK ChIP-seq peaks. For GLK-binding sites, we tested both the 150 and 300 bp sequences centered at the ChIP-seq peak summit. The sequences of the same length centered at the ATAC-seq peak summit are used as the background, and open chromatin overlaps with weak ChIP-seq peaks (IDR 0.05) were removed. As there are often 10 times more open chromatin regions than ChIP-seq peaks, an equal number of open chromatin regions are randomly selected to generate a balanced dataset for training the k-mer models. The balanced input datasets were then randomly split into 80% training and 20% test sets for the k-mer grammar tool.We used the Arabidopsis TF DAP-seq peaks obtained from the plant cistrome database . GLK target genes with DAP-seq peaks overlapping with the 150 bp GLK ChIP-seq summit regions were used. DAP-seq data were converted to categorical features  for training. The DAP-seq TF datasets with low features-label correlation (<0.1) were discarded. As there are more non-DEGs than DEGs, we randomly selected an equal number of the non-DEG labels to match the DEGs to obtain a balanced training dataset, and 80% of the data were used for training while the remaining 20% were used for the test. Given the small sample size, we also randomly re-select the training and test data 500 times to generate 500 models to calculate the average AUC and model scores.To study the different transcriptional regulatory functions of GLK-binding sites, we also used a random forest classifier to discriminate down-regulated DEGs from non-DEGs. Features used for RF model training are the GLK1 and GLK2 ChIP-seq signal foldchange values at the genes’ closest ChIP-seq summit, the distance from the summit to the genes’ transcriptional start site in base pair, the genes’ initial expression level in wild-type leaf in FPKM, and Ks) between homologs was computed by KaKs_Calculator (version 2.0).Detection of synteny and collinearity was performed by MCScanX with default parameters. The synonymous substitution rate (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-13Reporting Summary


INSM1 has emerged as a novel NB biomarker that plays a critical role in facilitating NB tumor cell development. Both N-Myc and INSM1 demonstrate high clinical relevance to NB. Therefore, further understanding the association of INSM1 and N-Myc functions in aggressive NB should be beneficial for future NB treatment.Neuroblastoma (NB) is a cancer that starts in certain very early forms of nerve cells of the sympathetic nervous system, most often found in an embryo or fetus. Symptoms may include bone pain, an abdominal mass, frequent urination, limping, anemia, spinal cord weakness, or bruising of the eye area. N-Myc is a key driver of high-risk NB. An elevated expression of N-Myc often predicts a poorer prognosis, in both time to tumor progression and overall survival rate. We discovered a transcription factor, MYCN amplification/elevated N-Myc expression, which contributes to the development of neural crest-derived embryonal malignancy. N-Myc is an oncogenic driver in NB. Persistent N-Myc expression during the maturation of SA precursor cells can cause blockage of the apoptosis and induce abnormal proliferation, resulting in NB development. An insulinoma-associated-1 (INSM1) zinc-finger transcription factor has emerged as an NB biomarker that plays a critical role in facilitating tumor cell growth and transformation. INSM1 plays an essential role in sympathoadrenal cell differentiation. N-Myc activates endogenous INSM1 through an E2-box of the INSM1 proximal promoter, whereas INSM1 enhances N-Myc stability via RAC-α-serine/threonine protein kinase (AKT) phosphorylation in NB. The ectopic expression of INSM1 stimulates NB tumor growth in contrast to the knockdown of INSM1 that inhibits NB cell proliferation. The clinical pathological result and bioinformatics analysis show that INSM1 is a strong diagnostic and a prognostic biomarker for the evaluation of NB progression. The INSM1/N-Myc expression shows high clinical relevance in NB. Therefore, targeting the INSM1/N-Myc-associated signaling axis should be a feasible approach to identifying new drugs for the suppression of NB tumor growth.An aggressive form of neuroblastoma (NB), a malignant childhood cancer derived from granule neuron precursors and sympathoadrenal lineage, frequently comprises  MYCN was identified as an important genetic biomarker in high-risk NB, it was not considered “druggable” in the standard care of MYCN-amplified NB patients. However, in high-risk NB, the survival rate is about 40%, with a long-term survival rate of only 15%. A successful therapeutic strategy for high-risk NB patients is urgently needed and represents an unmet societal necessity.Neuroblastoma (NB) is a cancer that starts in early nerve cells  of the sympathetic nervous system, so they can be found anywhere along this system. Most NB begins in the abdomen, either in an adrenal gland or in the sympathetic nerve ganglia. The rest starts in the sympathetic ganglia near the spine, in the chest, in the neck, or in the pelvis. Human NB is the most common childhood extracranial tumor arising from the sympathetic nervous system. It is also a clinically heterogeneous disease that ranges from spontaneous regression to high-risk stage 4 disease. This type of cancer occurs most often in infants and young children. NB accounts for about 6–7% of all cancers in children as the most common cancer in babies and the third-most common cancer in children after leukemia (26%) and brain cancer (21%). Although INSM1 promoter-driven luciferase assay, we were able to identify multiple inhibitors potently inhibiting INSM1 promoter activity as well as NB tumor cell growth [Recent studies of a neuroendocrine (NE) transcription factor, Insulinoma-associated-1 (INSM1), revealed that this zinc-finger DNA-binding protein not only regulates downstream target genes but also modulates signaling pathways critical for the stability of the N-Myc oncogenic protein [l growth . The dowl growth . When weINSM1 is an intronless gene located in chromosome 20 p11.2, encoding a 510-amino-acid polypeptide consisting of a Snail/Gfi (SNAG) domain, two proline-rich segments, four dibasic amino acid sites, a putative nuclear localization signaling sequence (NSL), and a potential amidation signal sequence (PGKR) in the N-terminus. The C-terminal portion of INSM1 contains five zinc-finger motifs, which constitute a functional domain of a DNA-binding transcription factor. The DNA-binding consensus sequence of INSM1 was determined by a selected and amplified random oligonucleotide binding assay as 5′-TG/TC/TC/TT/AGGGGG/TCG/A-3′ [Insulinoma-associated-1 (INSM1) is a zinc-finger transcription factor that was originally identified in a human insulinoma subtraction library . INSM1 iTCG/A-3′ ,7. SNAG TCG/A-3′ ,9,10. INTCG/A-3′ .INSM1 promoter (2090 bps) contains several tissue-specific regulatory elements that might contribute to a distinctive expression pattern of INSM1 dominant in tumors of NE origin, as well as displays its role in neurogenesis and NE cell differentiation during embryonic development [INSM1 gene functions as a developmentally regulated transcription factor, which transiently expresses in early NE cells and is highly reactivated in NE tumors.The expression of INSM1 was detected in early human fetal brain, endocrine pancreas development, and the tumors of NE origin including insulinoma, retinoblastoma, neuroblastoma, medulloblastoma, pheochromocytoma, Merkel cell carcinoma, medullary thyroid carcinoma, pituitary tumor, carcinoids, and small-cell lung carcinoma [elopment . During The neural crest (NC) is a transient structure during early embryonic development. NC cells from the trunk area of the neural tube generate a population of multipotent embryonic cells. The sympathoadrenal (SA) cells’ lineage is a major sub-lineage of the NC cells that further differentiate into sympathetic neurons, chromaffin, and intermediate small intensely fluorescent (SIF) cells. Sympathetic neurons and chromaffin cells are derived from a common SA progenitor, which develops from NC cells that aggregated the dorsal aorta ,14,15. BInsm1-null mutant mouse demonstrates that Insm1 acts as a crucial component in the network that controls the differentiation of SA lineage. The sympathetic neurons of Insm1-deficient mice appear to differentiate correctly but with a delay, and their proliferation reduced. In contrast, the terminal differentiation of chromaffin cells is significantly affected by the Insm1-null mutation. The fetal lethality of Insm1-mutant mice is caused by a catecholamine deficiency, which is the specific marker of SA lineage, supporting the importance of Insm1 in the development of SA lineage [Mash-1 (Ascl-1) and Insm1-deficient mice including the delay of neuronal differentiation, reduced proliferation, and impaired differentiation of chromaffin cells, suggesting that Insm1 might be the downstream target gene of Mash-1, whereas Mash-1 activates endogenous Insm1 during the differentiation of chromaffin cells [INSM1 plays an essential role in the development of chromaffin cells , pituita lineage . There iin cells .NB arises from SA progenitor cells within the NC that differentiate into sympathetic ganglion cells and adrenal catecholamine-secreting chromaffin cells ,30,31. NTH-MYCN transgenic mouse model was generated using a tyrosine hydroxylase (TH)-promoter-driven human MYCN cDNA [TH-MYCN transgenic model spontaneously developed NB, which is morphologically and phenotypically similar to human high-risk NB [TH-MYCN mouse tumor is strongly positive for the expression of Insm1 . We analyzed the correlated expression level of N-Myc and MYCN amplification from three public NB datasets derived from GEO. The bioinformatics analysis indicates that patients with MYCN amplification as high-risk NB also have significantly high levels of N-Myc expression. However, INSM1 expression correlates with MYCN amplified versus non-amplified is less significant than N-Myc expression, which shows p values of 0.12, 0.017, or 0.0077, respectively . This novel biomarker provides pathologists a useful tool to distinguish NETs from non-NETs, and it represents a more sensitive NE marker as compared to the traditional markers, such as synaptophysin, chromogranin A, and CD56 . INSM-1 INSM1 mRNA expression levels in a range of neurological cancers with NE features and other non-NE cancers were analyzed from the available datasets in an R2 Genomics Analysis and Visualization Platform  ,55. Functability . Both geINSM1 promoter-driven luciferase assay was developed [INSM1-luciferase assay as a platform in NB. The assay result provides a high Z’ factor value suggesting its effectiveness in a chemical library screen. Several chemical compounds were identified as potent inhibitors for INSM1 and N-Myc expressions, as well as readily suppressing NB tumor cell growth. Interestingly, all of these small molecules possess a common feature of modifying INSM1 promoter regulation. Potentially, interference of the N-Myc activation of an INSM1 expression mediated through binding to the E2-box of INSM1 proximal promoter could play a role in INSM1 regulation. These small molecules comprise HDAC inhibitors, natural compounds, and cellular metabolites that significantly reduce INSM1 activity [INSM1 promoter-driven luciferase screening-platform will be a valuable approach to screening additional novel small-molecules for NB cancer therapy. An eveloped . We perfactivity . The HDAactivity ,66,67. A


RNA-binding proteins (RBPs) are widely involved in the transcriptional and posttranscriptional regulation of multiple biological processes. The transcriptional regulatory ability of RBPs was indicated by the identification of chromatin-enriched RBPs (Che-RBPs). One of these proteins, KH-type splicing regulatory protein (KHSRP), is a multifunctional RBP that has been implicated in mRNA decay, alternative splicing, and miRNA biogenesis and plays an essential role in myeloid differentiation by facilitating the maturation of miR-129. In this study, we revealed that KHSRP regulates monocytic differentiation by regulating gene transcription and RNA splicing. KHSRP-occupied specific genomic sites in promoter and enhancer regions to regulate the expression of several hematopoietic genes through transcriptional activation and bound to pre-mRNA intronic regions to modulate alternative splicing during monocytic differentiation. Of note, KHSRP had co-regulatory effects at both the transcriptional and posttranscriptional levels on MOGOH and ADARB1. Taken together, our analyses revealed the dual DNA- and RNA-binding activities of KHSRP and have provided a paradigm to guide the analysis of other functional Che-RBPs in different biological systems. For example, the mammalian RBP Nova modulates both AS and poly(A) site usage.3 Moreover, recent studies have revealed that RBPs not only play important roles in posttranscriptional regulation but also interact with chromatin to regulate gene transcription. The chromatin-enriched RBP (Che-RBP) QKI5 regulates RNA processing and functions as a novel transcriptional activator during monocytic differentiation.7 RBP Lin28A binds to genes in the proximity of transcription start sites and recruits Tet methylcytosine dioxygenase 1 (Tet1) to control gene transcription in mouse embryonic stem cells.8 RNA methyltransferase-like 3 was shown to be recruited to chromatin by the transcription factor CRPBPZ to induce m6A modification of associated mRNAs in a human leukemia cell line (MOLM13).9 The WD repeat domain 43 (WDR43) is recruited to promoters by noncoding/nascent RNAs to release Pol II, thereby facilitating transcriptional elongation in embryonic stem cells.10 In addition, chromatin immunoprecipitation sequencing (ChIP-seq) analysis of K562 and HepG2 cells showed that multiple nuclear RBPs interact with chromatin to regulate gene transcription.12 These regulatory roles are essential to both physiological and disease states, with defects in RBP function causing diverse genetic alterations and abnormal cell differentiation, leading to conditions such as neurodegeneration, autoimmunity, and cancer.18RNA-binding proteins (RBPs) are generally composed of small RNA-binding domains (RBDs) such as the RNA-recognition motif,21 and their genes account for between 6% and 8% of all protein-encoding genes. Although many types of RBPs have been identified, the functions of only a few have been fully elucidated, and a dissection of RBP-RNA regulatory networks through the integration of multiple data types is essential. Such in-depth research into RBPs has been made possible by the continuous progress of technologies such as in vivo binding assays. For example, crosslinking immunoprecipitation sequencing (CLIP-seq) provides a set of candidate functional elements directly bound to each RBP, and integration of the results with knockdown/RNA-seq profiles can indicate RNA expression and regulatory splicing patterns. ChIP-seq profiles of DNA associations offer researchers the opportunity to understand the complex interconnectivity between chromatin association and RNA processing. Such integrated analyses may facilitate the identification of the roles of RBPs in broader cellular regulatory networks. Ren et al revealed that approximately 9.6% (52/544) of all annotated RBPs are commonly chromatin-enriched, and the chromatin-binding capacity of these proteins may represent a previously under-appreciated layer of gene expression regulation.7 They also defined 7 hematopoiesis-related Che-RBPs  and illustrated a novel chromatin-associating function for QKI5, which is located at the genomic loci of several target genes in monocytic cells and activates their transcription. Such genes include CXCL2, which encodes a cytokine that is essential for monocytic differentiation.24Using mass-spectrometry-based methods, hundreds of proteins that bind to RNA in human and mouse cells have been identified,25 KHSRP is important because it regulates both transcriptional and posttranscriptional processes. KHSRP was reported to bind to a sequence far upstream of the myc promoter to control26 and regulate TNF-α promoter activity.27 Additionally, several recent studies revealed that KHSRP is an essential factor for AU-rich element-directed mRNA decay28 and is a novel regulatory protein that mediates exon inclusion via an intronic splicing enhancer.25 RBPs that regulate translation are also known to contribute to the development and function of hematological lineages, as well as hematological malignancies, by acting as nodes in multiple signaling pathways.29 However, the mechanisms by which RBPs such as KHSRP regulate hematopoietic processes are still poorly understood. Previous studies revealed that granulocyte differentiation is promoted by the KHSRP−miR-129-RUNX1 regulatory axis at the expense of monocyte-macrophage differentiation.25 Consequently, a comprehensive understanding of the chromatin-binding capacity and chromatin-associated functions of KHSRP, as well as the mechanism by which it regulates mononuclear differentiation, may provide a new perspective on the role of Che-RBPs in monocytic differentiation.In this study, we concentrated on a hematopoiesis-related Che-RBP, KH-type splicing regulatory protein (KHSRP), which is a multifunctional, single-stranded nucleic acid (DNA or RNA)-binding protein.As a classical Che-RBP, we found that KHSRP not only performed a splicing regulatory function in posttranscriptional processes but also activated the transcription of several genes associated with monocytic differentiation. Therefore, our study indicated that, during monocytic differentiation, KHSRP performs regulatory functions that are mediated at both the transcriptional and posttranscriptional levels. These findings provide a paradigm that can be used to guide the analysis of other functional Che-RBPs in different biological systems.https://doi.org/10.5281/zenodo.6496559). In total, we identified 2061 AS events classified into 4 patterns as follows: 1376 skipped exons (SEs), mutually exclusive exons (MXEs), 271 alternative 3’ splice sites (A3SSs), 160 alternative 5’ splice sites (A5SSs), and 254 retained introns (RIs) . The eCLIP results showed that KHSRP tended to be associated with introns , with an average 3.98-fold enrichment, suggesting it binds to the intronic splicing elements to modulate AS . The gene annotation results showed that their functions were mainly enriched in terms related to “cell cycle,” “DNA repair,” “response to hypoxia,” and “cell-cell adhesion mediated by cadherin” . The downregulated DEGs included several hematopoiesis-related genes, such as JUNB, HMGB1, and HDAC1, while genes related to transcription factors (BCL6 and KLF3), the cell cycle (RB1 and CTNNB1), and epigenetics (EZH2) were significantly upregulated , which supports the repressive roles of KHSRP during monocyte differentiation. Functional enrichment analysis of the shKHSRP vs control groups showed the over-representation of cell-response terms, such as responses to glucocorticoid, peptides, endoplasmic reticulum stress, and cell proliferation terms, including regulation of JUN kinase activity . In contrast, the downregulated DEGs in the shKHSRP group were mainly clustered around DNA repair and cell cycle terms .To investigate the potential transcriptional regulatory activity of KHSRP, we first analyzed the differentially expressed genes (DEGs) induced by 31 We previously identified the Che-RBP KHSRP in monocytic cells; therefore, in the current study we aimed to determine how it is associated with chromatin. ChIP-seq analysis showed that KHSRP was mainly localized in genic regions . KHSRP tended to be associated with the intronic regions of these 168 targets (P = 7.19e-26)  that also colocalized with the H3K4me3 and Pol II signals. Genomic visualization of the KHSRP, H3K4me1, K3H27ac, and Pol II ChIP-seq datasets on the indicated gene loci is shown in Figure JUN), DNA-binding protein Ikaros (IKZF1), and Jun-B (JUNB), suggesting that KHSRP is recruited by these TFs .Recent studies have suggested that the association between RBPs and chromatin may be essential for the regulation of transcription.ons Fig. E and wasons Fig. E. To asshttps://doi.org/10.5281/zenodo.6496559). The 192 DEGs with changes to AS events were selected for further analysis and divided into 4 subgroups . Subgroup 1 comprised 58 genes that were downregulated and had increased alternative exon inclusion following KHSRP knockdown; GO enrichment analysis showed that these genes were significantly associated with processes such as “cell cycle” and “DNA repair.” Subgroup 2 comprised 38 genes that were downregulated and had reduced alternative exon exclusion following KHSRP knockdown; these genes were shown to participate in several functions, including “regulation of mRNA processing,” “RNA modification,” and “translation.” Subgroup 3 contained 49 upregulated genes that had increased alternative exon inclusion clustered mainly around processes such as “PI3K-Akt signaling pathway” and “regulation of MAP kinase activity.” Subgroup 4 contained 47 upregulated genes that had increased alternative exon exclusion; the genes were enriched in terms such as “regulation of hemopoiesis” and “regulation of leukocyte differentiation.”To compare the transcriptional and posttranscriptional regulatory activities of the Che-RBP KHSRP, we compared the 1662 DEGs and 2061 changed AS events (1590 genes) identified following KHSRP knockdown and divided them into 3 groups: 1470 DEGs without changes to AS events (DEG-only genes), 1398 genes without differential expression but with changes to AS events (AS-only genes), and 192 DEGs with changes to AS (Both) ; SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 1 (SMARCA1); and structural maintenance of chromosomes 4 and 5 ], protein kinases , hematopoiesis-related genes , epigenetic-related genes , kinesin family and dynamin members , and genes associated with cellular processes  . We found that both the KHSRP peaks and gene numbers displayed similar enrichment tendencies on DNA and RNA, suggesting balanced regulatory features and no significant differences among the different groups  and found 4373 DASEs in 2839 genes . The most common types of DASEs in monocytic differentiation were cassette exons (3232) and intron retention (454) and alternative 5′ (300) or 3′ splice sites (387), which were detected at similar levels . Among them, we found that 44.86% of SEs, 31.76% of RIs, 47.23% of A5SS, and 35.85% of A3SS were associated with KHSRP eCLIP peaks, accounting for nearly 40% (924) of DASEs . We integrated the relationships among AS events in PMA-treated samples, AS events following KHSRP knockdown, and eCLIP enrichment . The 327 DASEs that were detected following both shKHSRP knockdown and PMA treatment were enriched in processes such as “cellular response to DNA damage stimulus”, “DNA repair”, “RNA biosynthetic process”, and “chromosome segregation” . These genes included TATA-box-binding protein-associated factor 6 (TAF6), a binding protein that may participate in basal transcription and serve as a co-activator in promoter recognition and transcription initiation, and euchromatic histone lysine methyltransferase 2 (EHMT2), which encodes the enzyme responsible for catalyzing histone H3 methylation at lysine 9, leading to the recruitment of additional epigenetic regulators and transcription repression , and LIM domain only 2 (LMO2)  and found that 762 overlapped and accounted for nearly half of the KHSRP-regulated DEGs. Among them, 513 genes (317 commonly repressed and 196 commonly activated) were commonly affected by KHSRP knockdown and PMA treatment or were enriched in several monopoiesis-related pathways, such as “regulation of cell cycle phase transition,” “G2/M transition,” and “regulation of protein kinase activity,” supporting a regulatory function of KHSRP during monocytic differentiation , phospholipase D1 [PLD1]) and RBPs (RNA-binding motif protein 47). Taken together, these results indicate that KHSRP modulates hematopoietic gene expression at the transcriptional level during monocytic differentiation.It has been reported that the attenuation of KHSRP expression is required for monocyte differentiation.ADARB1] and Mago homolog, exon junction complex subunit [MAGOH])  Fig. A, which H]) Fig. B–C. Sashown Fig. B. The RNMAGOH and ADARB1 in monocytic cells.Taken together, our results showed that KHSRP regulates monocytic differentiation through the expression control of several hematopoietic genes at both the transcriptional and posttranscriptional levels. More importantly, combining their DNA- and RNA-binding capacities, KHSRPs are likely to function collaboratively to regulate the transcription and RNA splicing of The subcellular localization of proteins can indicate their key functionalities. Therefore, the identification of the subcellular position of proteins is important to achieve a deeper understanding of their function. It is clear that RBPs function at the posttranscriptional level to regulate different stages of the gene expression process, including RNA splicing, RNA modification, RNA transport, RNA editing, RNA decay, and mRNA translation. The world of RBP research has expanded rapidly as a result of the proteome-wide identification of proteins involved in RNA binding and function, and their association with chromatin and DNA has been reported. For example, Lin28A binds DNA in the proximity of transcription start sites and recruits Tet1 to regulate gene transcription; WDR43 is recruited to gene promoters by nascent RNAs, leading to the release of Pol II and facilitating transcriptional elongation; while HnRNPU helps maintain the 3D structure of chromatin through oligomerization with chromatin-associated RNAs. In our previous study, we revealed the accumulation of such RBPs in the nuclear chromosomes (Che-RBPs) and characterized the transcriptional activation role of QKI5 during monocytic differentiation. Therefore, it seems that the chromatin-binding capacity of RBPs may represent a previously under-appreciated layer of gene expression regulation. What remains unclear is whether these Che-RBPs have both transcriptional and posttranscriptional regulatory roles, and if so, how they coordinate their functions at these 2 levels. Here, we revealed that KHSRP co-regulates the expression of several genes, both through transcriptional activation and post-transcriptional splicing, extending our understanding of RBP functions and suggesting that the dual RNA-/DNA-binding capacity has been underestimated and may, in fact, be a common attribute of such RBPs.47 Moreover, KHSRP has been described as a direct negative regulator of type I IFN mRNA stability,48 and deletion of its expression leads to T-cell defects.25 By combining KHSRP eCLIP-seq and RNA-seq data from monocytes in this study, we showed that KHSRP modulates the alterative splicing of several essential monocytic genes, such as PTK2B, PUM1, and LMO2. We also revealed that KHSRP occupies specific genomic sites and activates the transcription of genes such as CDC25A, HMGB1, KIT, NEAT1, PBX1, and PLD1. Additionally, our results indicated that KHSRP functions collaboratively to regulate the transcription and RNA splicing of genes such as MAGOH and ADARB1 in monocytic cells.KHSRP is a multifunctional RBP that has been implicated in mRNA decay, AS, and miRNA biogenesis. Studies have demonstrated that the ability of KHSRP to posttranscriptionally regulate the expression of a variety of genes is required for cell-fate decisions, tissue regeneration, immune responses, lipid metabolism, and DNA damage responses. Our previous study focusing on RBPs that are functional during human monopoiesis revealed the essential effects of KHSRP on myeloid differentiation through the facilitation of miR-129 maturation. KHSRP has also been reported to regulate gene-specific splicing events in hematopoietic erythroid cells and demonstrated to regulate immune responses. Soonthornvacharin et al suggested that KHSRP is a regulator of the innate immune response to pathogenic challenges. As a negative regulator of antiviral signaling, KHSRP associates with the regulatory domain of RIG-I, reduces vital RNA associations with RIG-I during viral infection, and represses RIG-I activation.KHSRP is categorized as a DNA- and RBP (DRBP) that confers the capacity for powerful, coordinated control of gene expression and the ability to generate both immediate effects (by regulating RNA turnover) and long-lasting effects (by regulating transcription). One of the earliest known DRBPs, the glucocorticoid receptor, is a steroid hormone receptor shown to control the transcription of inflammatory genes and destabilize the mRNA of pro-inflammatory genes through direct RNA binding. A second example, NF90, is a versatile DRBP playing important roles in T-cell activation through the direct binding of DNA, mRNA, and miRNA, through which it controls transcription, regulates mRNA turnover and translation, and affects miRNA processing, respectively. These examples illustrate that DRBPs regulate gene expression at the transcriptional and post-transcriptional levels through their diverse DNA- and RNA-binding capacities.49 PRJNA295216 (H3K4me3 ChIP-seq datasets),50 PRJNA63443 ,51 and PRJNA382164 (Pol II ChIP-seq datasets).52 The ChIP-seq, CLIP-seq, and RNA-seq datasets for KHSRP were downloaded from the Gene Expression Omnibus, accession number GSE161943 .7ChIP-seq datasets of histone modifications and Pol II were downloaded from NCBI BioProject ID: PRJNA510375 (H3K27ac ChIP-seq datasets),Homo sapiens genome (Ensembl GRCh38.p5) using TopHat253 in the PE mode with default parameters, and uniquely mapped reads were retained for further analysis and filtered by SAMtools. HTSeq counts54 were used to calculate gene counts, and transcripts per million normalization was performed using in-house scripts. DEG analysis was conducted using DESeq2.55 DEGs were identified based on a P value of <.05 and |log2(fold change)| > log2 (1), and all genes with non-zero counts in any sample were considered.RNA-seq reads were aligned to the 56 based on the knockdown replicate bam files and their control replicate bam files within the H. sapiens genome (Ensembl GRCh38.p5) annotation file. Five types of differential AS events (DASEs) were reported: SEs, MXEs, A3SSs, A5SSs, and RIs. DASEs with abs (IncLevelDifference) of >.05, a P value of <.05, and a false-discovery rate (FDR) of <.1 were identified as significant.Differential AS events were analyzed using rMATS (v 4.0)http://wwwdev.ebi.ac.uk/).57 Details of these data sources are listed in the “Availability of data and materials” section.ChIP-seq datasets of histone modifications  and Pol II were downloaded from the European Bioinformatics Institute (H. sapiens genome (Ensembl GRCh38.p5) using Bowtie2 in the PE mode with default parameters.58 SAMtools59 was used for further analysis, and reads with mapping quality scores >30 were retained. Two biological replicates were merged to create the “Tag Directory” file by “makeTagDirectory.” Peak finding and downstream data analyses were performed using “findPeaks” by HOMER.60 KHSRP ChIP-seq datasets were analyzed using the “factor” mode with the parameter “-tbp 1 -inputtbp 1 -F 2.5 -P .00001 -L 2.5 -LP .02 -ntagThreshold 3.5”. To identify histone modification in ChIP-seq and Pol II ChIP-seq datasets, we used the “histone” and “factor” modes, respectively, with default parameters.Overall ChIP-seq dataset reads were aligned to the 61 Integrative Genomics Viewer software62 was used to visualize the predicted gene loci in the KHSRP ChIP-seq datasets. MEME-ChIP tools in the MEME online suite (http://meme-suite.org/) were used for KHSRP DNA-binding motif discovery, coupled with an e-value to determine motif enrichment and significance.63 Transcription factor (TF) motifs in the vicinity of the KHSRP motif were determined using SpaMo tools in the MEME online suite, coupled with an e-value to determine motif enrichment and significance.8The all-in-one program “annotatePeaks.pl” of HOMER was used to predict KHSRP DNA-binding sites in the genomic region. The repeatability of 2 biological replicates was evaluated according to the Pearson correlation coefficient, with read coverages for genomic regions per 1000 bp, which was generated by the “getPeakTags” program of HOMER. Histone modifications and the Pol II ChIP fragment depth around KHSRP promoter peaks (from −3000 bp to +3000 bp) were predicted by “annotatePeaks.pl” in HOMER. Overlapping peaks between each group of ChIP-seq datasets were identified using “intersectBed” in BEDTools.https://github.com/YeoLab/eclip. Raw reads with distinct inline barcodes were demultiplexed using in-house scripts, and the 10-mer random sequence was appended to the read name in the bam file for later use. Low-quality reads and adapter sequences were trimmed by cutadapt. Repetitive reads were removed by aligning reads with human repetitive element sequences in the RepBase database (https://www.girinst.org/) by STAR. Cleaned reads were mapped to the H. sapiens genome (Ensembl GRCh38.p5) by STAR.64 Duplicate PCR reads were removed by in-house scripts based on their sharing of identical random sequences. Two biological replicates were merged using “merge” in SAMtools for subsequent analysis. Peak calling and downstream data analyses were performed using Clipper software.65 Peak normalization was performed using “Peak_input_normalization_wrapper.pl” tools, available at “https://github.com/YeoLab/eclip”. CLIP-seq peaks were filtered based on a P value of < 10e-3 and fold change of > 4.The KHSRP eCLIP-seq datasets were processed in accordance with previous studies, and the eCLIP-seq data processing pipeline is available at 66 The repeatability of 2 biological replicates was evaluated by Pearson correlation coefficient, with read coverages for genomic regions per 1000 bp, which were generated by “multiBamSummary” in Deeptools.67 The relative distance between ChIP-seq peaks and neighboring CLIP-seq peaks was determined by “closest” in BEDTools. The MEME-ChIP tool in the MEME online suite (http://meme-suite.org/) was used for KHSRP RNA binding motif discovery, coupled with an e-value to determine motif enrichment and significance with the standard RNA alphabet.63Enrichment of KHSRP RNA-binding sites in the human genomic region was calculated by ChIPseeker in the R package.http://metascape.org/),68 which applies the standard accumulative hypergeometric statistical test to identify ontology terms.Gene ontology functional enrichment analysis was performed by Metascape ; the National Natural Science Foundation of China ; and CAMS Innovation Fund for Medical Sciences (2021-I2M-1-019 and 2021-I2M-1-040).


The fission yeast fbp1 gene is tightly repressed in the presence of glucose, and this gene is induced by over three orders of magnitude upon glucose starvation with a cascade of multi-layered regulations on various levels of genome and chromatin architecture. In this review article, we summarize the multi-layered transcriptional regulatory systems revealed by the analysis of the fission yeast fbp1 gene as a model system.Transcriptional regulation is pivotal for all living organisms and is required for adequate response to environmental fluctuations and intercellular signaling molecules. For precise regulation of transcription, cells have evolved regulatory systems on the genome architecture, including the chromosome higher-order structure , location of transcription factor (TF)-binding sequences, non-coding RNA (ncRNA) transcription, chromatin configuration , and the topological state of the DNA double helix. To understand how these genome-chromatin architectures and their regulators establish tight and specific responses at the transcription stage, the fission yeast  Transcriptional control is essential for all organisms to adapt to environmental changes and respond adequately to intercellular signals. Abnormalities in transcriptional regulation are associated with serious diseases, including cancer . Thus, eThese multiple layers of transcriptional regulatory networks in the genome architecture are regulated by many factors, including histone modification enzymes, ATP-dependent chromatin remodelers, and non-coding RNAs. The N-terminal tail of histones, components of nucleosomes, are frequently post-translationally modified, and the marks are generally linked to specific chromatin states (active or repressive) . Acetylafbp1 is a gene in which transcription is tightly regulated in response to environmental glucose concentration, and a series of studies over several decades have revealed the mechanism to establish glucose starvation stress-specific regulation of fbp1 transcription, which is complexly regulated by the coordination of multi-layered regulations at local chromatin-genome architectures including chromatin configurations, three-dimensional chromatin loop, local inter-TF direct interaction, and lncRNA regulation. In this review, we summarize the multi-layered regulation of fbp1 transcription and present a model for the tight and specific regulation of transcription.Although many studies have focused on each regulatory layer and regulation factor in many model organisms, there is a lack of clarity on how these multiple layered regulations manipulated by many factors on the genome architecture are coordinated to establish tight and specific regulation of transcription. Fission yeast Schizosaccharomyces pombe, the fbp1 gene, which encodes fructose-1,6-bisphosphatase (FBPase), an essential enzyme for gluconeogenesis, displays dramatic transcriptional regulation and is regulated several hundred-fold by glucose concentration [Saccharomyces cerevisiae is controlled by both glucose-dependent repression of gene transcription and glucose inactivation of the enzyme itself [S. pombe is regulated only by transcriptional control but not by direct glucose inactivation of the enzyme [In the fission yeast ntration . This enntration , wherease itself ,22. On te enzyme , which ifbp1 transcription in S. pombe were initiated by Dr. Charles S. Hoffman. He generated a strain carrying the fbp1-ura4 fusion gene, which is phenotypically uracil auxotrophic (Ura−) under glucose-rich conditions. He screened uracil prototroph (Ura+) colonies and identified several glucose-insensitive transcription (git) mutants [git-gene screening and contemporaneous studies on fission yeast sexual development by Dr. Masayuki Yamamoto determined the framework of the protein kinase A (PKA) pathway [fbp1  [ste11 gene promoter region [fbp1 transcription repression [fbp1 transcription [fbp1 transcriptional activation is not just a simple regulation of Rst2 nuclear and cytoplasmic localization. Additional segment deletion research around UAS1 found another Rst2 binding CT-rich sequence [cgs1 mutant, which fails to induce fbp1 transcription due to high PKA activity, identified another TF, CBF (CCAAT-binding factor), and global Tup family co-repressor [fbp1 promoter has not yet been identified, it is estimated by chromatin immunoprecipitation and is close to UAS2 [S. cerevisiae Tup1 is a well-studied homolog, and Tup1 is recruited to the gene promoter region via interaction with sequence-specific DNA-binding proteins [fbp1 studies provided mechanistic insights into Tup co-repressor functions to generate highly stress-specific induction of transcription. Fission yeast encodes two Tup1 co-repressor genes, tup11 and tup12, and double deletion causes impaired glucose starvation-specific induction of fbp1 mRNA, i.e., the fbp1 gene can be induced under other stresses  [fbp1 gene induction, including inhibition of open chromatin formation, stable TF binding to the target sites, and recruitment of transcription machinery to the fbp1 promoter, all of which are antagonized by many factors and mechanisms such as TFs, lncRNAs, and three-dimensional genome architecture (detailed in the following sections). This multi-step antagonistic regulation against multiple Tup11/12 mediated repressions potentially generates stress specificity as the fbp1 gene can be induced only when all antagonizing factors are activated. Studies exploring the signaling pathways and transcriptional regulation systems for the glucose-dependent control of  mutants ,28,29,30 pathway ,32,33,34ay [fbp1 A. PKA acer genes ,36  ,39,42, wsequence   . Subsequfbp1 gene regulatory regions had not yet been known. Indirect end-labeling analysis using micrococcal-nuclease (MNase)-digested chromatin was employed to reveal nuclease-hypersensitive sites, which reflect an open chromatin configuration. This revealed that chromatin-DNA around UAS1 and UAS2 was protected from MNase digestion in glucose-rich conditions, while MNase hypersensitive bands appeared at UAS1 and the region between UAS2 and TATA box 3 h after glucose starvation when fbp1 was fully expressed [fbp1 transcriptional activation. The chromatin remodeling kinetics were analyzed further in the shorter time points  after glucose starvation. It was observed that chromatin at the fbp1 upstream region was progressively converted into an open configuration, and it was induced by the several species of lncRNAs transcription through the fbp1 upstream region [fbp1–mRNA occurred [fbp1 upstream region abolished both the cascade of lncRNA transcription and progressive chromatin alteration [The importance of chromatin structure in transcriptional regulation had already been highlighted ,6. Howevxpressed . These rm region  -deletion mutants revealed that the chromatin at TATA box   are involved in this mlonRNA-transcription-mediated chromatin remodeling event [As described above, the cascade transcription of mlonRNAs upon glucose starvation induces the dynamic change of chromatin structure at m region c. Since en state ,62 , DNA topology is altered by over- and under-winding of the DNA double helix, which affects the reactivity of DNA-related reactions . Particunditions A, and itcription . In wildual cell   to a specific genome locus or decoy to sequester a certain protein for the regulation of many biological processes . With ren stress D. Genomefbp1 locus, it is important to assess whether mlonRNA is more broadly expressed in multiple gene loci and has a pivotal role in the regulation of chromosome function via modulation of chromatin structure other than in the transcription process. As shown above, some genome-wide analyses  have provided evidence of the existence of other mlonRNA-type lncRNAs in the regulation of glucose starvation stress [mlon-box [mlon-box sequence in the fission yeast genome showed enrichment of this sequence near upstream from the annotated gene TSS, indicating that this sequence works for the transcription initiation of the other loci. More interestingly, a correlation that showed close localization with the meiotic recombination hotspot has also been found. In meiosis, homologous recombination is an essential process that is initiated by the induction of DNA double-strand breaks (DSBs) at certain genome locations [mlon-box sequence arose interesting hypothesis that mlonRNAs responding in meiosis works for determination of DSB site via the modulation of chromatin architecture. With the increase in knowledge on the mechanism of mlonRNA function at the n stress ,62. In an stress . The sinmlon-box . Mappingocations . The selocations . As suchmlon-box sequence into the well-characterized meiotic recombination hotspot ade6-M26 [M26 mutation is a nonsense mutation in ade6, creating a CRE-like heptanucleotide sequence 5′-ATGACGT-3′, to which the transcription factor Atf1 binds and activates meiotic recombination [M26 transcription from the M26 mutation site in the ade6 ORF and chromatin remodeling around the M26 site for the induction of meiotic recombination, which is similar to fbp1 regulation [fbp1 regulation, in which the mlon-box is located 200 bp downstream from UAS1 comprising an Atf1 binding sequence in fbp1, the mlon-box was placed 200 bp downstream from the M26 mutation site by replacing the same size of the ade6 ORF  and CT-rich sequences (Rst2 binding sequence) located 33 and 42 bp apart, respectively. In this region, Atf1 and Rst2 were interdependently bound, as in the fbp1 UAS1 region.Around the UAS1 region, an additional Rst2 binding CT-rich sequence was identified, and thus, the two binding sites for the distinct TFs, Atf1, and Rst2, were placed in close proximity (45 bp apart) B. Indireectively , indicatfbp1 regulation, it has been found that a local chromatin loop is generated and has a critical function. With the unique Tup11/12 distribution of their binding at the fbp1 region, the other three TFs  also exhibit two peaks at UAS1 and UAS2 [fbp1 gene activation processes. This was indeed the case, since the 3C technology detected a significant interaction between UAS1 and UAS2, and this chromatin loop structure is termed the UAS-loop [fbp1 activation, Rst2 is first recruited to the CT-rich region near UAS1, which is stabilized by the coordinated proximal binding of Atf1 to UAS1, and the Rst2 is subsequently delivered to UAS2 through the UAS loop structure that brings UAS1 and UAS2 into close proximity three-dimensionally  and might contribute to providing stress specificity of the fbp1 transcription activation.In the last couple of decades, the development of chromosome conformation capture (3C) technology and its derivatives have allowed the mapping of the three-dimensional genome elements, and it is now commonly accepted that modulation of chromatin loop structure is an additional layer of transcription regulation ,80,81,82and UAS2 . These uUAS-loop . This UAsionally C. Interesionally C. Note tfbp1 gene as a model system. The layers of hierarchical genome architectures include three-dimensional genome structures, chromatin structure, DNA topology, the position of TFs binding sequences, and the coordination of these regulations establishes strict regulation of fbp1 transcription induction. In particular, Tup co-repressors have multiple repression functions, and multi-layered repression and antagonization mechanisms provide transcription stringency to achieve tight and condition specific control. Gene expression regulation should be diverse depending on the encoding protein function. Therefore, the gene that has the same transcription regulation system as the fbp1 gene is not known. However, some genome-wide analyses have shown evidence that some of the fbp1 gene regulatory layers are commonly used in many other genes, and it is expected that different combinations of the transcription regulation layers provide divergence for the gene-specific regulation of transcription. Although intensive analyses have been conducted using this model system, some unsolved important questions remain. First, although the roles of Tup11/12 as a modulator of transcription, but not simple repressor, have been established [mlon-box was clarified and the general roles of mlonRNA transcription in the regulation of chromosome functions  have been established [fbp1 during glucose starvation, and a histone acetyltransferase, Gcn5, and an ATP-dependent chromatin remodeler, Snf22, are required for chromatin remodeling upstream of fbp1 [mlon-box sequence might not be conserved in other organisms, but proteins involved in mlonRNA initiation might be conserved in other organisms.In this review, we summarize the transcription regulation mechanism organized by the multiple layered regulations on the local genome architectures, focusing on the fission yeast ablished ,43,52,78ablished , the proablished . Such hiablished . This hy of fbp1 ,62. If s