Pseudomonas aeruginosa, we confirmed that a few TF-guided C-T (or G-A) conversions occurred near their binding boxes on the genome, and a number of previously characterized and uncharacterized LasR-binding sites were detected. Further verification of AIDmut-Seq using various transcriptional regulators demonstrated its high efficiency for most transcriptional activators . We confirmed the binding of LasR, FleQ, and ErdR to 100%, 50%, and 86% of their newly identified promoters by using in vitro protein-DNA binding assay. And real-time RT-PCR data validated the intracellular activity of these TFs to regulate the transcription of those newly found target promoters. However, AIDmut-Seq exhibited low efficiency for some small transcriptional repressors such as RsaL and AmrZ, with possible reasons involving fusion-induced TF dysfunction as well as low transcription rates of target promoters. Although there are false-positive and false-negative results in the AIDmut-Seq data, preliminary results have demonstrated the value of AIDmut-Seq to act as a complementary tool for existing methods.Transcriptional factors (TFs) and their regulons make up the gene regulatory networks. Here, we developed a method based on TF-directed activation-induced cytidine deaminase (AID) mutagenesis in combination with genome sequencing, called AIDmut-Seq, to detect TF targets on the genome. AIDmut-Seq involves only three simple steps, including the expression of the AID-TF fusion protein, whole-genome sequencing, and single nucleotide polymorphism (SNP) profiling, making it easy for junior and interdisciplinary researchers to use. Using AIDmut-Seq for the major quorum sensing regulator LasR in IMPORTANCE Protein-DNA interactions (PDI) play a central role in gene regulatory networks (GRNs). However, current techniques for studying genome-wide PDI usually involve complex experimental procedures, which prevent their broad use by scientific researchers. In this study, we provide a in vivo method called AIDmut-Seq. AIDmut-Seq involves only three simple steps that are easy to operate for researchers with basic skills in molecular biology. The efficiency of AIDmut-Seq was tested and confirmed using multiple transcription factors in Pseudomonas aeruginosa. Although there are still some defects regarding false-positive and false-negative results, AIDmut-Seq will be a good choice in the early stage of PDI study. Those TF-bound DNA fragments were enriched using TF-specific antibody. In SELEX, a library of randomized 14-bp DNA ligands was constructed and incubated with interested TFs which are immobilized in a 96-well plate. Unbound DNA ligands were washed and eluted, the remaining TF-bound DNA ligands were amplified and subjected to sequencing. In DAP-seq, genomic DNA was sheared into 200-bp fragments and ligated with sequencing adaptors to generate a DNA library. Interested TFs were expressed in vitro with a fused tag and immobilized on magnetic beads. The TF-bounded DNA fragments were enriched via incubation, washing and elution similar to the SELEX method. The above three methods often involve techniques that are challenging for many researchers, like DNA fragmentation, end repair, adaptor ligation, and affinity purification of TFs. Combining these techniques into a workflow further complicates experiments, which could lead to failure in some cases.The identification of target promoters for transcription factors (TFs) remains to be a challenge when establishing genetic regulatory networks. Current methods for detecting protein-DNA binding specificity rely mainly on the sequencing of TF-bound enriched DNA fragments, such as chromatin immunoprecipitation by sequencing (ChIP-seq), systematic evolution of ligands by exponential enrichment (SELEX), and DNA affinity purification sequencing (DAP-seq) –4. In Ch–in vitro methods and may fail to detect TF-binding sites in cases where intracellular cofactors are involved in TF activation. For example, the activities of two-component response regulators are dependent on their phosphorylation by cognate sensors, and some TFs are activated by small molecules such as quorum sensing signals and metabolic intermediates. In addition, in vitro methods are defective when the activation condition for a TF is unknown. Under these conditions, in vivo methods have an advantage over in vitro methods for studying protein-DNA interactions.Besides, SELEX and DAP-seq are in vivo strategies to search for TF-binding sites across the genome. They act by introducing base methylation or transposon insertion near TF-binding sites, followed by sequencing of enriched sequences flanking the genome editing sites  into smaller fragments. These DNA fragments were ligated with a second adaptor so that only the fragments containing DpnI-digested ends have adaptors on both sides, thus can be amplified for sequencing. In the Calling cards method, interested TFs were fused with an integrase, which can initiate Ty5 integration into the genome near the TF binding sites. Genomic DNA were extracted from cells undergone Ty5 transposition, and then digested with Ty5-specific restriction enzyme that cut at sites away from their recognition motif. In this way, part of the genomic sequences flanking the TF-binding sites were included in the digested small fragments. These fragments were self-ligated to favor recircularization, amplified using inverse PCR, and sequenced. As can be seen from the above instructions, DamID-seq and Calling cards still involve multiple steps of DNA digestion, ligation, and amplification.DNA adenine methyltransferase identification and sequencing (DamID-seq) and Calling cards represent two ng sites , 6. In Din vivo method named 3D-seq was developed by fusing the cytidine deaminase DddA to the 3′ end of TFs  within a broad range of 10 kb, which is lethal to bacterial cells when essential genes were inactivated by mutation. Therefore, 3D-seq requires the exogenous expression of a DddA cognate immunity determinant induced by arabinose to antagonize DddA’s activity, so that genomic mutations only occurs when arabinose was removed. The 10 kb SNP window also precludes 3D-seq in distinguishing close TF-targets on the genome. For example, the bis-(3′–5′)-cyclic dimeric GMP (c-di-GMP) dependent transcriptional regulator FleQ in Pseudomonas aeruginosa has three binding sites that are less than 200 bp apart on the cdrA promoter, while they exhibited only one summit in the 3D-seq map  from mouse can deaminate C to U in targeted DNA sequences when accessing single-stranded DNA, resulting in C-T or G-A substitutions following DNA replication –11. Comp–ee steps , takes 68) in P. aeruginosa strain PAO1. AIDΔ is a variant of the mouse cytidine deaminase AID, with its nuclear export signal deleted and has three amino acid substitutions   inducer  in the rhlI and lasB promoters  , 13. The inducer . The lasromoters . No mutaromoters , indicatrhlI and lasB promoters, whereas a lower frequency of mutation was observed in the PA3904 and ambB promoters, and no mutation was observed in the PA4677 promoter 5 and (EA3K)5, that led to a significant reduction in mutation frequency, other linkers exhibited similar effects on the activity of AIDΔ-LasR  and carrying AID732-pJN105 vector (represented as AID732pJN_b1) was conducted. We found 51 SNPs in pJN_b1 relative to the PAO1 wild-type strain (lasIΔrhlIΔungΔlasR strain carrying AID732-LasR-pJN105 (represented as AID732LasR_b1). There was a high-frequency mutation region in the Pf4 prophage site (genes from PA0715 to PA0729) of the PAO1 genome, for reasons which remain unclear. SNPs detected in this region were also excluded. Second, sequencing depths of 100First, whole-genome sequencing of the background Δost SNPs .Stochastic deamination by AID732-LasR, which is independent of specific LasR-DNA binding, will produce false-positive SNPs in bacterial genome. To eliminate these false-positive SNPs, we extracted the shared mutations of the AID732LasR_b1 genome in three independent experiments. According to our sequencing results of AID732pJN_b1, the average probability of stochastic mutation  only one SNP was observed near most LasR-binding sites in the genome ; (ii) thOf the 105 SNPs identified in the genome of the AID732LasR_b1 strain, 80 were found in the sequence of 35 promoter-like  regions, including 16 previously reported LasR-regulated promoters  and J. TPA3347, PA5454) exhibited cooperative binding patterns . 10 of the 12 tested promoters exhibited noncooperative binding with LasR , while tpatterns . These rdeletion . LasR walasR gene was retained and AID732-LasR was allowed to initiate mutations in the lasI, rhlI, and ung triple mutant strain. A similar mutation spectrum was obtained as the result from the lasI, rhlI, ung, and lasR quadruple mutant strain, and the vast majority of SNPs were reproduced in the triple mutant strain . Therefore, the deletion of native TF genes had little effect on the output of AIDmut-Seq for LasR. However, due to the different intracellular levels of TF proteins, the above results for LasR may not be the case for other TFs. Nevertheless, for simplicity, subsequent AIDmut-Seq experiments with other TFs were conducted without TF gene deletion, i.e., in the ung single-knockout PAO1 strain.Furthermore, we checked the necessity of knocking out the native TF genes in the bacterial genome. The native P. aeruginosa  exhibited direct binding to FleQ, while the rest promoters have no binding affinity with FleQ  has 82% sequence identity with the cation/acetate symporter ActP in Escherichia coli. The above result suggests that ErdR triggers bacterial acetate intake through the regulation of YjcG expression. Other genes whose expression were significantly affected by erdR deletion includes the putative acyl-CoA dehydrogenase gene PA0508, the C4-dicarboxylate transporter gene dctA, isocitrate dehydrogenase genes icd and idh, the putative quinone oxidoreductase gene PA2680, the folate biosynthesis gene folM, the putative aromatic amino acid transporter gene PA3766, and the C5-dicarboxylate transporter gene PA5530, indicating the multiple routes of ErdR in regulating carbon metabolism.ErdR is a two-component response regulator that controls the expression of genes for ethanol oxidation, including the putative sensor kinase gene ene agmR , 20. Pren source  supplemeromoters . We quan deleted , indicatP. aeruginosa . While some expected ExsA targets were not found, such as the exoT, exoY, impA promoters . Currently, the exact reason that causes the false-negative and false-positive results of FleQ and ExsA is undetermined, possible involved factors are listed in the discussion section. Nevertheless, the above results demonstrate the value of AIDmut-Seq for various transcriptional activators.We also tested the efficiency of AIDmut-Seq using the major type III secretion system activator ExsA and the two-component regulator GacA in ruginosa –24. Halfromoters , 25. We and RsmZ . We recogntR and gntP promoters in P. aeruginosa, and the repression is released by binding of gluconate or 6-phosphogluconate to promoter bound GntR  and HmgR (hmgA)  . NotablylasI transcription and provides quorum sensing homeostasis in P. aeruginosa  and pyocyanin biosynthesis . Th. ThlasI fleQ, amrZ, and psl operon, while activates algD transcription  and 249 AmrZ-repressed promoters  , other AmrZ-binding sites previously observed from ChIP-Seq were missing. Similar to the case of RsaL, we obtained an AmrZ-binding DNA motif very close to the previously reported PMW model, and AmrZ binding sites were mapped in three of the newly identified promoters . Therefore, AIDmut-Seq identified some reliable but only a small fraction of genomic targets for both RsaL and AmrZ.The alginate and motility regulator AmrZ is a dual-functional regulator that represses the transcription of cription –36. A prF, etc.) . We applP. aeruginosa. Overexpression of them can lead to very low transcription rate of some genes, thereby precludes AID732 accession to ssDNA which is dependent on the formation of transcription open complex. Therefore, when using AIDmut-Seq for transcriptional repressors, the intracellular level of AID732-TF fusion proteins needs to be further optimized, so that the chimeric TFs are sufficient to bind their genomic targets while not overexpressed to disable the transcription of target promoters.RsaL and AmrZ are both small TFs with molecular weights of 12.3 kDa and 9.4 kDa. The fusion of the 24 kDa AID732 to these small TFs may interfere with their proper folding, thus reducing their ability to bind to target genomic sites. In addition, RsaL and AmrZ mainly act as transcriptional repressors in ChIP-seq is the most commonly used method for genome-wide mapping of TF targets in bacteria. We then compared previously published ChIP-seq data , 37, 38 Mutation sites detected by AIDmut-Seq located within 10 to 100 bp from TF binding boxes, while ChIP-seq peak summits located within a wider distance window, 1 to 1,000 bp from TF-binding boxes . The meapslA, PA2440, PA3177, PA3340, and cdrA  the expression of the AID732-TF fusion protein in the ung mutant; (ii) the extraction of genomic DNA and whole-genome sequencing; and (iii) SNP profiling. The whole procedure requires only basic techniques in molecular biology and can be finished within 1 month, thus facilitating its use by junior investigators or interdisciplinary researchers who are not familiar with complicated biological experiments.In this study, we have developed an easy, The false-negative results from AIDmut-Seq may because of the following reasons: (i), the fusion of AID732 to TFs precludes the binding of TFs to some target sites, which is probably the case in AmrZ and RsaL; (ii) AID732-initiated base conversions require its access to ssDNA, which is mainly provided in the forms of transcription initiation bubble, and some targets may have very low transcription initiation rates; (iii) the neighboring sequences of some targets are less susceptible to mutation by AID732; (iv) the expression of native TFs have a considerable competitive binding with some high-affinity targets; (v) some targets may have too low affinity to their corresponding TFs, resulting in a short dwelling time of TF-binding state, which is not enough for AID732 to initiate deamination. The exact reasons that cause the occurrence of false-negative results may differ case by case, which is not determined currently. To address this issue, further studies regarding the effect of protein fusion strategy, transcription activity measurement of target promoters, sequence bias of AID732, native TF expression, and TF-binding dynamics are needed.The false-positive results from AIDmut-Seq, as exemplified by EMSA results of FleQ, may arise from (i) the nonspecific binding of TFs, which occurs when intracellular TF level is high; (ii) stochastic genome mutations during strain construction; and (iii) TF-guided AID732 deamination of bases that are adjacent to TF targets in 3D-space. To eliminate those false-positive results, further efforts regarding the control of protein expression level, the optimization of experimental procedure to avoid stochastic mutations, and the replacement of different fusion linkers are required.The defects of AIDmut-Seq are obvious, elimination of those false-negative and false-positive results are needed in further studies. However, preliminary results have demonstrated the efficiency of AIDmut-Seq, which can serve as a complementary tool for existing methods.P. aeruginosa strain PAO1: Δung, ΔlasIΔrhlIΔlasRΔung, and ΔlasIΔrhlIΔung, ΔfleQ, ΔerdR, etc. All gene knockout mutants were obtained using a markless gene deletion protocol. . The TF gene, AID, and the linkers between them were cloned into pJNl05 via Gibson Assembly , immediately after the pBAD promoter. The exact sequences of AID variants, linkers (Table S2), and pJN105 vector are all included in Supplemental File 1. Plasmids were electroporated into the PAO1 strains. Colonies were selected on LB agar (1.5% w.t.) plates supplemented with 30 μg/mL gentamicin and verified via PCR and sequencing. Plasmids and PCR primers used in this study are included in Table S1 of Supplemental File 1.rhlI, lasB, rsaL, PA3904, ambB, and PA4677) were amplified from the genome of AID-LasR fusion strains with different fusion directions, AID variants, linker types, and culture conditions. The PCR products were sequenced using first-generation sequencing, and the mutation frequencies were calculated using the Mutation surveyor software (DNA fragments (about 500 bp) of the LasR-responsive promoters  plates supplemented with 30 μg/mL gentamicin and incubated for 16 h at 37°C. Bacteria were scraped from plates and resuspended in 1 mL of fresh LB+gen30 media to ung carrying pJN105 void vector) and AID732pJN (PAO1Δung carrying AID732-pJN105) strains were identified, and combined to generate a union SNP group, represented as SNPnegative. For each TF, the common SNPs were obtained from three parallel AIDmut-Seq experiments in the AID732TF strain, represented as SNPori. TF-guided SNPs were then obtained by excluding SNPs common to SNPnegative from SNPori, and this set was represented as SNPTF. We further deleted the SNPs of SNPTF in two cases: (i) SNPs were found in the pf4 prophage region from PA0715 to PA0729, and (ii) SNPs that appeared more than twice in AIDmut-Seq experiments for different TFs. We consider that the probability of a promoter being regulated by three different TFs is very low as these SNPs were generated through weak-specific binding of TFs to DNA. The resulting SNP group was further divided into SNPTF, promoter and SNPTF, nonpromoter, according to whether they were located in the promoter region of a gene. In the case of the SNP profiling for LasR in the ΔlasIΔrhlIΔungΔlasR strain, SNPs in the above-mentioned pJN and AID732pJN strain were replaced by SNPs in pJN_b1 and AID732pJN_b1.SNPs measured in the genome of PAO1 wild-type strain  were excluded first from all the data obtained from AIDmut-Seq experiments. Then, common SNPs measured in three parallel experiments of the pJN  was retrieved and summarized into a text file. The DNA motifs for TF binding were obtained using the online MEME tool (Pseudomonas database (www.pseudomonas.com). The reference TF-binding motifs for LexA and ExsA were redrawn from data in the CollecTF database (http://www.collectf.org).To calculate the TF-binding DNA motifs using SNPEME tool , with thLasR, ErdR, and FleQ purification was performed following protocols based on Nickel NTA column. For details, see the Supplementary Methods in Supplemental File 1. EMSAs were performed using 200 bp DNA fragments. TF protein of different concentrations were mixed with 30 ng DNA and incubated at room temperature for 30 min in binding buffer , and 100 μM 3OC12-HSL was added to all DNA-LasR binding reactions, 0.2 mM acetyl-phosphate was added to all DNA-ErdR binding reactions. Gel electrophoresis was run in 0.5lasIΔrhlI double mutant and the ΔlasIΔrhlIΔlasR triple mutant stains were used for pairwise comparison in LasR experiments. Wild type PAO1 and the fleQ mutant strains were used for pairwise comparison in FleQ experiments. Wild type PAO1 and the erdR mutant strains were used for pairwise comparison in ErdR experiments. Bacterial culture conditions were the same as that in AIDmut-Seq experiments for LasR and FleQ. For ErdR experiments, bacteria scraped from LB agar plate were resuspended into FAB medium supplemented with 30 mM glutamate + 30 mM acetate to OD~1.0 and were cultured (220 rpm shaking) for 12 h at 37°C before harvesting. Bacterial cells were harvested using centrifugation at 10000 rpm for 2 min. RNA purifications were conducted with a RNeasy minikit (Qiagen) and cDNA were generated from the extracted mRNA with a FastKing RT kit (Tiangen Biotech). RT-qPCR was performed by Hieff UNICON Universal Blue qPCR SYBR green Master Mix (YEASEN) according to the manufacturer’s instruction. Each reaction was performed in triplicate in 20 μL reaction volumes with 600 ng cDNA and 50S rRNA as an internal control. In each reaction, 200 nM RT-qPCR primers were used. RT-qPCRs were run at 95°C for 2 min , 95°C for 10 s and 60°C for 30 s (40 cycles). Real-time PCR primers are included in Table S1 of Supplemental File 1.The Δhttps://github.com/Carl-Ni/Computer-code-for-sequence-retrieval.git).The data for mutation spectra drawing are included in the Supplemental File 3. Computer codes for retrieval of sequence flanking the mutation sites are uploaded at GitHub website (


Arabidopsis H3K9 methyltransferases KRYPTONITE/SUPPRESSOR OF VARIEGATION 3–9 HOMOLOG 4 (KYP/SUVH4), SUVH5 and SUVH6 are redundantly involved in silencing of transposable elements (TEs). Our recent study indicated that KYP/SUVH5/6 can directly interact with the histone deacetylase HDA6 to synergistically regulate TE expression. However, the function of KYP/SUVH5/6 in plant development is still unclear. The transcriptional factors ASYMMETRIC LEAVES1 (AS1) and AS2 form a transcription complex, which is involved in leaf development by repressing the homeobox genes KNOTTED-LIKE FROM ARABIDOPSIS THALIANA 1 (KNAT1) and KNAT2. In this study, we found that KYP and SUVH5/6 directly interact with AS1-AS2 to repress KNAT1 and KNAT2 by altering histone H3 acetylation and H3K9 dimethylation levels. In addition, KYP can directly target the promoters of KNAT1 and KNAT2, and the binding of KYP depends on AS1. Furthermore, the genome-wide occupancy profile of KYP indicated that KYP is enriched in the promoter regions of coding genes, and the binding of KYP is positively correlated with that of AS1 and HDA6. Together, these results indicate that Arabidopsis H3K9 methyltransferases KYP/SUVH5/6 are involved in leaf development by interacting with AS1-AS2 to alter histone H3 acetylation and H3K9 dimethylation from KNAT1 and KNAT2 loci.The  Arabidopsis H3K9 methyltransferases directly interact with ASYMMETRIC LEAVES1 (AS1) and AS2 to repress KNOTTED-LIKE FROM ARABIDOPSIS THALIANA 1 (KNAT1) and KNAT2 in leaf development. KNOTTED-LIKE HOMOBOX (KNOX) genes3. Lateral organs such as leaves are initiated on the flank of the shoot apical meristem, and down-regulation of KNOX gene expression is essential to facilitate this process4. Moreover, silencing of KNOX genes is important in developing organs since ectopic KNOX expression during organogenesis results in patterning defects and hyper-proliferation of cells7. In Arabidopsis, the members of the KNOX family can be divided into three classes. Class I KNOX genes include BREVIPEDICELLUS/KNOTTED-LIKE FROM ARABIDOPSIS THALIANA1 (BP/KNAT1), KNAT2, KNTA6, and SHOOTMERISTEMLESS (STM)8. Class II KNOX genes comprise KNAT3, KNAT4, KNAT5, and KNAT7, which are broadly expressed and have been shown to function redundantly to influence lateral organ differentiation in Arabidopsis9. Class III only contains KNATM, which is a KNOX gene lacking the homeodomain10. In Arabidopsis, KNAT1 is expressed in the vegetative meristem and stem, and is down-regulated as leaf primordia are initiated6. Thus, the precise balance between the differentiation and proliferation of stem cells is achieved in part through proper regulation of KNOX expression.The initiation of leaf primordia is established by recruitment of the cells flanking the shoot apical meristem (SAM). Meristem activity in the shoot apex is specified in part by the class I KNOX repression during organogenesis is mediated by the transcription complex composed of the MYB domain protein ASYMMETRIC LEAVES1 (AS1) and the AS2/LATERAL ORGAN BOUNDARIES (AS2/LOB) domain protein AS2 in Arabidopsis16. KNAT1 and KNAT2 are mis-expressed in the leaves and flowers of the as1/as2 double mutant, suggesting that AS1 and AS2 promote leaf differentiation by repressing KNOX14. The AS1-AS2 complex (AS1/2) can recruit a chromatin-remodeling protein HISTONE REGULATORY HOMOLOG 1 (HIRA) to regulate target gene expression during organogenesis17. In addition, AS1/2 can also recruit POLYCOMB-REPRESSIVE COMPLEX 2 (PRC2) to repress KNOX genes by histone H3 lysine 27 methylation18. Collectively, these studies suggest that the repression activity of AS1/2 is associated with histone modifications.20. Lysine methylation on the side chains of histones is regulated by histone methyltransferases (HMTs) and histone demethylases (HDMs)20. Methylation on lysine 9 and 27 of histone H3 (H3K9me and H3K27me) is associated with transcription repression, while methylation on lysine 4 and 36 of histone H3 (H3K4me and H3K36me) is associated with transcription activation20. For instance, H3K9 mono-methylation (H3K9me1) and H3K9 dimethylation (H3K9me2) mainly function in repressing transposon activities. H3K9me2 is enriched in transposons and repeated sequences24. In addition, the level of histone acetylation is controlled by histone acetyltransferases (HATs) and histone deacetylases (HDACs). HATs can add acetyl groups to lysine, which loosens the chromatin confirmation and leads to transcription activation. In contrast, removing acetyl groups from lysine by HDACs leads to condensed chromatin structure and transcription repression20.Histone modifications including methylation, acetylation, phosphorylation, and ubiquitination can influence transcription, DNA repair, replication, and recombinationArabidopsis, 49 SET Domain Group (SDG) proteins have been identified, and 31 of them are known or predicted to have HKMT activity. These SDG proteins can be further classified into five classes (class I to V) based on their domain architectures or their target lysine residues25. Previous studies have revealed that the Class V SDG proteins including SUPPRESSOR OF VARIEGATION 3–9 HOMOLOG (SUVH) and SUPPRESSOR OF VARIEGATION 3–9 RELATED (SUVR) proteins are associated with H3K9 methylation involved in heterochromatin maintenance and DNA methylation29. All SUVH proteins contain a SET domain, a pre-SET domain, a post-SET domain, and a STE and RING-associated (SRA) domain. The SRA domain is responsible for recognizing methylated DNA30. KRYPTONITE , SUVH5 and SUVH6 are the best-characterized SUVH proteins in Arabidopsis and they function as histone H3K9 methyltransferases. KYP is required for the maintenance of CHG methylation controlled by CHROMOMETHYLASE 3 (CMT3)33. Furthermore, KYP, SUVH5, and SUVH6 act redundantly to silence transposable elements (TEs) by regulating H3K9me1 and H3K9me2 at their target loci. The kyp/suvh5/suvh6 triple mutant displays a loss of non-CG methylation similar to the cmt3 mutant35. The histone deacetylase HDA6 is also involved in transposon silencing36. In addition, HDA6 interacts and functions synergistically with KYP, SUVH5, and SUVH6 to co-regulate transposon silencing through histone H3K9 methylation and H3 deacetylation37.Histone lysine methyltransferases (HKMTs) have a specific conserved domain called SET  domain, which is mainly responsible for histone methylation activity. In TE silencing, their function in plant development remains elusive. In this study, we found that KYP/SUVH5/6 interacts with AS1/2 and regulates leaf development by repressing KNAT1 and KNAT2 expression through H3K9me2 and H3 deacetylation.Although it has been established that KYP/SUVH5/6 are important regulators of Arabidopsis H3K9 demethylases in plant developmental processes has been reported41, the function of KYP and SUVH5/6 in plant development remains elusive. Our recent study has revealed that KYP and SUVH5/6 interact with the histone deacetylase HDA6 and they function synergistically to regulate TE expression37. To further investigate the biological function of KYP/SUVH5/6, we analyzed the growth phenotypes of hda6 and kyp single, kyp/hda6 double, kyp/suvh5/6 triple, and hda6/kyp/suvh5/6 quadruple mutants. As reported previously42, the hda6 mutant had curling and serrated leaves. Compared to Col-0 wild type (WT), hda6, kyp and kyp/suvh5/6 mutants also displayed a slight curling leaf phenotype  assays and co-immunoprecipitation (Co-IP) assays to investigate whether KYP/SUVH5/6 can interact with AS1/2. We found that KYP, SUVH5 and SUVH6 can interact with both AS1 and AS2 in BiFC assays by using Agrobacterium-infiltrated tobacco leaves  assays with KYP, AS1, and AS2 recombinant proteins. The results showed that KYP interacted with AS1 and AS2 in vitro Fig. . The aveVarious deletion constructs of AS1 and AS2 were also generated to determine the domains responsible for their interaction with KYP using BiFC assays Fig. . Althoughda6/kyp/suvh5/6 quadruple mutant displayed a defective leaf phenotype similar to as1 and as2 . The previously identified AS1/2 binding sites (X and Y)17 and other regions such as the promoter (P), first exon (S) and coding region (E) of KNAT1 and KNAT2 were selected for ChIP-qPCR analysis . The protein levels of KYP in KYPpro::KYP:FLAG/as1 and KYPpro::KYP:FLAG were similar  using the AT2 Fig. , which iies Fig. . In Arabons Fig. . Indeed,ons Fig. .Fig. 5GeTE genes  in Arabidopsis. We found that there was no significant difference in KYP binding in coding genes and TEs  data of AS1KYP Fig. , supportons Fig. , support WT Fig. . Collectn repeat sequences . We found that the KYP-targeted genes are involved in multiple biological processes, including abscisic acid (ABA)/stress responses and different development pathways ” was enriched in the KYP-targeted genes ces Fig. . Intereship Fig. . In addiays Fig. . The invnes Fig. , supportKNAT1 and KNAT2, previous studies indicated that AS1/2 can deactivate the abaxial genes ETTIN/AUXIN RESPONSE FACTOR3 (ETT/ARF3), AUXIN RESPONSE FACTOR 4 (ARF4) and YABBY5 (YAB5)53. We found that KYP and HDA6 can also target the TSS region of ARF4 , GROWTH-REGULATING FACTOR 4 (GRF4) and CYCLIN DEPENDENT PROTEIN KINASE 2 (CDKC2)  Fig. . The claAT2G12520 and ZINC RIBBON 3 (ZR3). In addition, we also found that the KYP and HDA6 binding sites on these target genes are substantially closed to the (AG/AAG)n repeat motif  based on their domain architectures or their target lysine residues25. There are 15 class V SDG proteins including 10 SUVH proteins and 5 SUVR proteins in Arabidopsis. Several class V SDG proteins have been found to be associated with H3K9 methylation involved in heterochromatin maintenance and DNA methylation29. SUVHs contain an N-terminal SRA domain and a SET domain at the C-terminus65. The SRA domain is required for direct binding to methylated DNA65. KYP, SUVH5, and SUVH6, the best characterized SUVH proteins in Arabidopsis, are H3K9me1/2 methyltransferases responsible for chromatin silencing33. Two other SUVH proteins, SUVH2 and SUVH9, are inactive for histone methyltransferase activity, but they can recruit RNA polymerase V to chromatin by associating with the DDR (DRD1 peptide-DMS3-RDM1) complex67. In addition, the SUVR proteins SUVR4 and SUVR5 have been found to be involved in H3K9me in vivo70. Collectively, these studies indicate that the class V SDG proteins are important in gene silencing by regulating H3K9me.In 72. Recent studies have also shown that H3K9me2 is important in regulating gene expression in Arabidopsis development41. Although KYP and SUVH5/6 have been identified as crucial regulators of H3K9me2 in Arabidopsis, their function in plant development remains elusive. In the present study, we found that KYP and SUVH5/6 are functionally associated with HDA6. Furthermore, HDA6 and KYP/SUVH5/6 function synergistically to regulate the core leaf development genes, including KNAT1 and KNAT2. A recent study also demonstrated that another class V SDG protein, SUVH9, is involved in embryonic development by regulating asymmetric DNA methylation72. Taken together, these results indicate that the Class V SDG proteins including KYP and SUVH5/6 play important roles in plant developmental processes.H3K9me2 is a crucial histone modification marker during embryo development in both plant and mammalian systemsKNAT1 and KNAT2 are class I KNOX homeobox genes and play important roles in meristem development and leaf morphogenesis73. Previous studies have demonstrated that the expression of KNAT1 and KNAT2 is associated with the changes in H3Ac, H3K9me2, and H3K27me374. In this study, we found that KYP/SUVH5/6 and HDA6 function synergistically to regulate KNAT1 and KNAT2 by altering H3K9me2 and H3Ac levels. Furthermore, the expression of the KNOX genes was increased in the hda6/kyp/suvh5/6 quadruple mutant compared with hda6 and kyp/suvh5/6. Similarly, the expression of TEs was also increased in the hda6/kyp/suvh5/6 quadruple mutant compared with hda6 or kyp/suvh5/637. These results suggest that both H3K9me2 decreases and H3Ac increases are required for gene activation. Interestingly, it has been shown that there is an antagonistic pattern of H3K9me2 and H3Ac enrichment during embryogenesis in both plants and mammals76, indicating a functional crosstalk between H3K9me2 and H3Ac in developmental processes. Our recent studies demonstrated that Arabidopsis HDA6 is also functionally associated with the H3K4 demethylases LDL1/2 and FLD79. It remains to be determined whether KYP/SUVH5/6 are also functionally associated with H3K4 demethylases.81. Previous studies have demonstrated that the interactions between the core protein components of Mi2/NuRD and CoREST complexes are relatively stable. However, they can dynamically interact with different transcription factors depending on environmental conditions83, indicating that HDAC complexes require various transcription factors to recognize specific genomic regions. In this study, we found that KYP and SUVH5/6 can directly interact with AS1-AS2 and regulate the expression of KNAT1/2 by altering H3Ac and H3K9me2 levels. In addition, the binding of KYP to KNAT1 and KNAT2 was reduced in the absence of AS1, indicating that KYP is recruited by AS1 to the KNAT1/2 loci.In yeast and animal systems, HDACs are the core components of several multi-protein complexes, such as Mi2/NuRD and CoREST29. The triple mutant of the non-CG DNA methylases, drm1/drm2/cmt3 (ddc), is defective in leaf development with decreased hypocotyl elongation, which is associated with increased expression of the F-box domain gene SUPPRESSOR OF drm1 drm2 cmt3 (SDC)85. In addition, the DNA methylation mediated by SDC is associated with periodic adjustment of circadian rhythm85. The involvement of HDA6-mediated histone modifications in the regulation of circadian rhythm has also been reported79. Interestingly, AS1-AS2 is also required for maintaining DNA methylation on ETT/ARF355, suggesting that AS1-AS2 and KYP/SUVH5/6 may also function together in the regulation of DNA methylation. Recent studies indicated that AS2 is highly associated with chromocenter including ribosomal DNA repeat regions, and is involved in the regulation of cell division87. In addition, the abaxial genes ETT/ARF3 and ARF4 can be indirectly repressed by AS1/2 through the trans-acting siRNA (tasiRNA) called tasiR-ARFs87. It remains to be determined whether KYP/SUVH5/6 are also involved in these processes.Accumulation of H3K9me2 is highly associated with DNA methylation at CHG and CHH sitesKNAT1/2, other leaf development genes including KNAT3, KNAT5, NUC1, GRF4 and CDKC257 are also regulated by HDA6-KYP/SUVH5/6. GO-BP analysis indicates that KYP-targeted genes are associated with stress responses, hormone responses and different developmental processes. It has been shown that KYP is involved in regulating seed dormancy by repressing ABA signaling genes51. Furthermore, SUVH5 can act as a positive regulator of light-mediated seed germination88. Interestingly, we found that the GO-terms “response to abscisic acid” and “response to light stimulus” were also enriched in KYP-targeted genes. Taken together, these results indicate that KYP/SUH5/6 is involved in various developmental processes and pathways. By analyzing the genome-wide occupancy profile of KYP, we found that the binding of KYP was highly enriched in promoter regions, and most of the KYP-targeted genes are protein coding genes. Furthermore, the binding of KYP is highly correlated with the binding of AS1 and HDA6. Together, these data support the notion that AS1/2 recruits the transcriptional repression complex containing HDA6 and KYP/SUVH5/6 to regulate gene expression.In addition to KNOX genes KNAT1 and KNAT2 via H3K9me2 and H3 deacetylation. In addition, the HDA6-KYP/SUVH5/6 histone modification complex can also regulate gene expression involved in other developmental processes.In conclusion, this study provides insight into understanding how the H3K9 methyltransferases KYP and SUVH5/6 are involved in leaf development by interacting with AS1-AS2 Fig. . The AS1Arabidopsis  plants were germinated and grown in 22°C under long day (LD) (16 h light /8 h dark cycle) conditions. kyp/suvh4-3, (SALK_130630), suvh5 (GABI_263C05), suvh6 (SAIL_1244_F04), hda6-6 (axe1-5) as well as the kyp/suvh5/suvh6 (kyp/suvh5/6) triple mutant and the hda6/kyp/suvh5/suvh6 (hda6/kyp/suvh5/6) quadruple mutant were reported previously89. The hda6/kyp double mutant was generated by crossing kyp (suvh4-3) and hda6-6 (axe1-5). All mutants used in this study are in the Col-0 background.KYP, SUVH5, SUVH6, AS1, and AS2 were reported in the previously published studies42. To generate the KYPpro::KYP:GFP and KYPpro::KYP:3xFLAG constructs, the 5 kb KYP genomic DNA sequence containing the 2 kb KYP native promoter was PCR-amplified and cloned into the pCR8/GW/TOPO vector (Invitrogen), then recombined into a modified pEarleyGate302 vector containing the 3xFLAG tag or PMDC107 vector with the mGFP tag. The maltose-binding protein (MBP) fused AS1 (MBP-AS1) and AS2 (MBP-AS1) were reported previously90. KYP CDS was cloned into the pMAL-c5v vector to generate MBP-KYP.The full-length coding sequences (CDS) of KYPpro::KYP:GFP/kyp and KYPpro::KYP:3xFLAG/kyp transgenic plants were generated by transforming KYPpro::KYP:GFP or KYPpro::KYP:3xFLAG into the kyp mutant by the floral dip method. To express KYPpro::KYP:3xFLAG in the as1 mutant background, KYPpro::KYP:3xFLAG plants were crossed with the as1 mutant.KYP, SUVH5, SUVH6, AS1 and AS2 were PCR-amplified and cloned into the pCR8/GW/TOPO vector (Invitrogen), and then recombined into the YN vector pEarleyGate201-YN and the YC vector pEarleyGate202-YC. Constructed vectors were transiently transformed into Arabidopsis protoplasts or tobacco (Nicotiana benthamiana) leaves. Transfected protoplasts or leaves were then examined by using a TCS SP5 confocal spectral microscope imaging system .To generate the constructs for BiFC assays, full-length or truncated cDNA fragments of https://www.lifetechnologies.com/).For co-immunoprecipitation assays, anti-GFP  and anti-AS1  antibodies were used as primary antibodies for Western blot, the resulting signals were detected by using a Pierce ECL Western blotting kit . To measure the binding ability among AS1, AS2, and KYP recombinant proteins, the quartz crystal microbalance (QCM) technique was applied. The pairwise protein-protein pairs were analyzed using an AffinixQN QCM biosensor . To determine Kd, one has to describe the relationship between resonance and the number of proteins on the surface undergoing adsorption by applying the Langmuir equation91.MBP-KYP, MBP-AS1, and MBP-AS2 recombinant proteins were expressed using 2SO4 and H2O2 in a 3:1 ratio) and incubated with 1% SDS for 5 min. Then, 440 μL of reaction buffer ) was applied to the dried sensor to balance and set up the magnetic stir frequency at 1000 rpm at 25 °C. For the AS1-KYP pair, 6 μL AS1 protein (2.0 mg/mL) was injected into the reaction buffer and immobilized on the Au electrode plate until saturation. Next, 4 μL KYP protein (2.0 mg/mL) was injected. The injection process was repeated until the frequency curve reaches saturation. The frequency change values were recorded as multiple binding curves using the AffinixQN v2 software . Data obtained from three independent repeats were processed using AQUA v2 software .Prior to usage, the QCM biosensor was cleaned twice with 3 μL of piranha solution (HUbiquitin10 (UBQ10). The Cq and relative expression level are calculated by the Biorad CFX Manager 3.1 based on the MIQE guidelines. Standard deviations represent at least 3 technical and 2 biological replicates. The variance in average data is represented by SEM (standard error of the mean). The SD (standard deviation), SEM determination and P-value were calculated using Student’s paired t test. The gene-specific primers used for qRT-PCR are listed in Table Total RNA was isolated using TRIZOL reagent  according to the manufacturer’s instructions. Two micrograms of total RNA treated with DNAse  were used to synthesize cDNA . RT-qPCR  was performed using iQ SYBR Green Supermix solution . The CFX96 Real-Time PCR Detection System  was used with the following cycling conditions: 95 °C for 10 min, followed by 45 cycles of 95 °C for 15 s, 60 °C for 30 s, and then fluorescent detection. This was immediately followed by a melting curve analysis  to confirm the absence of non-specific products. Each sample was quantified at least in triplicate, and normalized by calculating delta Cq (quantification cycle) to the expression of the internal control P-value were calculated using Student’s paired t test.Chromatin extracts were prepared from seedlings treated with 1% formaldehyde. Chromatin was sheared to the mean length of 500 bp by sonication, proteins and DNA fragments were then immunoprecipitated using antibodies against anti-FLAG , H3Ac , H3K9me2  or total H3 . The DNA cross-linked to immunoprecipitated proteins were reversed, and then analyzed by real-time PCR using specific primers Table . PercentArabidopsis genome (TAIR10)92. Two independent KYPpro::KYP:3xFLAG/kyp transgenic lines were used as biological replicates for ChIP-seq experiment. Approximately 24 and 16 million mapped reads of KYPpro::KYP:3xFLAG transgenic line #1 and #4 were used for analysis . The alignments were first converted to Wiggle (WIG) files using deepTools. The data were then imported into the Integrated Genome Viewer (IGV)93 for visualization. The distribution of the ChIP binding peaks was analyzed with ChIPseeker  was used to represent the ratio of the total Arabidopsis genomic regions. To identify DNA motifs enriched sites, 400-bp sequences encompassing each peak summit (200 bp upstream and 200 bp downstream) were extracted and searched for enriched DNA motifs using MEME-ChIP with the default parameters 95.2 ng of DNA from ChIP was pooled to ensure that there are enough starting DNA for library construction. The ChIP DNA was first tested by qPCR and then used to prepare ChIP-seq libraries. End repair, adaptor ligation, and amplification were carried out using the NEBNext® Ultra™ II DNA Library Prep kit (cat no. E7645) according to the manufacturer’s protocol. The Novoseq PE150 was used for high-throughput sequencing of the ChIP-seq libraries. The raw sequence data were processed using the GAPipeline Illumina sequence data analysis pipeline. Bowtie2 was then employed to map the reads to the The KYP:FLAG ChIP-seq short read data have been submitted to the NCBI Gene Expression Omnibus (GEO) database (GSE195735).p-values calculated by paired two-tailed Student’s t test were used to identify significant difference between controls and samples, as described in each figure legends.All graphical data represent the mean ± standard deviation of at least three biological replicates as described in figure legends. Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


MEIS1 locus in a human AML cell line. Using this model, we have delineated and dissected a critical enhancer region of the MEIS1 locus for transcription factor (TF) binding through in silico prediction in combination with oligo pull-down, mass-spectrometry and knockout analysis leading to the identification of FLI1, an E-twenty-six (ETS) transcription factor, as an important regulator of MEIS1 transcription. We further show direct binding of FLI1 to the MEIS1 locus in human AML cell lines as well as enrichment of histone acetylation in MEIS1-high healthy and leukemic cells. We also observe a positive correlation between high FLI1 transcript levels and worse overall survival in AML patients. Our study expands the role of ETS factors in AML and our model constitutes a feasible tool for a more detailed understanding of transcriptional regulatory elements and their interactome.Myeloid ecotropic virus insertion site 1 (MEIS1) is essential for normal hematopoiesis and is a critical factor in the pathogenesis of a large subset of acute myeloid leukemia (AML). Despite the clinical relevance of MEIS1, its regulation is largely unknown. To understand the transcriptional regulatory mechanisms contributing to human MEIS1 expression, we created a knock-in green florescent protein (GFP) reporter system at the endogenous  MEIS1 expression via shRNA mediated knockdown has been shown to significantly reduce leukemic stem cell potential [MEIS1 with the long-term goal of identifying ways to alter MEIS1 expression could be beneficial for developing new therapies in MEIS1-dependent leukemias [Myeloid ecotropic virus insertion site 1 (MEIS1) is a HOX co-factor known to be necessary for normal hematopoiesis  and it iotential . Identifeukemias .MEIS1 locus spans some 175 kb of the genome and recent findings suggest that this large genomic region encompasses multiple regulatory regions including several enhancers. Two such studies have identified candidate cis-regulatory regions based on sequence conservation and with a focus on regulation of MEIS1 in early development [MEIS1 expression and identified three candidate enhancer regions based on epigenetic markers [MEIS1 expressing cells, an active E2 region was more associated with high MEIS1 expressing cell lines and an active E3 region was more associated with medium level MEIS1 expressing cell lines [MEIS1. A recent report by Lin et al. indicates the region encompassing E1 and E2 as a super-enhancer in Ewing sarcoma with high H3K27Ac correlating to high MEIS1 expression [The elopment , 11. In  markers : enhancell lines . Using cpression . In the pression , 15 to f2 in RPMI 1640 supplemented with 10% Performance Plus FBS and Pen/Strep (RPMI++)  and frequently mycoplasma tested. A GFP-P2A-HA encoding sequence was tagged to the start codon (ATG) of MEIS1 gene as previously published [MEIS1 . Generation and selection of cells are further described in the Supplementary Methods and Material section.U937 cells were obtained from the American Tissue Culture Collection (ATCC). U937 cell cultures were maintained at 37 °C in 5% COCells were analyzed or sorted using a BD Fortessa cell analyzer or Aria Fusion  respectively. Data acquisition was performed in the presence of 1 μM DAPI for the gating of viable cells. Data analysis was performed using the FlowJo software .Total RNA was isolated using Trizol  following the manufacturer’s instructions. cDNA synthesis and Real-time PCR were performed as described previously . Primer Cells were lysed in RIPA buffer subjected to 5-12% SDS-PAGE, and transferred to a nitrocellulose membrane . Proteins extracted from ~0.2 million cells were loaded in each lane. The blot was incubated with a primary anti-HA rabbit monoclonal antibody  or an anti-ERG/FLI1 rabbit monoclonal antibody . An anti-ACTIN monoclonal antibody  was used as control. Blots were visualized by ECL .https://media.addgene.org/data/plasmids/52/52961/52961-attachment_B3xTwla0bkYD.pdf).The CRISPR-Cas9 lentiviral vector used in this study was generated by substituting the Puro sequence with Cherry from CRISPR-Cas9 vector designed in Feng Zhang’s group . Cloning was done accordingly to their recommended protocol . Sequencing was performed using Illumina MiSeq. Reads were aligned to their respective reference amplicons and editing efficiency was quantified by CRISPResso v.1.0.12 [A 500 bp region surrounding the predicted Cas9 cutting sites was amplified from the sorted Cherryv.1.0.12 . High quMEIS1 promoter and candidate enhancer regions, the cut site for the restriction enzyme, BsrG I, located in the GFP gene of the tagged allele, was used to generate the fragments for subsequent ligation. Primers used are listed in the Supplementary Methods and Material section.3C assay was performed as previously described , 18. To DNA pull-down was performed using the protocol by Andrews et al.  with somChIP assays on U937 cells were performed as previously described with modifications  detailedWelch’s two-tailed t-test was used to calculate p-values. Statistical analysis was performed using the GraphPad Prism 8.0 .Primary AML patient RNA sequencing data was obtained from the Beat AML trial (NCT03013998) . Both boGFP and HA-tagged MEIS1 using the endogenous MEIS1 transcriptional regulatory machinery. The self-cleaving action of the P2A sequence post co-translation, leads to the simultaneous generation of GFP and HA-tagged MEIS1 proteins. The insertion of the tag into the correct genomic locus at both alleles (bi) or one allele (mono) in independent single clones was confirmed by PCR analysis  tag at its translation start site as previously described  in U937 sis Fig. . Flow cysis Fig.  and westsis Fig.  showed hMEIS1 enhancer regions, we introduced random mutations (Indels) at the E1, E2 and E3 regions, as well as a few along the gene body based on our previously published H3 acetylation and DNaseI hypersensitivity peaks in U937 [MEIS1 locus were selected for guide RNA (gRNA) mediated genome editing  and 4 sites  within the CpG island of intron 6 which correspond to the previously identified E2 region  and MEIS1-GFP-tagged Cherry+/GFP+ sorted cells. We detected a significantly (p = 0.0022) decreased interaction between the promoter and the intron 6 region surrounding the E2 region in E2.2 targeted cells compared to the parental cells in four tested sub-regions [MEIS1 expression targeted by E2.1 and E2.2, we PCR amplified and sequenced the E2 region in GFP+ and GFP− cells  compared to the E2.1 gRNA targeted site where indels span a comparatively larger genomic range for either the GFP− or GFP+ populations  and E2.2 CRISPR’d (MEIS1-GFP-tagged Cherry+GFP−) cells   binding of FLI1 to the E2.2 region as well as to the promoter  at the E2.2 region of enhancer E2 compared to control regions  and NUP98-HOXA10 homeodomain/Meis1 (NA10HD/Meis1). All three Meis1 leukemia models show a strong Meis1 peak at the same position of the Fli1 locus  and CN-AML (p = 0.044) . We observed a similar correlation for another ETS factor, ELF1, which we had previously shown to bind and upregulate MEIS1 expression [p = 0.017), CN-AML (p = 0.038), NPM1-wildtype CN-AML (p = 0.011) and NPM1-mutated/FLT3-wildtype CN-AML (p = 0.046) , CN-AML (p = 0.0036), NPM1-wildtype CN-AML (p = 0.00046) NPM1-mutated CN-AML (p = 0.072) and NPM1-mutated/FLT3-wildtype CN-AML (p = 0.017)  Fig. . Additiopression . High EL46) Fig. . These cMEIS1 displays high DNase I hypersensitivity and histone acetylation levels in U937 cells [MEIS1 gene in the U937 AML cell line.HOX proteins and their cofactor MEIS1 are often deregulated in AML especially in normal karyotype  and MLL 37 cells . Here weMEIS1 locus by CRISPR-Cas9 mediated genome editing. Using this pan-locus genome editing approach we identified regions whose loss resulted in the dramatic reduction of MEIS1 expression. Unlike the traditional plasmid-based enhancer assay, such editing occurred at the GFP-tagged endogenous MEIS1 locus in an AML cell line where the epigenetic landscape was preserved allowing a physiologically relevant readout of the regulatory region modifications. Chromatin capture and insertion/deletion analyses allowed us to pinpoint the region with the greatest impact on MEIS1 transcription. Our data provides important new functional evidence of the regulatory role of the previously identified candidate enhancer regions for MEIS1 transcription, and demonstrates the power of CRISPR-Cas9 mediated editing to deduce gene regulation mechanisms via transcriptionally coupled reporter genes. A combination of sequence prediction and oligo pull-down followed by mass-spectrometry detected TF candidates associated with the region of interest. Currently the CRISPR-Cas9 genome editing methods have been used in screening regulatory elements [MEIS1 in U937 cells. CRISPR-Cas9 mediated mutation of the FLI1 locus, led to reduced FLI1 levels and a drastic reduction in MEIS1 expression. This finding is in concordance with the observation of high FLI1 protein levels in NPM1 mutated and FLT3-ITD normal karyotype AMLs where MEIS1 levels are also elevated [MEIS1 deletion in hPSCs downregulated FLI1 and arrested megakaryocytic differentiation whereas the overexpression of FLI1 reversed the impaired megakaryopoiesis caused by MEIS1 deletion [Fli1 locus in mouse, point to a potential positive feedback loop between MEIS1 and FLI1 in normal and aberrant human hematopoiesis.Through the establishment of an endogenous locus reporter system for quick readout of the expression of TFs via flow cytometric analysis, we validate the function of the putative enhancers of the elements , 34. Howelements . Our appelevated . Intereselevated . MEIS1 ddeletion . These fMEIS1 transcription in these cells. A recent study has shown that mouse Fli1 promotes chromatin looping between the Meis1 enhancers and promotor in murine erythroleukemia cells [FLI1 site reduces promoter-enhancer interaction and chromatin accessibility pointing to the importance of ETS factor binding at the enhancer in addition to the promoter region. Additionally, we have previously shown that another conserved ETS family member, ELF1, positively regulates MEIS1 transcription in K562 cells via promoter binding [FLI1 and ELF1 transcript levels in all non-acute promyelocytic leukemia patients, especially in CN-AML where MEIS1 levels are high. A recent report also shows a positive correlation between MEIS1 and ELF1 levels in glioma patients and the upregulation of MEIS1 through ELF1 binding to its promoter sequence [MEIS1. From a therapeutic perspective, it has been shown that mithramycin (plicamycin) inhibits the binding of ERG and FLI1 to DNA in the context of RUNX2, providing a potential new therapeutic avenue for ETS TF driven leukemias [FLI1, ERG and ELF1 belong to the ETS family of TFs which are frequently involved in tumorigenic translocations , 38. Botia cells . Consist binding . Based osequence . These seukemias . Mithrameukemias  and is cMEIS1 transcription via CRISPR-Cas9 genetic manipulation. Such a method can be expanded toward the detailed study of other enhancer candidate regions in a pan-genomic approach through gRNA libraries. Our study revealed FLI1 as the candidate key regulator of MEIS1 expression and a positive correlation between FLI1 mRNA levels and worse overall survival in MEIS1-high AML subgroups.In summary, we have developed a rapid flow cytometry-based readout for the fine dissection and characterization of the cis-regulatory elements and associated TFs critical for Supplementary MM and FiguresSupplementary Table S1Supplementary Table S2Supplementary Table S3


BHLHE40 gene is upregulated in colorectal tumors. Transcription of BHLHE40 was jointly stimulated by the DNA-binding ETV1 protein and two associated histone demethylases, JMJD1A/KDM3A and JMJD2A/KDM4A, which were shown to also form complexes on their own and whose enzymatic activity was required for BHLHE40 upregulation. Chromatin immunoprecipitation assays revealed that ETV1, JMJD1A and JMJD2A interacted with several regions within the BHLHE40 gene promoter, suggesting that these three factors directly control BHLHE40 transcription. BHLHE40 downregulation suppressed both growth and clonogenic activity of human HCT116 colorectal cancer cells, strongly hinting at a pro-tumorigenic role of BHLHE40. Through RNA sequencing, the transcription factor KLF7 and the metalloproteinase ADAM19 were identified as putative BHLHE40 downstream effectors. Bioinformatic analyses showed that both KLF7 and ADAM19 are upregulated in colorectal tumors as well as associated with worse survival and their downregulation impaired HCT116 clonogenic activity. In addition, ADAM19, but not KLF7, downregulation reduced HCT116 cell growth. Overall, these data have revealed a ETV1/JMJD1A/JMJD2A→BHLHE40 axis that may stimulate colorectal tumorigenesis through upregulation of genes such as KLF7 and ADAM19, suggesting that targeting this axis represents a potential novel therapeutic avenue.BHLHE40 is a transcription factor, whose role in colorectal cancer has remained elusive. We demonstrate that the  The DNA-binding protein ETS variant 1  is a downstream effector of signaling proteins such as RAS and HER2/EGFR2 that induce posttranslational modifications on ETV1 and thereby control its transcriptional activity . This seTranscriptomic studies on human HCT116 colorectal cancer cells pointed at potential downstream effectors of ETV1, including the basic helix-loop-helix family member E40  –19, affeBhlhe40 knockout mice and demonstrated that colon carcinogenesis induced by azoxymethane/dextran sulfate was compromised upon BHLHE40 ablation  3A) and JMJD2A , which are histone demethylases  and then examined in real-time with the AccuPower 2xGreenstar qPCR kit  in a BioRad light cycler according to the manufacturer’s recommendations. Sequences of RT-PCR primers are provided in RNA was isolated with Trizol  and furtBHLHE40 promoter fragments were amplified with nested PCR  were cultured in poly-m plates . Upon rem plates . Two daym plates  and immum plates . Precipim plates , transfem plates , and them plates . After im plates , signalsm plates  and expom plates . LikewisTransduced HCT116 cells were seeded into 96-well plates at a density of 2800 cells/well and their growth measured as described . For cloBHLHE40 mRNA  mediate BHLHE40’s tumorigenic functions. For instance, the CREB5 transcription factor gene was downregulated in the BHLHE40 shRNA-treated HCT116 cells, suggesting that BHLHE40 stimulates CREB5 expression. And CREB5 is overexpressed in colorectal tumors and capable of enhancing proliferation, invasion and metastasis of colorectal cancer cells  can be found below: YS, HJ, CK and RJ conceived, designed and performed experiments. YS, HJ, CK, SO and RJ analyzed and interpreted data as well as contributed to the writing of this manuscript. RJ supervised this study. All authors contributed to the article and approved the submitted version.


Planarians have become an established model system to study regeneration and stem cells, but the regulatory elements in the genome remain almost entirely undescribed. Here, by integrating epigenetic and expression data we use multiple sources of evidence to predict enhancer elements active in the adult stem cell populations that drive regeneration. We have used ChIP-seq data to identify genomic regions with histone modifications consistent with enhancer activity, and ATAC-seq data to identify accessible chromatin. Overlapping these signals allowed for the identification of a set of high-confidence candidate enhancers predicted to be active in planarian adult stem cells. These enhancers are enriched for predicted transcription factor (TF) binding sites for TFs and TF families expressed in planarian adult stem cells. Footprinting analyses provided further evidence that these potential TF binding sites are likely to be occupied in adult stem cells. We integrated these analyses to build testable hypotheses for the regulatory function of TFs in stem cells, both with respect to how pluripotency might be regulated, and to how lineage differentiation programs are controlled. We found that our predicted GRNs were independently supported by existing TF RNAi/RNA-seq datasets, providing further evidence that our work predicts active enhancers that regulate adult stem cells and regenerative mechanisms. The molecular and evolutionary mechanisms of regeneration remain underexplained compared to animal development. This can be attributed to the greater technical difficulty historically associated with studying the molecular mechanisms of adult biology compared with development. However, an ever-broadening repertoire of model organisms for regeneration, progress in understanding the variety of cellular and molecular mechanisms used across taxa, and advances in experimental tools are serving to close this gap. One area of slower progress has been our knowledge of the regulatory logic of regenerative mechanisms, with relatively few studies in highly regenerative models providing precise insight into the epigenetic regulation of regeneration. While transcription factors (TFs) have been assigned functions, and in some cases a list of likely targets through transcriptome analysis, regulatory logic has been studied in only a few cases . This isNothobranchius furzeri, and the fruit fly Drosophila melanogaster, and at the whole-body level in the acoel worm Hofstenia miamia and the cnidarian Hydra vulgaris  is known to mark active enhancers together with mono-methylation of lysine 4 on histone H3 (H3K4me1), whereas H3K4me1 alone marks predetermined or poised enhancers , while the new coding and non-coding transcripts were much shorter  . HoweverS. mediterranea (70 %), but 30% had a homolog in the Dugesia japonica genome . The naming of planarian TFs in the literature was mostly consistent, but some inconsistencies were found , many of which have not received much consideration yet in planarian regeneration research. While some of these unstudied ZNFs could be assigned to well-known ZNF families such as GATA, KLF, EGR, and PRDM, many could not be assigned to well-described families. The nomenclature of ZNFs was based on the naming of human proteins to which they have the highest identity, and hence many appear in the ZNF and ZNP categories . InteresNkx and Six, and a few TFs belonging to families not previously described in planarians were also discovered, such as Lmx and Shox .We proceeded to allocate proportional expression values to the TFs with respect to the X1, X2, and Xins cell compartments . The diszfp-7, pbx, prep, gli-3, and rfx7, while the least informative motifs were for hesl-2, pou2/3, irx, tead1, and lmx1  (zic-1) (zeb-1 and snail-1/2)    , and TFs (zic-1)  and cellail-1/2) . An enha foxA(P) . A distaed ets-1 . A dista foxA(P) , neural A(P)  smad1/9 , isl-1, and prep  and multiple bound motifs at enhancer-like regions linked to tbx genes    (We identified the same five planarian d tbx-20 . In mammem cells . Nonethe motifs)  and. tbx-1, sp5) , TFs inv-1, sp5) , and var-1, sp5) .Next, we studied enhancer-like regions and ppotential regulatory links associated with the well-known planarian PCGs. Planarians are a primary model system to understand how positional information guides and directs stem cell function during regeneration , and thesp5 and the Hox genes post-2c, post-2d, lox5a, and hox4b are rapidly downregulated  animals. Upon wnt1 knockdown, posterior Hox genes hox4b, post-2c, post-2b, lox5a, lox5b and wnt11-1, wnt11-2, fzd4, and sp5 were downregulated, replicating the results of β-catenin knockdown  and a few motifs were found to bind to the enhancer-like regions linked to prep, zic-1, isl-1, and foxD1  and nkx2-2/3/4  causes blastemal defects both at the anterior and posterior ends, while knockdown of prep leads to defects in the anterior, suggesting that nkx2-2 might work upstream of prep at the anterior end (anarians . Interesnd foxD1 . Prep hax2-2/3/4 . Knockdorior end .patched-1 (ptch-1) and found an intronic enhancer-like region containing multiple bound motifs, including the Hedgehog pathway TFs gli-1/2 as would be expected, for the Wnt effector sp5, the Hox genes lox5a/b, BMP signaling components , the anterior pole TFs prep and zic-1, the cell migration factors snail-1/2, and the muscle TFs myoD and twist  (foxD1(RNAi)  , foxD1(RD1(RNAi) , pax2/5/x2/5/8-1 , myoD of whole worms, we observed eight DETFs, and by collating these two datasets, we obtained pou4-1 as the only DETF containing a putative bound coe footprint . This puS. mediterranea by integrating available RNA-seq data and identified new coding and non-coding transcripts. We reviewed the literature on both computationally and experimentally derived planarian TFs and defined a high-confidence set of TFs. If possible, we also predicted binding motifs for the planarian TFs. We developed and performed ATAC-seq on the proliferating stem cell compartment and determined genomic regions of open chromatin. We analyzed genome-wide profiles of the histone modifications H3K27ac and H3K4me1, along with chromatin accessibility data, and used these epigenetic datasets to delineate putative active enhancers in planarian stem cells. Lastly, we identified TFs binding to the enhancers of potential target genes and constructed hypothetical GRNs active in the stem cells.In this study, we improved the current genome annotation of We found more than 50,000 new isoforms to known transcripts in our annotation. This information has already helped to improve the clustering of single-cell RNA-seq data . Our appAltogether, we annotated 551 TFs that were distributed evenly with respect to enrichment across all three FACS cell compartments in planarians . In planarians, most studied functionally have been involved in regulating differentiation. Here, we define many neoblast-enriched TFs that have not been formally studied, with potential roles in maintaining neoblast pluripotency and potentially early lineage commitment. A future systematic functional screen of these may help to uncover the GRN network that maintains pluripotency analogous to that in mammals and vertebrates . In partOverall, for the main goals of this study, our analysis and identification of TFs allowed us to confidently assign likely binding motifs to just under half of the annotated planarian TFs . In the Based on combining epigenomic experiments and data types, we could identify putative intergenic and intronic enhancers in the planarian the genome of proliferating stem cells. The combined use of ChIP-seq data, RNAi of a histone methyltransferase combined with ChIP-seq, ATAC-seq data, and footprinting analyses together provided strong evidence for the identification of bona fide planarians stem cell enhancers. In the future, genome-wide ChIP-seq against the transcriptional cofactor p300 could serve as a complementary high-throughput approach to further substantiate these enhancers , if avaiVerification of the function and targets of planarian enhancers is currently not possible using traditional approaches as no transgenic reporter technologies enabling enhancer-reporter constructs to be assayed are available. Therefore, we are bound to rely on less direct evidence from genome-wide sequencing technologies. Here, we assigned enhancers to target genes based on distance and the expression of the target gene, similar to established protocols used by others . We findTaken together, our definition of enhancers in stem cells genome wide creates a foundation for constructing detailed GRNs to help understand regenerative mechanisms driven by stem cells in planarians.The sexual genome SMESG.1 genome, the SMESG high-confidence annotation (SMESG-high release 1), and the SMESG annotation filtered for repeats (SMESG-repeat release 2) were downloaded from PlanMine . Availabhttps://github.com/jakke-neiro/Oxplatys/raw/gh-pages/Schmidtea_mediterranea_Oxford_v1.gtf.zip) was compared to the SMESG-high annotation with gffcompare . Hierarchical clustering of FACS proportions was performed using Euclidean distance and Ward’s method with the package hclust . The freThe motifs of the TFs  were pre2, 0.1% NP-40) was added, the cells were centrifuged , and the nuclei pellet was collected. Then, the transposase mix  was added, and the cells were resuspended and incubated . Finally, DNA was isolated using the Zymogen Clean & Concentrator Kit, and eluted in EB buffer.A standard protocol was used for preparing the ATAC-seq library . The X1 Subsequently, the eluted DNA was used for PCR amplification and library purification. DNA was amplified using a standard reaction . Finally, the libraries were cleaned with the AMPure bead purification kit. Samples were paired-end sequenced on the Illumina NextSeq. The sample replicates can be found on Sequence Read Archive (SRR18923214 and SRR18923213).The H3K27ac ChiP-seq library was prepared and sequenced using established protocols . A totallpt(RNAi) was reanalyzed . We certainly agree that each individual predicted enhancer and regulatory interaction of interest needs to be studied for its exact role in stem cells and/or during regeneration and will make sure the revision of the text reflects this.We have added extensive new analyses to address two of the reviewers’ comments that further functional evidence was required through new experiments using RNAi of TFs coupled to RNA-seq, to find independent evidence of for links between TFs and predicted targets. As this research project was affected by the pandemic, we were unable to currently afford this significant extension to the work in terms of lab time or resources, rather we see this as the work of many labs in the coming years. Nonetheless, we acted on the reviewers’ suggestions by using existing TF RNAi/RNA-seq data sets for TFs that we predict to be active in the stem cell regulatory networks predicted in our initial submission . Despite these experiments being performed in mixed tissues  we still observe clear evidence for differential expression of TFs predicted by our enhancer and footprint predictions to be downstream of the TF targeted by RNAi. This improves the confidence of our original findings significantly and we are grateful for the reviewers’ for suggesting this approach. Overall, we strongly agree with the reviewers’ comments that our work will be a valuable resource for those studying regeneration and animal stem cells.Reviewer #1 ):In this manuscript Neiro et al. aim to expand our knowledge on the regulation of gene expression in stem cells of the planarian model organism.As a first step the authors used published available data to expand the repertoire of the planaria transcriptome. By combining 183 RNAseq datasets the authors were able to identify thousands of new coding and non-coding transcripts. They then screened for TF motifs in the new annotations, identifying 551 putative TFs, of which 248 were already described in the planarian literature.The most substantial contribution of this work to the field of stem cells and planaria biology is the characterization of new putative enhancers that were identified by performing H3K27ac ChIP-seq and ATAC-seq and combining these data with previously published H3K4me1 ChIPseq dataset.We thank the reviewer for their careful assessment of our work, we agree that the identification of likely enhancers genome wide is a substantial contribution. Equally the improved annotation of all genes, including the collected set of transcription factors we choose to focus on here, is a substantial step forward for the planarian research community.By overlapping H3K27ac and H3K4me1the authors find 5,529 new enhancers, for which they report a higher chromatin accessibility than random points in the genome as assessed by ATAC-seq. By using ATAC-footprints Neiro et al. refined the subset of TFs that have binding motifs in the predicted enhancer-like regions and present a list of 22,489 such factors.The manuscript is well written and organized and overall, the reported data will provide an important resource to study gene expression regulation in planaria's stem cells. However, this manuscript would greatly benefit from some functional validation to support the predicted gene regulatory networks. One option would be to use a CRISPR-dCas9-KRAB system to silence the putative enhancers identified in the manuscript and check by qPCR the expression of nearby genes.Currently mis-expression technologies, in order too directly test enhancer elements in driving expression, are still not available in planarians. This also preempts us using the suggested silencing system used in mammals and other animals with robust mis-expression tools.If this type of experiment is not feasible in planaria (I am not an expert in this model organism) another simple but key experiment would be to perform a knockdown of one (or more) putative enhancer-bound TFs identified in this study followed by RNA-seq. This would allow the authors to verify what are the target genes of the putative enhancer-bound TFs and if they correspond to the predicted gene networks they identified. Simultaneously, this experiment would allow the authors to verify if there are any changes in the expression of differentiation/pluripotency markers as a result of the knockdown of the putative enhancer-bound TF.These experiments are possible, but this would be the work of many labs in the future expert in studying those TFs, the organs and tissues they are expressed in, and their roles in planarian stem cells and regeneration. However, what we have done is analyze existing RNA-seq data collected after specific TF RNAi . There are several studies where TF have been studied and RNA-seq performed after RNAi. Although these studies are performed in specific experimental regenerative contexts, and not specifically in stem cells, it was nonetheless possible to look at expression changes of potentially downstream TFs with predicted enhancers for those TFs knockdown by RNAi. We analyzed these data and added it to the manuscript, rather than perform further TF RNAi experiments, as this was just about feasible within a 3-month revision time. We would add that currently there are no genes implicated in controlling pluripotency in the same way we might consider, for example, OSKM in mammals. Our identification of the TFs enriched in stem cell expression and implicated in binding predicted enhancers suggests future candidates.Our additional analyses  clearly show that TF predicted to be regulated by our enhancer predictions are significantly enriched amongst differentially expressed TFs after RNAi of the predicted regulating TF. We observed differences between the levels of unbound enhancers (10x enrichment) and bound enhancers with footprint evidence (21x enrichment) across all the TF RNAi/RNA-seq studies, and a correlation between the predictive power of our GRNs and the proportion of expression of the knockdown TF in the stem cell compartment. This suggests that our predicted GRNs are reflective of activity in stem cells, but not of other cells.Minor revision:The authors have mostly focused on the identification of enhancer-bound TFs. However, it would be interesting to look at differential enrichment of TFs in promoters versus enhancers and identify if there are specific factors that are enriched specifically at the planarian newly identified enhancer regions.We have not looked at potential TF binding sites near promoters/transcriptional start sites. We have added an analysis to look at this . This analysis does not reveal any TFs or TF families that show a pattern of having binding sites that are more proximal to promoters than others. We did not pursue this further as it was beyond the scope of our study.All tornado plots are missing a color bar We have fixed this error.There is a typo in the discussion: "the combined use of ChIP-seq data, RNAi of a histone methyltransferase combines with ChIP-seq" should be changed to "combined".We have fixed this and other typographical errors.Reviewer #1 (Significance (Required)):The manuscript is well written and organized and overall the reported data will provide an important resource to study gene expression regulation in planaria's stem cells.We thank the reviewer for their appreciation of our workReferees cross-commentingI agree with the other reviewers that additional functional data should be added to support the author's claims .See response above, with regard to adding further analyses , see Figure 8 and new Results section.In addition, as noticed by the third reviewer, all data should be made publicly available to the scientific community.We have made all data publicly available and have submitted all relevant data to public database repositories in advance of final publication.Reviewer #2 ):Summary:Schmidtea mediterranea. The authors start with the integration of transcriptome with genome sequencing data to more precisely annotate the genome of the planarian Schmidtea mediterranea. The second part of the manuscript actually then deals with the identification of potentially active enhancer elements in adult stem cells of this regenerating organism using genomic techniques like ATAC-seq and ChIP-seq of histone marks combined with motif searches and in silico footprint analysis. Using these data, the authors predict regulatory interactions potentially critical for pluripotency and regeneration in planarian adult stem cells.This manuscript aims at identifying enhancers in the planarian Major comments:- Are the key conclusions convincing?1) The authors claim  that their study identifies enhancers regulating adult stem cells and regenerative mechanisms. This is an over-statement found throughout the manuscript, as none of these enhancers are functionally tested nor is it shown that target gene expression changes when transcription factors predicted to interact with such enhancers are knocked down.We agree and it was not our intention to overstate our results, this is why we have tried to refer to putative enhancers, enhancer-like elements etc in manuscript from the title onwards. Only once we have demonstrated a set of elements with key conserved and widely supported characteristics do we suggest we have a set of higher confidence enhancers to study. However, we have adjusted the manuscript to reflect that our claims await direct testing as is the case for all enhancers implicated with the approaches used here.Another example is at the end of paragraph 1 of section 2.4. Here the authors claim that identifying many fate-specific transcription factor genes in the vicinity of potential enhancers is a further proof that the identified regions represent "real enhancers". It strongly supports this hypothesis, but no evidence for real enhancer activity.We agree the total body of evidence strongly supports that we have identified enhancer elements, but have adjusted the language to suggest further directed functional work will follow from many groups.Thus, although the authors state that the regulatory interactions and networks they predict from their data can be studied now in future, they should be more careful with their wording and correct these over-statements. Therefore, the key conclusion is that they identified by various techniques potential enhancers, which are close to genes controlling adult stem cells and potentially controlling these genes, which has to be shown by further analyses.We agree.Thus, also the title needs to be changed.We have changed ‘enhancer-like’ to “putative enhancer-like” and “defines” to “predicts” in the title as well as broadly adjusting the text to caveat that further work will clarify their functions and roles.The authors have no proof that the networks are active in planarian adult stem cells, as they do not show that the predicted networks are active in the presented way.We agree, see comments above. It was not our attention to claim we are showing pathways that were definitively active, but rather predicted by our experiments and analyses of the data from these experiments.2) Similarly, the identification of TF motifs within these potential motifs strongly suggests but not shows that these factors are binding, even when these sites were found to be bound by a protein using the ATAC-seq footprinting analysis. Thus, the authors need to be careful with their wording. One example is in the second paragraph of section 2.5, where the authors write that "We found that numerous FSTFs were binding to putative intronic enhancers.… ". The motif suggests that these factors bind, however, they have no experimental confirmation that these sequences are indeed bound by the planarian TFs.We agree and have been careful to emphasize “predicted” and “putative”Schmidtea mediterranea. The manuscript is informative yet descriptive, as tit presents no functional evidence for any of the predictions. If further toned down, the key conclusions are valid.In sum, this manuscript uses existing genomic tools to define potential enhancer regions in the planarian Future functional experiments to test the roles of all TFs and enhancers is now possible due to our work. The combination of data and analyses provides strong support of enhancer elements activity in stem cells across the genome. We have added analyses of previous RNAi work on TFs which provide strong cross validation for our predicted TF GRNs  and RNAi of the H3 K4 mono-methyl transferase provides genome wide functional validation of the predicted enhancer regions.– Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The experiments performed are well designed and in line with what is known in the field about enhancer architecture. However, as this model system is not very well characterized on that level and the authors do not provide real experimental evidence that any of the identified regions has really enhancer activity and that any of the identified motifs binds indeed the predicted TF, the authors need to be very careful with their statements. The authors should maybe emphasize even stronger that all the GRNs predicted under section 2.6 are really preliminary and need to be validated.Yes, we are happy to have been even clearer about this as the reviewer suggests– Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.One experiment that could provide more evidence for their predicted regulatory interactions is to knock-down one of the FSTFs for which motifs have been identified in potential enhancer regions and to study expression of associated genes (to confirm that the enhancers potentilla bound by these TFs control the expression of associated genes) or by analyzing the chromatin status of selected chromatin regions (by Q-PCR). These experiments would strongly support the claims of the authors. However, it also depends strongly on the journal whether I would consider these experiments essential or "nice to have".This suggestion of possible extra experiments is very similar to that of Reviewer 1. We are copying our earlier comment as this also addresses this point.These experiments are possible, but this would be the work of many labs in the future expert in studying those TFs and their roles in planarian stem cells and regeneration. However, what we have done is analyze existing RNA-seq data after specific TF RNAi, where this data is available. There are several studies where TF have been studied and RNA-seq performed after RNAi. Although these studies are performed in specific experimental regenerative contexts, and not specifically in stem cells, it was nonetheless possible to look at expression changes of potentially downstream TFs with predicted enhancers for those TFs knockdown by RNAi. We analyzed these data and added it to the manuscript, rather than perform further TF RNAi experiments, as this was just about feasible within a 3-month revision time. We would add that currently there are no genes implicated in controlling pluripotency in the same way we might consider, for example, OSKM in mammals. Our identification of the TFs enriched in stem cell expression and implicated in binding predicted enhancers suggests future candidates.Our additional analyses  clearly show that TF predicted to be regulated by our enhancer predictions are significantly enriched amongst differentially expressed TFs after RNAi of the predicted regulating TF. We observed differences between the levels of unbound enhancers (10x enrichment) and bound enhancers with footprint evidence (21x enrichment) across all the TF RNAi/RNA-seq studies, and a correlation between the predictive power of our GRNs and the proportion of expression of the knockdown TF in the stem cell compartment. This suggests that our predicted GRNs are reflective of activity in stem cells, but not of other cells.– Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.Schmidtea mediterranea, thus it is hard to judge how time consuming these experiments would be. Cost-wise they should be feasible, as it would include primarily Q-PCR experiments. And some functional back-up of their claims would be very helpful.This reviewer is not an expert in See previous comment regarding additional analyses. We have managed these just about within the 3 month revision window.– Are the data and the methods presented in such a way that they can be reproduced?For the parts I can judge, yes.– Are the experiments adequately replicated and statistical analysis adequate?It is not clear from the manuscript how many replicates of the ChIP-seq experiments were done.ChIP-seq replicate data (duplicates) has been explicitly added to the methodsMinor comments:– Specific experimental issues that are easily addressable.– Are prior studies referenced appropriately?For the literature I can judge, yes.– Are the text and figures clear and accurate? The figures are clear, the text (besides over-statements) is clear. However, the writing can be improved. A few examples: section 2.2 paragraph 1: "… we found 248 to be described in the planarian literature in some way." In which way described?; same paragraph: "… but significantly we could identify new homologs of.…" what does significantly mean? Which test etc? section 2.2, last paragraph: "Most TFs assigned to the X1 and Xins compartments and the least to the Χ2 compartment", "Very few TFs had expression in X1s and Xins to the exclusion of Χ2 expression as would be expected by overall lineage relationships"; what do these sentences mean?We thank the reviewer for paying careful attention to the language in our manuscript throughout. We will provide clearer explanation of the sentences indicated. We have better explained terms specific to the planarian model system that are obviously not intuitive and be clear in the figure legends, methods and included code notebook about statistical tests.– Do you have suggestions that would help the authors improve the presentation of their data and conclusions?No over-statements.See previous comments agreeing with the careful adjustment of our language to avoid this.Reviewer #2 (Significance (Required)):– Describe the nature and significance of the advance  for the field. This manuscript identifies genome-wide potential enhancers in adult planarian stem cells, and thus represents a very valuable resource for the community to study these enhancers and the gene regulatory networks they control in the future.– Place the work in the context of the existing literature .As I am not a planarian scientist, it is hard to judge this part.– State what audience might be interested in and influenced by the reported findings.In my opinion, this work will be primarily interesting for people working with planarian. When functional data exist, this might be also interesting for researchers working generally on regeneration.Given the nature of our data we also think all groups working on animal stem cells would be interested in our data and analyses.– Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.Schmidtea mediterranea.My field of expertise is transcriptional regulation using genomic techniques, however I am not familiar with the model Reviewer #3 ):Schmidtea mediterranea and new ChIP-seq and ATAC-seq data to use computational approaches to identify putative enhancers in the planarian genome. They integrate analysis of enhancers with transcription factor binding sites to generate testable hypotheses for the regulatory function of transcription factors active in stem cells or control of cell lineage trajectories. Their work creates an excellent resource for future work to resolve the regulatory logic underpinning stem cell biology and tissue regeneration in planarians.Neiro et al. capitalize on existing genomic data for the planarian We are glad the reviewer likes our research.Major: Overall, the work in this manuscript and methodology are well executed and presented. However, the authors should consider the following comments to improve the clarity and accessibility of the data and interpretations.1) The new transcriptome does not appear to be publically accessible. The links to Github resources are broken, and there is nothing on Neiro's Github page. Will the new transcriptome be integrated with Planmine?annotation has been available for over a year as we wished the community to have access to it ASAP . We tested the links in the paper before depositing our preprint and after review and they seemed to work for us both within and outside our institutional network. We can only apologize if they were broken or have not worked for the reviewer. We are unclear if this new annotation will be included in Planmine, but we have asked the colleagues maintaining this database to consider including it. Our recent analyses of Planmine suggest that our annotation is now all included as all the new genes have appeared, even if our work is not explicitly referenced.The new 2) Figure 1: Ternary plot in 1F. The legend is not clear or could be explained better. What is the metric? It could be my misunderstanding, but I didn't consider the ternary plots as insightful or unnecessary. Perhaps the authors can expand on what they are showing.These plots are important in demonstrating the distribution of mRNA expression of all genes across cell sorted compartments. Given the broad lineage relationship between sorted cell compartments This analysis allows us to identify genes expressed predominantly in one cell compartment or another, or across a specific transition. For example, genes enriched in Χ2 cells and Xins, but not X1 are likely to be enriched in post-mitotic differentiating progeny and differentiated cells. In contrast to single cell data where expression data can be sparse this analysis with bulk data allows identification and assignation of lowly expressed genes, like transcription factors. We will provide some further explanation of this in the revised text.1I is a map of exons, not alternative splicing. So, it isn't clear what the authors intend t show. Are the specific exons that are more likely to be spliced? Is the figure necessary?We wished to demonstrate the power of the annotation approach and the richness of the annotation for looking at alternate splicing. We have provided a more informative figure that indicates the variety of splice forms. We apologize for this oversight.3) Figure 2: 2A labels Xins as irradiation responsive. Is this the case (just making sure)?The reviewer is correct, this is wrong! This should read “insensitive” or “resistant” In Figure 1A. We thank the reviewer for spotting this error. We have fixed this.2F-G: Ternary plot in F seems redundant with G, but that could be my lack of understanding. In 2G, what is represented on the plots on the right of the hierarchical clusters?The ternary plot (2F) and heatmap of hierarchical clustering (2G) are complementary ways to visualize the proportional expression values of transcription factors. The ternary plot (2F) allows an overview of all the proportional expression values, while the heatmap (2G) shows how the proportional values may be grouped into clusters of similar expression profiles and displays the relative size of these clusters. For example, the heatmap shows that the clusters of X1 and Xins are more prominent than Χ2, suggesting that there are relatively a few Χ2-specific transcription factors. We have edited the text to better to explain this difference.4) Figure 3: The heat maps need a legend . In addition, labeling the figures could help the reader. For example, in G-J, a header about the different experiments above each map, such as "enhancers" and "random," etc., would make the figure more accessible.We agree the figures to be more easily interpretable and provided an independent scales and labels for the heatmaps.5) Figure 5: Although it is in the figure legend, the authors could label the 6th track as "RNA-seq in X1."We have added this to the figure.6) Section 2.6 second page last sentence of the first paragraph "GRN of asexual reproduction is not active in neoblasts" data in the supplement? Is it not shown?We apologize for this poorly written sentence. In line with Reviewer 2s comments this statement needs to be toned down and clarified. The raw information is included in the general table of enhancers (Supplementary File 2), but the genomic tracks visually highlighting the motifs at the promoters of lox5b and post2b are presented now Figure S4 A and B.7) Discussion: The discussion about pluripotency factors in planarians could be expanded. The authors could contrast the study's findings with Önal et al. 2012.We agree have expanded our discussion. But we very little is known with regard to TF roles in controlling pluripotency in adult stem cells.Minor: The manuscript has no page numbers or line numbers, so I'll provide a general location of the potential issues.1) Section 2 – newly identified isoforms are shorter (1656 vs. 1618). Is the order of the median length reversed?Yes, we have corrected this.2) No mention of Figure S1B in the text.We have added a correct reference to this figure in the presentation of transcript diversity.3) Figure 1H should be 1I in the text?Yes, we have corrected this4) The discussion contains some minor typos and grammatical errors.We have addressed with careful rereading.We thank the reviewer for spotting these errors and we will fix them in revision.Reviewer #3 (Significance (Required)):Neiro et al. provide an excellent resource for the planarian community. The paper is generally very well written and easy to read. The new transcriptome described, which improves the annotation of the planarian genome, should be made readily available. It would be excellent if the transcriptome could be incorporated in Planmine.The annotation  has been available since the pre-print for Garcia Castro, 2021, Genome Biology was deposited in BioRxiv.Schmidtea mediterranea. Their work provides insight into which factors are highly expressed in the stem cell compartment. Their computational identification of transcription factors and putative enhancers will be helpful to the growing community of researchers studying stem cell and regenerative biology using planarians. In addition, the large dataset generated in this study could inform studies in the evolution of regulatory sequences and transcription factor function.Referees cross-commentingFurthermore, the authors provide a comprehensive list of transcription factors in the planarian The data presented are well supported by previous studies. As noted by the authors, it is not possible to make transgenic planarians, and thus the field needs to rely on indirect methods. The authors focus on using the stem cell population, which can be isolated from the animals. Overall, I don't think additional experiments are necessary. Additional RNAi experiments combined with RNA-seq (using the stem cells) could take 6-12 months to complete. I believe this is a solid contribution that should be framed as a resource paper. The authors should pay close attention to Reviewer #2's suggestions and edit the paper accordingly.I have 20 years of experience in the field. It would be unreasonable to ask the authors to do more experiments, especially in this post-pandemic environment. I hope this helps.We thank the reviewer for their comments.