Studies of the regulatory networks and signals controlling erythropoiesis have brought important insights in several research fields of biology and have been a rich source of discoveries with far-reaching implications beyond erythroid cells biology. The aim of this review is to highlight key recent discoveries and show how studies of erythroid cells bring forward novel concepts and refine current models related to genome and 3D chromatin organization, signaling and disease, with broad interest in life sciences. Erythropoiesis is a complex and fascinating biological system. It stands out as being one of the largest cellular outputs of the human body, with the daily production of billons of new red blood cells (RBCs). It is also a remarkable system from a cell-biology perspective: erythroid cell production involves the precise coupling of cell cycle with cellular differentiation, culminating in massive intracellular organelle remodeling, nuclear condensation and expulsion, a process referred to as enucleation ,2. DespiThese outstanding features of erythroid cells are to a large extent epigenetically programmed and controlled by the combinatorial action of numerous transcription factors (TFs), signaling pathways and metabolites, the interplay of which ensures the fine-tuning of the intricate differentiation steps leading to the production of mature RBCs. Erythropoiesis has been widely used as a model system to dissect the processes of cellular differentiation and to unravel gene regulatory principles. Several (epi)genetic concepts originate from findings originally obtained in erythroid cells, such as chromatin looping and dynamic enhancer–promoter communications, and the discovery of several key transcription factors, pathways and their intricate interplay. Erythroid cells still have wonders to deliver, and their study continues to be at the forefront of several research fields, providing new insights into basic science and human disorders. This review aims to summarize key recent discoveries, illustrating how the study of erythroid cells have generated tremendous functional insights into the fields of genomics, transcription factor dynamics and 3D chromatin organization, regulatory networks and signaling in healthy differentiation and disease.Erythropoiesis has long been used as a paradigm for cellular differentiation and disease. It has been an incredibly rich source of discoveries putting forward novel concepts over the past decades. An initial key observation stems from the analysis of globin genes, where it was found that mammalian gene transcriptional regulation is under the control of distal regulatory elements  that can be located tens of kilobases away from promoters. The discovery of the globin locus control region (LCR) is one such example, and this peculiar feature was subsequently generalized, years after, with the development of high throughput chromatin profiling assays , which unraveled the complexity of the mammalian regulatory landscape ,4,5.SHH [TAL1, RUNX1 [GATA2 [Genome-scale profiling studies have shown that the number of gene regulatory elements such as enhancers largely exceeds the number of protein-coding genes, implying that genes are under the control of multiple enhancers. This is indeed the case for a large number of tissue-specific and house-keeping genes. For some well-studied loci, such as the SHH , TAL1, R1, RUNX1 ,8,9 or G1 [GATA2 ,11,12 lo1 [GATA2 ,14. Whet1 [GATA2 ,16,17, a1 [GATA2 . This inA key question that arose at the time of the discovery of the globin LCR, was how distal regulatory elements could control target gene expression over such large distances. The first demonstrations that distal enhancers establish specific ,20 and fBecause most of the current chromatin-looping assays are performed on a population of cells, it only provides an average view of the chromatin contacts, preventing dissecting chromatin contact dynamics in individual cells and on individual alleles. In the original models put forward in the 1980s and 1990s, it was proposed that the different promoters would compete for the access to the enhancers (reviewed in ). In thiMpg and Rhbdf1 which are normally present in the neighboring domain and which do not interact with the α enhancers in wild-type cells  have revealed that distal enhancer–promoter interaction can form stable chromatin complexes that engage into the gene body during productive transcriptional elongation, following the edge of elongating RNA polymerase II [It should be noted that although long-range enhancer–promoter chromatin interactions are highly dynamic ,41,45,46erase II ,35, or ierase II ,49,50,51Different levels of chromatin loop or domain formation co-exist in mammalian cells. At the tens to hundreds kilobases scale, chromosomes are folded into so-called Topologically Associating Domains (TADs). The TADs partition our chromosomes and are believed to be involved in the insulation of gene expression and to prevent, to some extent, uncontrolled enhancer promoter communications A,B. In tBesides the clear roles played by CTCF and the cohesin complex which have been thoroughly investigated in the context of higher order 3D genome structures, functional enhancer-promoter communications within such domains were shown to require TF and other types of chromatin complexes. Within these permissive structures, functional enhancer-gene communications take place via protein-protein interactions stabilizing interactions between regulatory elements and their target genes. These so-called “looping factors” mostly belong to the TF category  ,58,59,60An important remaining open question concerns the functional role of chromatin loops. Are distal enhancer-promoter loops really important and how do they mediate their role? Are they fulfilling an instructive role (i.e. impacting on the transcription cycle) or are they only a consequence of transcriptional activation, i.e. being permissive structures that are formed following transcriptional activation? A key answer to these questions was provided by artificial tethering of LDB1 to the globin locus, showing that in the absence of GATA1, forced localization of the LDB1 dimerization domain at the β-globin gene promoter induces globin expression, a phenomenon depending on chromatin looping with the upstream LCR  Figure B. TherefMyb gene locus, where it was shown that LDB1 recruits the CDK9 kinase at the distal enhancers which would phosphorylate RNA polymerase II serine 2 (a mark of productive elongation) via looping [Myb gene first intron, precisely at the site of the transition between transcription initiation to elongation , and sucription .Shh locus in embryonic stem cells differentiating to neural progenitors [Increased proximity between regulatory elements and genes is a common feature underlying transcriptional activation as observed on numerous gene loci. However, it is noteworthy that the opposite was also observed: increased enhancer-promoter distance was recently demonstrated at the genitors , correlaIn aggregate, these observations suggest that multiple regulatory components control long-range chromatin interactions for the fine-tuning of transcription. As technologies allowing high-resolution chromatin contacts measurements, and enabling precise manipulation and tracking of candidate factors progress at an incredible pace ,78,79,80BCL11A and HBS1L-MYB loci. This led to the identification of the BCL11A TF as a potent direct repressor of γ-globin through binding the promoter and recruiting repressive components of the CHD4/NuRD chromatin-modifying complex [MYB expression and KLF1 haploinsufficiency in humans associate with hereditary persistence of fetal hemoglobin [BCL11A gene [BCL11A remain unclear. The β-globin gene locus has long been used as a model to dissect chromatin-looping dynamics and developmental gene regulation, and it continues to be a widely studied locus ,72,81,82 complex , also ac complex . Furthermoglobin ,85,86. Amoglobin , KLF1 semoglobin ), includ11A gene . Interes11A gene . AlthougCHD4 locus, being highly enriched and functionally occupied . Careful control experiments were performed to ascertain that this peculiar binding pattern was a true biological observation, confirming that CHD4 is the only direct target gene that is sensitive to ZNF410 dosage. Shortly thereafter another study reported similar findings, independently confirming this surprising observation [CHD4 levels decrease by ~70%. As the main direct γ-globin repressors BCL11A and LRF/ZBTB7A use the CHD4/NuRD complex as a mean for repressing γ-globin genes, decreased CHD4 levels lead to γ-globin reactivation. Surprisingly, the only measurable effect of ZNF410 depletion is the reactivation of γ-globin due to CHD4 down-regulation, which happens in an otherwise non-pathological terminal differentiation context (both in primary human cells and in the mouse in vivo) [CHD4 promoter and distal regulatory element, are the only regions of the entire genome with such high density of perfect consensus motifs (clusters of more than 10 motifs). Interestingly, these regions exhibit high evolutionary conservation, indicating that such sequences have been positively selected or co-opted by ZNF410 for proper functions. This may sound surprising as repression of the fetal γ-globin genes is not an absolute requirement for erythroid cell survival since γ-globin and HbF persistence has long been observed in the human population without any associated erythroid defects. However, as CHD4 has a broad expression pattern, such ZNF410 clusters of binding sites close to CHD4 may be essential in other tissues where its function may be limiting. It should be noted that one of the studies report that Zfp410 (the murine ZNF410 ortholog) KO mice show no overt abnormalities but littermate have slightly decreased body weight, indicating that ZNF410-mediated control of CHD4 may be important during specific developmental windows or within specific contexts that remain to be determined [Interestingly, by harnessing the power of Crispr-Cas9, an elegant study using in situ saturated mutagenesis have delineated the specific residues (protein interfaces) within NuRD complex components involved in γ-globin repression  . Althougervation . Upon ZNin vivo) ,91. The CHD4, in erythroid cells). The mammalian regulatory landscape therefore involves atypical TF binding events, with extremely restricted functional consequences [CHD4 regulation). This result underscores the usefulness of carefully designed Crispr screens with clear phenotypic readout to uncover novel regulatory mechanisms. These studies further highlight how intensive continuous research focused on deciphering the repressive mechanisms occurring at fetal globin genes has uncovered surprising and novel biological features. Whether such a peculiar low binding site TF is actually a widespread feature of gene networks in hematopoietic cells and other tissues remains to be uncovered, but these findings further expand the characterization of the regulatory networks at play in erythroid cells, and provide a unique framework for integrative analyses of cellular differentiation, and interplay between TF activity and coordinated activation/repression of gene networks.Besides the role of ZNF410 in HbF silencing, this study provides one of the very few examples of mammalian TF with an extremely small number of binding sites throughout the genome . However, a recent study revealed that contrarily to what was originally thought, the levels of GATA1 largely exceed the ones of GATA2 already from the earliest CFU-E onwards. This implies that GATA2 displacement from its chromatin binding site is not the mere reflection of GATA1 versus GATA2 protein dosage, and suggests that GATA2 can maintain its genomic binding even in the presence of an excess of GATA1. This indicates that dynamic GATA2 occupancy during erythropoiesis is more likely the result of decreased GATA2 concentration in differentiating erythroid progenitors rather than a direct displacement by excess GATA1. This switch is further reinforced by the action of other TFs such as TAL1, which activates Gata1 more potently than GATA2 does, probably because TAL1 protein levels were shown to be on average three times more abundant than GATA2. In light of these novel discoveries, the GATA-switch model has been refined and it opens up new questions such as how GATA2 and GATA1 co-exist and maintain their respective binding sites in the genome, and sheds new light on the combined actions of synergistic and antagonistic TFs. The mechanistic bases of the GATA-switch remain incompletely understood. A possible explanation for the capacity of GATA2 to maintain its genomic binding sites without overt displacement in the presence of its repressor GATA1 could be the result of a gradual capacity of GATA1 to occupy its genomic target sites, or an increased off rate of GATA1 at the early stages of erythropoiesis. This way, if GATA1 chromatin binding is not fully stabilized in immature progenitors, it would leave GATA2 with the possibility to properly regulate its target genes even in the presence of excess GATA1. Testing this hypothesis requires further quantitative measurements of relative dynamic chromatin occupancy. The advances in single-molecule tracking may offer new opportunities to shed light on this intriguing question [The above-mentioned process of GATA2-to-GATA1 expression transition is known as the “GATA-switch” whereby GATA2 activates iewed in ,103). Thquestion ,79,80. Iquestion  further + erythroid progenitors that likely undergo a lineage fate change upon Lsd1 ablation. This lineage conversion could be recapitulated in vitro upon the use of LSD1 inhibitors both on mouse and human erythroid progenitors, confirming the essential role played by LSD1 in maintaining erythroid cell identity. Gene expression analysis showed that the PU.1 TF is overexpressed in LSD1-deficient erythroid-derived myeloid cells. Mechanistically, LSD1 was shown to directly occupy the PU.1/Sfpi1 gene transcription start site and to maintain low H3K4 methylation modification, contributing to transcriptional repression. The RUNX1 transcription factor, which is a known positive regulator of PU.1 expression in stem cells and myeloid cells, contributes to PU.1 reactivation upon LSD1 loss of activity, and the erythroid-to-myeloid lineage transition could be rescued by inactivating either PU.1 or RUNX1 in Lsd1−/− cells. This confirms that a RUNX1/PU.1 axis is reactivated upon loss of LSD1 activity, which confers the ability to counteract the erythroid program and instruct a myeloid transcriptional state, at the origin of the observed erythroid-myeloid lineage plasticity [Several TF related to HSC maintenance  or belonging to other myeloid lineages  continue to be expressed in lineage-committed erythroid progenitors. Although their relative levels decline during the course of erythroid differentiation, they are still detectable up to the CFU-E stage, which suggests that committed erythroid cells may retain some degree of lineage plasticity ,96,105. Lsd1 KO animals express myeloid differentiation markers  [This is a surprising result as it shows that committed erythroid progenitors retain the capacity to diverge into an alternative myeloid fate, and that hematopoietic lineage restriction is relying on an active epigenetic mechanism to suppress alternative (myeloid) lineages, rather than being directed by a unidirectional and irreversible engagement into lineage-specific paths. The persistent (moderate) expression of myeloid TFs in erythroid progenitors (CFU-E) is key to this lineage plasticity and supports the idea that hematopoietic differentiation should be considered to be a continuum of cell states with antagonistic epigenetic switches favoring one lineage over another. However, whereas the erythroid progenitor-derived myeloid cells in  CD11b+) , it is nErythropoiesis is under the control of key signaling pathways regulating progenitor proliferation, survival and differentiation. Besides the well-known roles of SCF/c-KIT signaling to sustain immature progenitors proliferation, and the well-established role of the EPO/EPOR axis , and gluGATA1 mRNA and others involved in the regulation of GATA1 mRNA translation . The selective decrease of GATA1 translation efficiency was explained by the peculiar 5′UTR structure of its mRNA, which makes it unique among hematopoietic master regulators [KLF1, MYB, GATA2, LMO2, etc. was insensitive to decreased ribosomal proteins and ribosome content, providing a molecular explanation of the selective impairment of the erythroid lineage in DBA. Importantly, the erythroid defects could partially be rescued by raising GATA1 levels in DBA cells, underscoring the critical role played by GATA1 in this human disease. Importantly, it was shown that additional mechanisms contribute to disease severity [As mentioned above, the GATA1 TF is a master regulator of erythropoiesis . It bindgulators ,127. Accseverity  and GATAseverity , and chaseverity ,131  generating a novel transcriptionally active element in the α-globin locus, with chromatin signature matching classical promoter signatures . It was shown that this element partially acts as an enhancer blocker, therefore “attracting” the α-globin enhancers resulting in reduced α-globin gene interactions with their native enhancers. Since the α enhancers interact more frequently with the new promoter-like element and less frequently with the native α-globin promoters, it decreases α-globin production which drives globin chains imbalance and a thalassemic phenotype. This single DNA base change therefore imposes a strong novel regulatory constraint within the locus and partially displaces long-range interactions. A surprising observation that came from this study is that this new promoter-like element acts in an orientation-dependent fashion. This implies that the enhancer-blocking activity of such element is constrained by its orientation. Although no de novo CTCF binding could be detected that could explain this orientation-dependence The importance of GATA1 in the context of thalassemia has also been underscored by the discovery that the globin chain imbalance, which is characteristic of this disorder, indirectly influences GATA1 activity . LandmarAcute erythroid leukemia  accounts for ~5% of all AMLs but it unfortunately stands out as being one of the most aggressive human blood cancers, associated with a median survival of 8-17 months ,144. ImpGATA1 were detected in AEL cases [Nsd1−/− animals a signature of dysregulated Gata1 target gene expression was detected, no changes in GATA1 levels could be observed. However, GATA1 chromatin occupancy was severely decreased in Nsd1−/− erythroblasts, together with globally reduced mono-, di-, and tri-methylated H3K36. Using mass spectrometry analysis of differentially bound GATA1 co-factors, it was shown that NSD1 regulates the association of GATA1 with its negative regulator SKI and the NCoR co-repressor complex. In the absence of NSD1, SKI remains associated with GATA1 in differentiating erythroblasts, leading to impaired GATA1 chromatin binding, incapacity to regulate its target genes and impaired induction of terminal differentiation. Decreasing SKI expression in an Nsd1−/− background could rescue erythroid differentiation, confirming that the main differentiation block was SKI-mediated impairment of GATA1 chromatin targeting and/or stabilization. It therefore appears that efficient deposition of H3K36 methylation marks on chromatin at gene bodies is a critical step for stabilization of GATA1 on its targets. This observation is in accordance with other reports showing that transcription elongation marks (i.e. H3K36me3) play critical roles in erythropoiesis and terminal erythroid differentiation [Although some rare mutations of EL cases ,149 theiEL cases  (Figure ntiation ,155,156.Erythroid research remains at the forefront of many research fields. The new molecular markers ,157,158


Drosophila melanogaster R-loop landscape across strains and between sexes using DNA:RNA immunoprecipitation followed by high-throughput sequencing (DRIP-seq). We find that R-loops are associated with sequence motifs that are G-rich or exhibit G/C skew, as well as highly expressed genes, tRNAs, and small nuclear RNAs, consistent with a role for DNA sequence and torsion in R-loop specification. However, we also find motifs associated with R-loops that are A/T-rich and lack G/C skew as well as a subset of R-loops that are enriched in polycomb-repressed chromatin. Differential enrichment analysis reveals a small number of sex-biased R-loops: while non-differentially enriched and male-enriched R-loops form at similar genetic features and chromatin states and contain similar sequence motifs, female-enriched R-loops form at unique genetic features, chromatin states, and sequence motifs and are associated with genes that show ovary-biased expression. Male-enriched R-loops are most abundant on the dosage-compensated X chromosome, where R-loops appear stronger compared to autosomal R-loops. R-loop-containing genes on the X chromosome are dosage-compensated yet show lower MOF binding and reduced H4K16ac compared to R-loop-absent genes, suggesting that H4K16ac or MOF may attenuate R-loop formation. Collectively, these results suggest that R-loop formation in vivo is not fully explained by DNA sequence and topology and raise the possibility that a distinct subset of these hybrid structures plays an important role in the establishment and maintenance of epigenetic differences between sexes.R-loops are three-stranded nucleotide structures consisting of a DNA:RNA hybrid and a displaced ssDNA non-template strand. Previous work suggests that R-loop formation is primarily determined by the thermodynamics of DNA:RNA binding, which are governed by base composition  and transcription-induced DNA superhelicity. However, R-loops have been described at genomic locations that lack these properties, suggesting that they may serve other context-specific roles. To better understand the genetic determinants of R-loop formation, we have characterized the  Drosophila melanogaster adults. Most R-loops are found at simple repeats in regions of high transcriptional activity such as active chromatin states, 5’UTRs, tRNAs, and topologically associating domain boundaries. In both sexes, we find that R-loops are more common on the X chromosome compared to autosomes, likely due to an increased density of X-linked simple repeats that favor R-loop formation. While R-loops are largely conserved between sexes, we uncover a small but significant subset of sex-biased R-loops. Female-enriched R-loops are associated with genes showing ovary-biased expression and form at unique genome features, compared to other R-loops. Male-enriched R-loops occur preferentially at dosage-compensated genes on the X chromosome, yet surprisingly, show reduced levels of the marker of dosage compensation, H4K16ac, raising the possibility that the H4K16ac histone modification may attenuate R-loop formation. Our identification of sex-biased R-loops suggests a specialized role for these structures in establishing and maintaining sex-specific epigenetic programs.R-loops are DNA:RNA hybrid structures that act as important regulators of gene expression and genomic stability and whose dysregulation can contribute to diseases such as neurological disorders and cancer. Here, we utilize DNA:RNA immunoprecipitation followed by high-throughput sequencing (DRIP-seq) to assess the sex-specific variability in R-loop formation in  Within the nucleus, the mechanical processes driving transcription must strike a balance between providing the cell sufficient transcripts for survival and the inherent danger to genome stability via induction of torsional stress. One mechanism by which cells regulate transcription and relieve said stress is the formation of R-loops. R-loops form when RNA invades double-stranded DNA and binds the template strand, creating a DNA:RNA hybrid and displacing the non-template strand. R-loops have been associated with a variety of biological processes and are implicated in essential aspects of gene regulation as well as genome stability ,2. PersiOn a mechanistic level, there is emerging evidence that R-loop formation may be primarily driven by a combination of DNA sequence and DNA topology. At the sequence level, previous work has shown that G-rich sequences and sequences exhibiting GC skew are prone to R-loop formation . DNA:RNAIn terms of DNA topology, R-loops are known to form in response to transcription and replication-induced torsional stress in double-stranded DNA . R-loop in trans at circular RNAs (circRNAs) to regulate splicing factor recruitment [A relationship between R-loops and DNA torsion is further supported by multiple studies that document a correlation between R-loop formation and high rates of transcription, which leads to negative supercoiling upstream of the translocating polymerase. For example, in yeast strains lacking RNase H activity, R-loops have been detected at Pol III-transcribed genes, such as tRNAs and small nuclear RNAs, likely due to their high expression levels . Furtherruitment  and DNA ruitment , and theruitment ,29. ThesIn this study, we investigate the determinants of R-loop formation in Drosophila using DNA:RNA Immunoprecipitation followed by high-throughput sequencing (DRIP-seq). We characterize genome features and sequence motifs that are associated with R-loops, and we compare the location and strength of R-loops between males and females to address whether transcriptional levels are major determinants of R-loop formation. We also assess whether hypertranscription of dosage-compensated genes is associated with increased R-loop formation in males. Overall, we find a consistent positive association between gene expression level and R-loop formation within both sexes. Furthermore, for female-enriched R-loops, we find a significant association between sex-biased gene expression and sex-biased R-loop formation. By contrast, we find that male-biased R-loops are not associated with male-biased gene expression, but instead are enriched on the X chromosome and are associated with dosage-compensated genes. While male-biased and non-biased R-loops are associated with similar sequence motifs, genome features, and chromatin states, female-biased R-loops inhabit unique regions of the genome. These results suggest that, while high transcription levels may play a role in R-loop formation, they are not sufficient to determine the R-loop landscape in Drosophila. Other genetic and epigenetic factors must also be involved, lending support to the multifaceted and context-specific nature of these genomic features.D. melanogaster from the Drosophila Genetic Reference Panel (DGRP): DGRP379 and DGRP732. Standard DRIP-seq protocols employ a restriction enzyme cocktail to fragment chromatin. However, previous studies have shown that this approach biases R-loop enrichment for specific euchromatic regions of the genome [To determine the characteristics that contribute to natural variation in R-loop formation, we performed DRIP-seq separately for adult males and females in two strains of e genome . To corre genome  Fig 1C) aMetagene analysis of gene-associated R-loops reveals that at autosomal genes, R-loops appear most abundant just downstream of the transcriptional start site (TSS) and are absent from the 3’UTR, while X chromosome-associated R-loops show increased signal both across the entire gene region and at the 3’UTR Figs  and S1B.Despite fewer overall R-loop peaks identified in males, gene-associated R-loop signal is higher in males compared to females on both autosomes and the X chromosome Figs  and S1B.n in the nonDE group and ‘GGCGAAGGAG’ and (CTC)n in the FE group), several enriched motifs lack such skew and instead display no skew ((CACA)n in the nonDE and ME groups and (AGAG)n in the FE group) or AT-skew (poly-A tracts in the nonDE and ME groups)  . R-loop in yeast ,21,39,40The unique motifs found at FE R-loops suggest that they may occupy genomic features distinct from the nonDE and ME R-loops. Assigning these sex-biased R-loops to known chromatin states  reveals Given the relationship between high rates of transcription and R-loop formation, we next sought to determine whether the ME and FE R-loops occur at genes that show sex-biased expression patterns. We performed RNA-seq from whole flies for the same four samples used for DRIP-seq and found that, surprisingly, ME and FE R-loop-containing genes show similar expression patterns in both sexes . To addrBecause the gonads make up a large percentage of the adult body, we reasoned that the DRIP signal from whole adult flies is likely similar to that from gonads alone. To test this prediction, we performed DRIP-seq in ovaries dissected from adult females and found strong, highly significant correlations between the female whole fly samples and ovary samples for both nonDE and FE peaksets from the adult data  [To further explore the relationship between R-loop formation and MOF/H4K16ac levels, we extended our comparison above to females. In females, the MSL complex is absent due to translational repression of al (Sxl) . Insteadal (Sxl) . We therin trans [in trans remains to be determined. In addition to an association between R-loops and overall gene transcription, our observation of R-loop enrichment within the BLUE Polycomb-regulated chromatin state and at PREs in Drosophila supports previously established roles that R-loops play in Polycomb-mediated gene repression [Our assessment of natural R-loop variation between sexes has revealed multiple insights. First, R-loops are largely associated with transcriptionally active loci: they are found in chromatin states with either broad or specific transcriptional programs. More specifically, R-loops form proximal to the TSS, at multiple classes of ncRNAs and G-quadruplexes, and at TAD boundaries where transcriptionally active loci have been shown to reside . Mechaniin trans ,27, yet pression ,25,50.Our differential enrichment analysis confirms the strong conservation of the R-loop landscape between individuals and sexes, in line with previous comparisons across human and murine cell lines . AdditioDespite the overall conservation of the R-loop landscape between sexes, we also observe a subset of sex-biased R-loops, suggesting some level of specialization for their formation and function. The enrichment of nearly half of all male-enriched R-loops on the male X chromosome likely reflects its hypertranscribed, dosage-compensated state. Furthermore, the relationship between transcription and R-loop formation is maintained even within the X chromosome: R-loop-containing genes on the male X exhibit higher gene expression levels than X-linked genes lacking R-loops. It seems paradoxical, then, that canonical markers of active transcription deposited by the MSL and NSL complexes are reduced at R-loop-containing genes compared to genes with no R-loops. One possible explanation is that H4K16 acetylation may subtly disfavor R-loop formation. Indeed, previous studies have established a role for the DCC and H4K16ac in reducing negative superhelicity and disordering dosage-compensated chromatin to encourage DNA binding protein activity and Pol II loading ,53. ThisFor the genes associated with female-enriched R-loops, their ovary-biased expression raises the possibility that the FE R-loops form specifically in ovaries, yet the function of these R-loops remains elusive. Their unique motifs and association with ribosomal protein and translation-related genes distinguish them from the nonDE and ME groups. The sequence-specific transcription factor binding protein (M1BP) regulates transcription of ribosomal protein genes  and is mIn summary, this work provides insight into the genome features, sequence motifs, and natural variation of the R-loop landscape in Drosophila. Our results are consistent with transcription rate, DNA torsion, and base composition being important determinants of R-loop formation. However, none of these properties fully explains the sex-biased R-loops that we identify, suggesting that other genetic or epigenetic mechanisms are involved in their formation. Further study of these male and female-biased R-loops will provide insight into their role in the establishment and maintenance of epigenetic differences between sexes.As R-loops are known to be sensitive to sonication-induced degradation ,31, we dFor DRIP-seq of ovaries, ovaries were dissected from 2 to 10-days-old w1118 females  and homogenized in cold PBS. Genomic DNA was extracted and processed for S1-DRIP-seq as described above.D. melanogaster reference genome assembly FlyBase version 6 [Reads were trimmed with trimmomatic  with theersion 6 ,61 usingersion 6  with theD. melanogaster, for each of our DRIP-seq samples. To control for the differences in length among the chromosome arms, we normalized the peak counts by chromosome length . To determine whether the increased number of peaks observed on the X chromosome was statistically significant, we used a binomial test to test the null hypothesis that there is no difference in peak density between the X chromosome and autosomes.The ENCODE group has found that there is a consistent relationship between the number of ChIP-seq peaks identified and sequencing depth, without a clear saturation in most cases, due to an increased ability to identify low-affinity sites with increased sequencing depth . Reads aDRIP peaks from each sample were intersected with genomic features and chromatin states using bedtools intersect  and plotDifferential R-loops were identified using DiffBind . BrieflyMetaprofiles of expressed R-loop-containing genes were generated using deeptools  computeMgimme maelstrom from GimmeMotifs [Motif analysis was performed using STREME (MEME suite)  with themeMotifs  using thmeMotifs  was usedFemale adult DRIP-seq and ovaries DRIP-seq read coverage at loci from nonDE and FE peaksets was compared using deeptools  multiBamWe used ~10 whole adult DGRP379 and DGRP732 males and females. Flies were homogenized with an electric pestle in DNA/RNA Shield solution (Zymo Research). Homogenized tissue was digested with Proteinase K and RNA was purified with the Zymo Quick-RNA Plus Kit (Zymo Research). Ribosomal RNAs were removed using siTools rRNA depletion Kit  and MyOne Streptavidin C1 Dynabeads (ThermoFisher) (#65001). Ribosomal RNA-depleted RNA was purified using the RNA Clean and Concentrator-5 kit (Zymo Research). Illumina libraries were generated using NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB).RNA sequencing reads were first aligned to the FlyBase r6.27 rRNA sequences using HISAT2 . Non-ribhttp://flyatlas.org/. Genes were separated by differential enrichment group: “nonDE,” “FE,” “ME,” and “no R-loop,”. The minimum gene expression threshold for each tissue was determined by detectable expression in at least two of four microarrays .Microarray expression data were downloaded from S1 Fig(A) R-loop formation at various genetic features on autosomes (upper panel) and the X chromosome (lower panel). R-loop enrichment is shown as the observed number of DRIP-seq peaks overlapping each feature (or chromatin state) divided by the expected number of peaks see . P-value(TIF)Click here for additional data file.S2 Fig(A) Metaprofiles of R-loop signal across protein-coding genes from , separat(TIF)Click here for additional data file.S3 Fig(A) Male-to-female gene expression ratios by DE group on autosomal and X chromosome genes. Wilcoxon test, **,*** = p < 0.01, 0.001.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


MUC1 gene evolved in mammals to protect barrier tissues from loss of homeostasis. MUC1 encodes the transmembrane MUC1-C subunit, which is activated by inflammation and contributes to wound repair. However, chronic activation of MUC1-C contributes to lineage plasticity and carcinogenesis. Studies in human NEPC cell models have demonstrated that MUC1-C suppresses the AR axis and induces the Yamanaka OSKM pluripotency factors. MUC1-C interacts directly with MYC and activates the expression of the BRN2 neural transcription factor (TF) and other effectors, such as ASCL1, of the NE phenotype. MUC1-C also induces the NOTCH1 stemness TF in promoting the NEPC cancer stem cell (CSC) state. These MUC1-C-driven pathways are coupled with activation of the SWI/SNF embryonic stem BAF (esBAF) and polybromo-BAF (PBAF) chromatin remodeling complexes and global changes in chromatin architecture. The effects of MUC1-C on chromatin accessibility integrate the CSC state with the control of redox balance and induction of self-renewal capacity. Importantly, targeting MUC1-C inhibits NEPC self-renewal, tumorigenicity and therapeutic resistance. This dependence on MUC1-C extends to other NE carcinomas, such as SCLC and MCC, and identify MUC1-C as a target for the treatment of these aggressive malignancies with the anti-MUC1 agents now under clinical and preclinical development.Castration resistant prostate cancer (CRPC) is responsive to androgen receptor (AR) axis targeted agents; however, patients invariably relapse with resistant disease that often progresses to neuroendocrine prostate cancer (NEPC). Treatment-related NEPC (t-NEPC) is highly aggressive with limited therapeutic options and poor survival outcomes. The molecular basis for NEPC progression remains incompletely understood. The  Castration resistant prostate cancer (CRPC) is effectively treated with agents, such as enzalutamide and abiraterone, that target the androgen receptor (AR) axis . HoweverResistance to AR pathway-targeted agents is associated with significant increases in truncated AR splice variant-7 (AR-V7) expression ,13,14. AMUC1 gene is aberrantly expressed in advanced CRPC and NEPC [MUC1 is amplified in 30% of a CRPC cohort with NEPC enrichment compared to 6% in the SU2C CRPC cohort and 2% in the TCGA primary PC cohort [As another potential target, the and NEPC . MUC1 isC cohort . In addiC cohort . UpregulC cohort ,26,27,  [MUC1 gene in breast cancers [MUC1 first appeared in mammals, which supported a fundamental role in placentation and lactation [ cancers ,32. The ts (TRs) ,34. Furt cancers . Of inteactation ,37.MUC1 encodes a polypeptide that undergoes autocleavage into N-terminal (MUC1-N) and C-terminal (MUC1-C) subunits [O-glycosylation and extends from the cell surface into a protective mucous barrier [subunits . In turnsubunits . MUC1-N  barrier . MUC1-C  barrier . The MUC barrier . This ba barrier ,41. Impo barrier .The activation of MUC1-C in the response of barrier tissues to stress induces inflammatory, proliferative and remodeling pathways that are associated with wound healing and repair ,44. MUC1MUC1-C drives lineage plasticity and progression to the CSC state in human cancers ,44. StudBRN2 is a neural transcription factor that drives SOX2, induces NE markers and enriches for an NEPC gene signature . DU-145 t-NEPC is associated with the activation of gene programs that drive lineage plasticity, the CSC state and NE dedifferentiation ,4,55,56.The seminal findings that NEPC CSCs are dependent on MUC1-C for self-renewal capacity uncovered a new line of investigation; that is, more precisely determining how MUC1-C drives the NEPC CSC state. Certain insights in this regard emerged from work on the involvement of MUC1-C in activating the PRC1/2 complexes in cancer cells ,62,63. TNOTCH1, which has been linked to stemness and driving CRPC [The finding that MUC1-C activates BAF in NEPC cells thus opened a new line of investigation for the involvement of this SWI/SNF complex in NEPC progression . The bining CRPC , was ideing CRPC . In addiing CRPC . Consisting CRPC . These fing CRPC , and  chromatin remodeling complex includes the BRG1 ATPase, PBRM1/BAF180, ARID2/BAF200 and BRD7 . BRG1 isg motifs . These fg motifs ,62,73,84SLC7A11 gene [SLC7A11 gene and for xCT expression [G6PD gene and expression of glucose-6-phosphate dehydrogenase (G6PD), which converts NADP+ to NADPH [SLC7A11, G6PD and other antioxidant genes, silencing MUC1-C and PBRM1 in NEPC cells decreased GSH, GSH/GSSG and NADP/NADPH levels in association with increases in sensitivity to oxidative stress [The MUC1-C→esBAF pathway contributes to the NEPC CSC state . CSCs arA11 gene , which eA11 gene . In thispression . MUC1-C/to NADPH . In accoe stress . Of furte stress . These fNOTCH1 was identified among these genes in concert with the demonstration that MUC1-C induces NOTCH1 in driving the self-renewal of NEPC CSCs [NOTCH1 [NOTCH1 proximal enhancer-like sequence (pELS) with JUN and ARID1A in association with increases in chromatin accessibility, H3K4 trimethylation and NOTCH1 expression [EGR1 stemness gene, indicating that MUC1-C activates a JUN- and esBAF-dependent pathway of importance for increasing the chromatin accessibility of genes that promote the NEPC state [Chromatin remodeling is critical for lineage plasticity, EMT and the CSC state . MUC1-C EPC CSCs ,90. In f [NOTCH1 , MUC1-C PC state ,91.IFNGR1 gene at a distal enhancer-like signature (dELS) in association with increases in chromatin accessibility, H3K4me3 levels and IFNGR1 expression [STAT1 and IRF1 genes and their expression  MYC family members, (ii) Yamanaka pluripotency factors, (iii) NE lineage dictating TFs and (iv) NOTCH stemness TFs. Moreover, targeting MUC1-C genetically and pharmacologically in NEPC, PDAC-NE, SCLC and MCC cells inhibits tumorsphere formation and tumorigenicity  24,92,99,100,101In summary, the discoveries described here indicate that MUC1-C contributes to NEPC progression by activating pathways that drive lineage infidelity, epigenetic reprogramming and chromatin remodeling ,84,91,92


The stress-responsive transcription factor EB (TFEB) is a master controller of lysosomal biogenesis and autophagy and plays a major role in several cancer-associated diseases. TFEB is regulated at the posttranslational level by the nutrient-sensitive kinase complex mTORC1. However, little is known about the regulation of TFEB transcription. Here, through integrative genomic approaches, we identify the immediate-early gene EGR1 as a positive transcriptional regulator of TFEB expression in human cells and demonstrate that, in the absence of EGR1, TFEB-mediated transcriptional response to starvation is impaired. Remarkably, both genetic and pharmacological inhibition of EGR1, using the MEK1/2 inhibitor Trametinib, significantly reduced the proliferation of 2D and 3D cultures of cells displaying constitutive activation of TFEB, including those from a patient with Birt-Hogg-Dubé (BHD) syndrome, a TFEB-driven inherited cancer condition. Overall, we uncover an additional layer of TFEB regulation consisting in modulating its transcription via EGR1 and propose that interfering with the EGR1-TFEB axis may represent a therapeutic strategy to counteract constitutive TFEB activation in cancer-associated conditions. The stress-responsive transcription factor TFEB is a master controller of lysosomal biogenesis and autophagy and plays a major role in several cancer-associated diseases. Integrative genomic analyses identify the immediate-early gene EGR1 as a positive transcriptional regulator of TFEB expression and reveal the function of the EGR1-TFEB transcriptional axis in physiology and disease. Cells have evolved by improving their capacity to metabolically adapt to changes in substrate availability and energy requirements. This metabolic flexibility, essential to maintain energy homeostasis, is made possible by the coordinated interplay of diverse quality control mechanisms. In this scenario, transcriptional control of gene expression heavily impacts the homeostatic energy balance in both physiological and pathological conditions . ImportaAn example is transcription factor EB (TFEB), a member of the microphthalmia family (MiT-TFE) of bHLH-leucine zipper transcription factors, which act as a global modulator of intracellular clearance and energy metabolism through the control of lysosomal biogenesis and autophagy –5. BesidTFEB activity is known to be regulated at the posttranslational level through phosphorylation mediated by the nutrient-sensing complex mTORC1. Upon nutrient depletion, TFEB translocates to the nucleus and induces a global transcriptional response to ensure adaptation to changes in cells’ energy demands ,12–15. DHere, we surveyed transcription factors for their capacity to control TFEB expression and identified the immediate-early gene EGR1 as a positive transcriptional regulator of TFEB. Stress signals and secreted factors, including growth factors, tumor necrosis factors, inflammatory factors, ionizing radiation, reactive oxygen species, or others, are known to trigger EGR1 transcription via the MEK1/2/ERK signaling cascade –20. Once). This trend is also maintained across several ENCODE reference cell lines  with a threshold of 0.05. The coverage signal profile was generated with deep tools [deeptools] using CPM normalization.Paired sequencing reads were aligned on human GRCh38 reference genome using BWA[BWA] and filtered with samtools [samtools] to remove unmapped read pairs, not primary alignment, reads failing platform quality, with mapping quality score below 30, and duplicate reads were then removed using Picard MarkDuplicates [picard]. Each sample was equally split into 2 pseudoreplicates, peaks were called with MACS2[MACS] with S1 Fig(A) (Left) Boxplot showing the gene expression distribution of TFEB across different tissues. The average nTPM value for each tissue has been recovered from Human Protein Atlas (proteinatlas.org). (Right) Boxplot showing the gene expression distribution of TFEB across different cell lines. The CPM value for each cell line has been quantified from UCSC Transcription data . (B) Structure of the human TFEB locus. Locus coordinates and encoded protein lengths of TFEB isoforms are indicated, along with coding exons (black boxes), UTR regions (red boxes), and directionality (arrows). The primary reference transcriptional isoform is underlined. (C) UCSC genome browser visualization of normalized RNAseq, H3K4me1, H3K4me3, and DNase profiles at the indicated locus in 6 ENCODE reference cell lines (colors). Arrow #1 indicates the transcriptional start site of the major TFEB isoform common to the reference cell lines. Arrow #2 corresponds to the region where most active H3K4me3 marks are located. Arrow #3 corresponds to the most accessible DNAseI region. Arrow #4 highlights the location of the chromatin loop enclosing the TFEB locus with likely carryover epigenetics and chromatin marks from the promoter region. (D) TFEB CREs are indicated on the bottom (from 1 to 7). The promoter region, which includes CREs 1, 2, and 3, is displayed in red. Chromatin folding profile (Hi-C HFF) is also displayed, reporting only the strongest interactions. Individual quantitative observations that underlie the data summarized here can be located under the Supporting information file as cis-regulatory element; TFEB, transcription factor EB.(TIF)Click here for additional data file.S2 Fig(A) Balloon plots of representative term enrichment analysis results using Custom Genesets (Autophagy and Lysosome) and Curated Pathways  of genes up-regulated upon EGR1 overexpression, with respect to control cells (CTRL). Enriched terms are ranked by adjusted p-value (x-axis), and the balloon color scale represents the percentage of overlap between the input genes and the analyzed term. Significance threshold  is reported. (B) RNAseq-based expression (CPM) of representative genes upregulated upon CTRL and EGR1 overexpression in HeLa cells. Mean ± SD values are shown. Individual quantitative observations that underlie the data summarized here can be located under the Supporting information file as (TIF)Click here for additional data file.S3 Fig(A) (Left) Schematic representation of EGR1 target genes, as defined by ChIPseq analysis, and their corresponding dynamics at 6 hours of starvation . EGR1 targets were defined by evaluating their enrichment within 2 kb of the transcriptional start site of the corresponding genes. (Right) Pie chart showing the percentage of EGR1 target genes, which undergo transcriptional changes in starvation with respect to fed conditions. (B) Balloon plot of representative term enrichment analysis results using Curated Pathways  of EGR1 targets up-regulated in starvation. Enriched terms are ranked by adjusted p-value (x-axis), and the balloon color scale represents the percentage of overlap between the input genes and the analyzed term. Significance threshold  is reported. Individual quantitative observations that underlie the data summarized here can be located under the Supporting information file as (TIF)Click here for additional data file.S4 Fig(A) Representative images of wound healing assay acquired at 24 hours from the removal of silicone insert. HeLa-FLCN KO expressing low (+TFEB low) or high (+TFEB high) levels of TFEB, along with control cells (CTRL) were employed for the migration assay. (B) Histogram of High-Content Imaging-based quantification of the distance between edges measured at time 0 (T0) and 24 hours (T24) after the removal of silicone insert relative to the cells indicate above. ANOVA followed by Tukey’s multiple comparisons test was used; *p < 0.05, **p < 0.01, ***p < 0.001. Individual quantitative observations that underlie the data summarized here can be located under the Supporting information file as (TIF)Click here for additional data file.S5 Fig(A) Bar plots showing relative quantification of EGR1 and TFEB mRNA levels measured by qPCR in HeLa cells treated with DMSO or Trametinib in fed conditions (FED) and after 8 hours of starvation (HBSS). Values were normalized on the HPRT expression and displayed as fold change relative to DMSO in FED. Mean ± SD values are shown. ANOVA was used; *p < 0.05, **p < 0.01, ***p < 0.001. (B) Immunoblot analysis of EGR1, TFEB, ERK, and pERK expression in HeLa cells treated with DMSO or Trametinib in fed condition (FED) and after 8 hours of starvation (HBSS). GAPDH was used as a loading control. (C) Volcano plot showing the results of the differential expression analysis in HeLa-FLCN KO cells upon Trametinib treatment compared to DMSO, as a function of log2 fold change (x-axis) and -log10 FDR (y-axis). Up- and down-regulated genes are highlighted . (D) Balloon plot of representative term enrichment analysis results using Curated Pathways  relative to down-regulated genes upon Trametinib treatment in HeLa-FLCN KO cells. Enriched terms are ranked by adjusted p-value (x-axis), and the balloon color scale represents the percentage of overlap between the input genes and the analyzed term. Significance threshold  is reported. Individual quantitative observations that underlie the data summarized here can be located under the Supporting information file as (TIF)Click here for additional data file.S1 DataEach tab contains the panels relative to the indicated figure for which a quantitative analysis was required.(XLSX)Click here for additional data file.S1 Raw Images(ZIP)Click here for additional data file.


The m1A landscape and potential functions of m1A modification in neurons remain poorly understood. We explored RNA  m1A modification in normal and OGD/R-treated mouse neurons and the effect of m1A on diverse RNAs. We investigated the m1A landscape in primary neurons, identified m1A-modified RNAs, and found that OGD/R increased the number of m1A RNAs. m1A modification might also affect the regulatory mechanisms of noncoding RNAs, e.g., lncRNA–RNA binding proteins (RBPs) interactions and circRNA translation. We showed that m1A modification mediates the circRNA/lncRNA‒miRNA–mRNA competing endogenous RNA (ceRNA) mechanism and that 3' untranslated region (3’UTR) modification of mRNAs can hinder miRNA–mRNA binding. Three modification patterns were identified, and genes with different patterns had intrinsic mechanisms with potential m1A-regulatory specificity. Systematic analysis of the m1A landscape in normal and OGD/R neurons lays a critical foundation for understanding RNA modification and provides new perspectives and a theoretical basis for treating and developing drugs for OGD/R pathology-related diseases.Cerebral ischaemia‒reperfusion injury (IRI), during which neurons undergo oxygen-glucose deprivation/reoxygenation (OGD/R), is a notable pathological process in many neurological diseases. N1-methyladenosine (m Writers9 and 58 . TRMT61Bsition 9 . Readersnscripts . Erasers and RNA . Two Alk RNA  is the main pathological process in many brain diseases, such as traumatic brain injury (TBI)  and acut1A modification of various RNAs  in mouse neurons and explored the potential effects of this modification on different RNA functions. We first identified m1A-modified peaks (m1A peaks) in normal neurons and OGD/R-treated neurons at different times and thus discovered a GA-rich motif. The number of RNAs with m1A modification, the chromosome distribution, and the changes in m1A modification on various RNAs before and after OGD/R treatment were analysed. The number of m1A RNAs was increased after OGD/R treatment, and most of these differentially expressed m1A-modified RNAs were involved in biological processes and signalling pathways related to the regulation of cellular homeostasis and synapses. In addition, the results indicated that m1A modification mediates the complex posttranscriptional regulation network and that the regulation of m1A modification in key nodes of RNA interaction networks may cause widespread changes in downstream signalling. We also proposed three patterns of m1A modification, with genes fitting these modification patterns participating in different biological functions and signalling pathways. Overall, we analysed the characteristics of m1A modification of multiple RNAs in normal neurons and OGD/R-treated neurons from multiple perspectives. The landscape and other findings provide evidence useful for the exploration of epitranscriptomic mechanisms and the development of targeted drugs in OGD/R-related pathological processes.In this study, we identified the characteristics of m1A modifications in the transcriptome is uncharacterized in the nervous system, especially in important component neurons. We performed m1A RNA immunoprecipitation sequencing (MeRIP-seq) to clarify the m1A transcriptomic landscape in primary neurons and OGD/R-treated neurons  and 25.1% were located in the 5' untranslated region (5’UTRs) in mRNAs, with 88.4% located in CDSs and 8.9% in 5’UTRs in circRNAs (1A peaks were in the CDS in circRNAs than in mRNAs (88.4% vs 54.2% in the Control group), and a clear decreasing trend in near-5’UTRs was observed ME Suite . Indeed,database  (v 2022)database , and the1A mainly depend on m1A demethylase (‘erasers’), m1A methyltransferase (‘writers’), and m1A read-binding protein (‘readers’). We wondered whether these m1A regulators were differentially expressed after OGD/R-treatment. The results showed that the expression of Alkbh3, Trmt10c, Trmt61a, Ythdf2, and Ythdf3 was statistically different among the three groups  in normal primary neurons and OGD/R-treated neurons to determine whether there are characteristic modifications of different RNAs after OGD/R treatment. First, we determined the numbers of m1A mRNAs identified by four different methods: the conventional, treatment, mismatch, and trough methods . The most m1A mRNAs were identified by the conventional method, while the mismatch method identified the least . Since the modification density differed by treatment, we sought to determine whether the amount of m1A modification on each chromosome differs before and after treatment. The numbers of m1A mRNAs on each chromosome were increased after OGD/R treatment  (munctions . However lncRNAs . We then methods . Fewer m methods . The m1Ar: 1294) .1A lncRNAs in the Control and OGD/R-treated groups. The two main genomic sources of m1A lncRNAs were exon sense-overlapping regions and intergenic regions . We also counted the m1A lncRNAs on each chromosome in normal and OGD/R-treated neurons and found that the number of m1A lncRNAs on each chromosome was increased after OGD/R treatment  . However, the m1A level did not differ significantly between the OGD/R 1.5 hr and Control groups  . Next, wl groups . m1A cireatments .1A-methylated circRNAs in the OGD/R 1.5 hr and OGD/R 3 hr groups , we identified some circRNAs (237/811) with translation ability among the identified differentially m1A-modified circRNAs. In addition, m6A modifications annotated in the database were present on some m1A circRNAs. The polypeptide structures were predicted to show the possible translation products. Then, mmu_circ_0000705 (encoded by App) and mmu_circ_0002207 (encoded by Foxo3) were selected to show the abovementioned features  (Arid1a) (Grin2d) (Wdtc1)  and the neuron-specific chromatin remodelling complex (nBAF complex). We cotransfected Arid1a, Mir101b, and Trmt6 (an m1A methyltransferase) into HEK293T cells. Trmt6 alleviated the inhibitory effect of Mir101b on Arid1a expression . Rbfox3–Mir129 is another miRNA‒mRNA pair affected by Trmt6. After cotransfection of Rbfox3–Mir129 and Trmt6, Trmt6 alleviated the repression of target genes by the miRNA  (Grin2d and Wdtc1), after cotransfection with their corresponding miRNAs and Trmt6, Trmt6 did not affect the ability of the miRNAs to bind to their target genes  , neural (Arid1a) , NMDA re(Grin2d) , and ubi (Wdtc1) . Therefoto mRNAs . Arid1a y ANOVA) . Regardiet genes . This re1A modifications in mRNAs and noncoding RNAs and explored the potential functions of those RNAs. However, the m1A patterns remain unknown. We applied the NMF method to identify m1A modification patterns; k=3 was the value at which the largest change occurred and was thus selected as the optimal k value  since its members were enriched in signal transduction and biosynthetic processes  analysis. The ‘Enrichr’ database (http://amp.pharm.mssm.edu/Enrichr/) was used to enrich the TFs  in normal neurons and OGD/R-treated neurons. We identified thousands of m1A mRNAs and found that the number and level of mRNA m1A modifications were increased after OGD/R. Regarding lncRNAs, in addition to exploring the basic features of m1A modification, we found two important m1A lncRNAs, Meg3 and Neat1, might be associated with RBPs and possibly play vital roles in the regulation of mRNA metabolism. There are approximately 3000 m1A circRNAs in neurons, and they play roles in the nervous system. The m1A modifications on circRNAs such as App and Foxo5 may be involved in the mechanism of circRNA translation. Furthermore, we explored the underlying associations among m1A RNAs  and found that there are two different complex regulatory networks for differential expression and differential methylation. We confirmed that m1A modification of some mRNAs affects the function of other regulatory mechanisms. The m1A modification patterns in neurons were also investigated. Three m1A patterns were identified: the Metabolism-associated cluster, Autophagy-associated cluster, and Catabolism-associated cluster. And there are specific TF networks in these three m1A patterns.mnd cells , its fea6A modification, the most important and common modification from yeast to humans, is located predominantly at RRACU (where R=A/G) consensus motifs in mammals and RGAC (where R=A/G) consensus motifs across yeast species -YTHDF2 (m6A ‘reader’), which provides evidence for a crosstalk between different RNA modifications  are related to neural development and differentiation; the genes that were not affected (Grin2d are Wdtc1) are related mostly to broader cellular functions. Therefore, we asked whether m1A modification preferentially occurs on certain genes. m1A modification depends on several methyltransferases. Thus, do methyltransferases exhibit preferences for different functional genes? Just as we proposed that m1A modification of different RNA species may be mediated by different m1A-related enzymes, we hypothesize that different methyltransferases may exhibit preferences for different functional genes.Transcriptome-wide m1A modification patterns remain unclear in neurons. As our results showed, m1A modification of different RNAs may be regulated by different enzymes, and different functional genes may be regulated via different methyltransferases; thus, identification of m1A patterns is urgently needed. We identified three patterns of m1A modification in neurons: a metabolism-associated cluster, an autophagy-associated cluster, and a catabolism-associated cluster. The genes with each pattern of m1A modification perform different biological functions. These three m1A patterns are closely related to the fate of neurons. The proper functioning of neuronal metabolic mechanisms ensures the function of neurons  in OGD/R, but also constructed noncoding RNA regulatory networks for differential expression and differential methylation under OGD/R treatment, and identified a number of noncoding RNAs that may play important roles, all of which may be targets for mitigating the effects of OGD/R.With the deepening of the RNA epitranscriptomic research studies, some mc target . METTL14c target . In addi targets . In our 1A modification in neurons and OGD/R-treated neurons. We identified m1A modifications on different RNAs and explored the possible effects of m1A modification on the functions of different RNAs and the posttranscriptional regulation mechanism. Although we found and proposed some roles related to m1A modification, it is still a relatively complex modification type . Therefore, as more research techniques are developed , were used mainly for harvesting primary cerebral neurons. All experiments were performed in adherence to the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals and were approved by the Medical Ethics Committee of Qilu Hospital of Shandong University (IACUC Issue NO. DWLL-20210061).2 incubator. Dissociated cortical cells were plated on poly-L-lysine -coated cell culture dishes and cultured in DMEM-HG  containing 10% foetal bovine serum  and 1% penicillin/streptomycin  at a density of 1.0×106 cells/mL. Four hours after seeding, the medium was replaced with neurobasal medium  supplemented with B-27 . Cells were cultured in a humidified incubator at 37°C with 5% CO2. The medium was changed every 3 days. Cultures were used for in vitro experiments after 7 days.Primary cultures of cerebral neurons were obtained as described previously . In brie2) for 24 hr. Neurons cultured in a normal serum-free medium under normoxic conditions served as Controls.The OGD/R model was established using a previously described method with slight modifications . CultureTotal RNA was extracted from primary cultured neurons using TRIzol reagent  according to the manufacturer’s protocol. A NanoDrop ND-1000 system  was used to measure the RNA concentration in each sample. The OD260/OD280 ratio was assessed as an index of RNA purity, and samples with OD260/OD280 values ranging from 1.8 to 2.1 met the qualifications for purity. RNA integrity was evaluated using denaturing agarose gel electrophoresis.1A antibody in IP buffer. Both input samples without IP and m1A input samples were used for library construction using a NEBNext Ultra II Directional RNA Library Prep Kit . Eluted RNA fragments were converted to cDNA and sequenced or treated to induce partial m1A to m6A conversion before cDNA synthesis. Library sequencing was performed using an Illumina HiSeq 4000 instrument  with 150 bp paired-end reads.MeRIP-seq was performed by Cloudseq Biotech Inc  according to a published procedure with slight modifications . In briep value)>3 were detected using Model-Based Analysis of ChIP-Seq (MACS) software (v2.2.7.1). Differentially methylated sites with a fold change of ≥2 and false discovery rate (FDR) of ≤0.0001 were identified with the diffReps differential analysis package (v1.55.6). The peaks identified by MACS and diffReps that overlapped with exons of mRNAs, lncRNAs, and circRNAs were identified and selected for further analysis. In addition to conventional peak calling , the peaks with A->T mismatches (mismatch method) were analysed. In addition, m1A sites have a partial chance of terminating reverse transcription, resulting in lower coverage in the middle of the peak (near the m1A site) than on both sides, forming a depression. These peaks and depressions (trough method) were also analysed. Moreover, when m1A was converted into m6A, the mismatch and termination properties of m6A were lost, while reverse transcription was normal. Thus, after sequencing, peaks with the normal shape could be detected at the sites (treatment method).In brief, quality control of the paired-end reads was performed with FastQC (v0.11.9) prior to trimming of 3’ adaptors and removal of low-quality reads using Cutadapt software (v1.9.3). Then, HISAT2 software (v2.0.4) was used to align the clean reads from all libraries to the reference genome (mm10) downloaded from Ensembl. Peaks for which –10×log10 using Ribo-Zero rRNA Removal Kits  following the manufacturer’s instructions. RNA libraries were constructed by using rRNA-depleted RNAs with a TruSeq Stranded Total RNA Library Prep Kit  according to the manufacturer’s instructions. Libraries were subjected to quality control and quantified using a BioAnalyzer 2100 system . RNA was purified from each sample using Oligo(dT) Dynabeads (Invitrogen) and subjected to first-strand cDNA synthesis and library preparation using a TruSeq Stranded mRNA Library Prep Kit (Illumina). Libraries (10 pM) were denatured as single-stranded DNA molecules, captured on Illumina flow cells, amplified in situ as clusters, and finally sequenced for 150 cycles on an Illumina HiSeq 4000 instrument according to the manufacturer’s instructions.Quality control of paired-end reads was performed with FastQC (v0.11.9) prior to trimming of 3’ adaptors and removal of low-quality reads using Cutadapt software (v1.9.3). The high-quality reads were aligned to the mouse reference genome (mm10) with HISAT2 software (v2.0.4). Then, guided by the Ensembl (GRCm39.104) GTF gene annotation file, expression was estimated in units of fragments per kilobase of transcript per million mapped reads (FPKMs). The differentially expressed genes were identified as those with a fold change of ≥2 and adjusted p value of ≤0.05.p-value ≤0.05.miRNA-seq was conducted at Cloudseq Biotech Inc . In brief, total RNA from each group was prepared and quantified with the BioAnalyzer 2100 system . Polyacrylamide gel electrophoresis (PAGE) was performed, and the gel was cut to select the band corresponding to a length of 18–30 nt to recover small RNAs. Adaptors were then ligated to the 5’ and 3’ ends of the small RNAs. After cDNA synthesis and amplification, the PCR-amplified fragments were purified from the PAGE gel, and the complete cDNA libraries were quantified with a BioAnalyzer 2100. Sequencing was performed on the Illumina HiSeq 4000 instrument, and 50 bp single-end reads were generated. For miRNA-seq data analysis, the adaptor sequences were trimmed, and the trimmed reads (≥15 nt) were retained by Cutadapt software (v1.9.3). Then, the trimmed reads were aligned to the merged mouse pre-miRNA databases (known pre-miRNAs from miRbase [v22.1]) using NovoAlign software (v3.02.12). The number of mature miRNA-mapped tags was defined as the raw expression level of that miRNA. Read counts were normalized by tag counts per million aligned miRNAs (TPM) values. Differentially expressed miRNAs between the two groups were filtered by the following criteria: fold change ≥2 and adjusted p value for a GO term denotes the significance of the enrichment of genes in that term. Pathway enrichment analysis is a functional analysis that maps genes to KEGG pathways. The Fisher p value denotes the significance of the pathway correlation to the conditions. GO analysis was performed using enrichGO function in the R package ‘clusterProfiler’ with the following parameters: pvalueCutoff = 0.005, qvalueCutoff = 0.005, minGSSize = 2, and maxGSSize = 500. KEGG analysis was performed using enrichKEGG function in ‘clusterProfiler’ with the following parameters: pvalueCutoff = 0.05 and qvalueCutoff = 0.05. The GO and KEGG pathway terms with adjusted p values of≤0.05 were considered significantly enriched.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses based on the differentially expressed genes were performed with the R package ‘clusterProfiler’ (v4.4.0) . GO covehttps://starbase.sysu.edu.cn/). We predicted the lncRNA–RBP interaction network using the RBP-Target module in ENCORI with the following parameters: Clade = mammal, Genome = mouse, CLIP Data = medium stringency ≥ 2. We then integrated the lncRNA–RBP interacting pairs using R tidyverse package (v 1.3.2). Network visualization was done using Cytoscape vs 3.9.1 (http://www.cytoscape.org/). The shape and colour of the points are customizable.Based on differentially methylated lncRNA profile , we predhttp://www.ribocirc.com/index.html). For the circRNA structure prediction, after submitting the query, the site provides several pieces of information, including ribosome associated evidence, m6A evidence, and ORF evidence. We redrew the RNA structure from the website using Adobe Illustrator CC 2017 and added additional information. For the protein structure prediction, the I-TASSER Suite was used  to analyse the binding of lncRNAs and circRNAs to the differential miRNAs. After obtaining the lncRNA‒miRNA and circRNA–miRNA interaction networks, we intersected the predicted lncRNAs and circRNAs with the differentially expressed lncRNAs and circRNAs and the differentially methylated lncRNAs and circRNAs in our data. Finally, we integrated all data to construct the expression and methylated circRNA/lncRNA‒miRNA‒mRNA interaction networks. Network visualization was done using Cytoscape vs 3.9.1 (http://www.cytoscape.org/). The shape and colour of the points are customizable.Based on the miRNA expression data , we usedMir129, Mir101b, Mir27, and Mir16 mimics were designed and synthesized by GeneChem. We used Lipofectamine 3000 to cotransfect reporter plasmids, the Trmt6 plasmid, and miRNA mimics into HEK293T cells. Luciferase activity was measured 48 hr later according to the manufacturer’s procedures . All experiments described were replicated independently with similar results at least three times.The Rbfox3, Arid1a, Grin2d, and Wdtc1 reporter vectors were constructed by inserting the 3’UTRs of the corresponding genes downstream of the luciferase reporter gene into the GV272 plasmid . The Trmt6 overexpression vector was constructed by subcloning the coding sequence (CDS) into the GV712 plasmid . The 1A modification patterns in our nine differently treated samples  in R (v4.1.3).NMF is a widely used tool for the analysis of high-dimensional data because it automatically extracts sparse and meaningful features from a set of nonnegative data vectors. NMF clustering was used to determine the m samples . We perf1A patterns. Submit the genes contained in each of the three patterns to the ‘Enrichr’ database (http://amp.pharm.mssm.edu/Enrichr/) and select the ChEA 2022 database to view the results of the enriched TFs. To predict the activity of TFs in each pattern, we used the R package ‘decoupleR’ (v 2.5.0). The data containing signed tTF - target gene interactions were harvested from the R package ‘decoupleR’ (v 1.10.0). We employed the run_wmean function to calculate the TF activity with the following parameters: mor=‘mor’, times = 1000, and minsize = 5. Network visualization was done using Cytoscape vs 3.9.1. The shape and colour of the points are customizable.TF analysis was employed to determine the upstream regulation of three m1A patterns by calculating the GSVA score. Genes for each pathway were obtained from the corresponding GO database (http://geneontology.org/). The gsva function was used to calculate the pathway score. Then we used the corr.test function from R package ‘psych’ (v 2.2.9) calculated the correlation between the m1A_regulators and pathway score.The R package ‘GSVA’ (v 1.46.0) was exploited to quantify the pathway activity in three m This study presents a rather valuable finding on the exploration of RNAs m1A modification in normal and OGD/R-treated neurons and the effects of m1A on diverse RNAs. The evidence supporting the claims of the authors is quite solid. The work will be of interest to scientists working in the field of m1A modifications. Noteworthy, this manuscript provides a new avenue for the development of novel therapeutics against ODG/R-related disease. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Caigang Liu as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "The landscape of m1A modification and its posttranscriptional regulatory functions in primary neurons" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The authors should provide all the technique details for the bioinformatics analysis presented in the manuscript.2) The authors should conduct in-depth literature search about the m1A regulators and perform gene expression profile analyses to identify differentially expressed regulators.3) The authors should explore the role of m1A regulators and, in particular, the association with the three m1A modification patterns.4) In Figure 6, the authors presented three patterns regulate m1A modification in neurons. The authors should further analyze the transcription factors to clarify the upstream regulation of these three patterns.Reviewer #1 (Recommendations for the authors):Some specific points for the manuscript are as follows,1. The authors should provide all the technique details for the bioinformatics analysis presented in the manuscript. This is important because the reader should be able to reproduce all the results following their technique protocols. For instance, Figure 2D-E-F, Figure 3E-F, Figure 4E-F, Figure 5A-B-C-D-E and etc. This is mandatory.2. Technique details for the predicted polypeptide structures in Figure 4H and Figure S4H should also be provided.3. The authors claimed the identification of three patterns of m1A modifications. What are the underlying functional links of these patterns with neurons fate? The authors should discuss this in length in the manuscript.4. Have the authors examined the m1A modifications of tRNA in neurons and OGD/R-treated neurons?5. Have the authors examined the topology of the m1A modification in normal and OGD/R-treated conditions or on diverse RNAs? This should be discussed.6. In page 3, line 46, the authors claimed that the present work provide "a theoretical basis for treating and developing drugs for OGD/R pathology-related diseases". The authors should elaborate this point in the Discussion section.7. Language inconsistences should be fixed throughout the manuscript. This is mandatory.Reviewer #2 (Recommendations for the authors):These findings deepened the understanding of m1A modification in neuron and provided new insights into the pathogenic mechanism and pathological process of OGD/R. In general, this study is innovative, practical and well designed. Despite the interesting results obtained, there are some concerns and criticisms with their observations/conclusions.1. Despite the authors' comprehensive exploration of m1A modifications in neuron, they seem to have overlooked an important aspect: are m1A regulators differentially expressed in normal neurons and OGD/R neurons? The currently known m1A regulators include: Trmt6, Trmt61A, Alkbh1, Alkbh3, Fto, Ythdf1, Ythdf2 and Ythdc1. The authors could also do more in-depth literature search about the m1A regulators and performed gene expression profile analyses to identify differentially expressed regulators.2. Considering the research frontier, the biological role of m1A methylation modification should be described in detail in the Introduction section. M1A modification has been found to play a critical role in various biological conditions and diseases. Recent studies have also shown that m1A methylation regulates glycolysis of cancer cells through modulating ATP5D  and also participate in the modulating of macrophage polarization which promotes aortic inflammation , authors should review the latest literature on m1A modification and describe the role and significance of m1A modification in depth.3. In Figure 6, the authors presented three patterns regulate m1A modification in neurons and the functions of these three patterns are identified. I consider this result to be of great interest. I suggest that the authors could further analyze the transcription factors to clarify the upstream regulation of these three patterns. Transcription factor analysis can tell us at least two things: 1. whether there is specific transcription factor expression in different patterns; 2. whether there is a regulatory network of transcription factors in different patterns.Reviewer #3 (Recommendations for the authors):Overall, this is an interesting and well performed study that described a comprehensive landscape of m1A modification in primary neuron and investigated the role of m1A in the circRNA/lncRNA‒miRNA-mRNA regulatory network following OGD/R. The focus on the two different complex regulatory networks for differential expression and differential methylation is important and it will be a valuable resource for the research community that focuses on epitranscriptomics and central nerve system diseases. Collectively, the authors present an exciting piece of work that certainly adds to the literature regarding epitranscriptomic features in neuron. While interesting results obtained and the paper is nicely written, I have several comments the authors might want to consider to improve the overall strength of their manuscript.1. In this manuscript, a meaningful conclusion is that the m1A modification on mRNA 3'UTR hinders the binding of miRNAs . I think this provides a good idea to study the possible roles of m1A modification in a variety of diseases. I would like to know what the authors envision for the continuation of research in this area.2. The authors present three patterns  of m1A modifications in the last part of RESULTS , which is an intriguing finding. However, it does not seem to explore the specific details of these three modification patterns further. I suggest that the authors could further analyze whether the genes in these patterns are regulated by some common transcription factors.3. The m1A modification mainly depends on the dynamic regulation of RNA methyltransferases (writers), demethylases (erasers) and m1A-binding proteins (readers). However, in this manuscript the authors do not mention the changes in m1A regulators. I am curious about whether m1A regulators will be differentially expressed after OGD/R and suggest the authors to add this part of the analysis. Of course, if differentially expressed m1A regulators exist, the authors could further explore the role of these factors and, in particular, the association with the three m1A modification patterns.4. The authors obtained interesting results at the two time points set in the OGD/R model, they should try to explain the possible effects of such changes on neuronal biological processes in Discussion.5. Figure 3D and Figure 4D: The title of the Y-axis might be mislabeled.6. Figure 4 and Figure 6 has an additional caption at the bottom of the image, please double check.7. P28 Line547 and P30 L575: The abbreviations of reference genome are inconsistent. Essential revisions:1) The authors should provide all the technique details for the bioinformatics analysis presented in the manuscript.Special thanks to Editors for the pertinent suggestions. We have elaborated on the technique details for the bioinformatics analysis in the Materials and methods section.2) The authors should conduct in-depth literature search about the m1A regulators and perform gene expression profile analyses to identify differentially expressed regulators.This is an excellent point that is brought forth by the Editors and Reviewers. we have performed an additional analysis to identify the differentially expression of m1A regulators in OGD/R-treated neurons. The results showed that the expression of Alkbh3, Trmt10c, Trmt61a, Ythdf2, and Ythdf3 were statistically significant . However, the trends of these m1A regulators were different under different OGD/R treatments. This new data analysis now appears in Figure 1F of the revised manuscript.3) The authors should explore the role of m1A regulators and, in particular, the association with the three m1A modification patterns.We appreciate the insightful suggestions and agree with the reviewer. To explore the relationship between m1A regulators and three m1A modification patterns, we used GSVA for scoring the major pathways in the three patterns and subsequently calculated the correlation between these m1A regulators expression and the 3 pattern pathway scores. Our results suggested that most of m1A regulators were positively correlated with metabolism-associated functions and autophagy-associated functions, and negatively correlated with catabolism-associated functions . We have added this part of content in the Figure 6—figure supplement 1 of the revised manuscript.4) In Figure 6, the authors presented three patterns regulate m1A modification in neurons. The authors should further analyze the transcription factors to clarify the upstream regulation of these three patterns.We thank the reviewer for this helpful comment. To clarify the transcription factor regulation of different patterns, we have performed new analyses to identify activity of transcriptional factors in those three patterns. We found that the representative transcription factors enriched specifically in cluster. Further prediction of the activity of these transcription factors showed that different transcription factor activation and repression patterns exist in different clusters. In MAC and CAC, most of the transcription factors had increased inferred activity. However, most of the transcription factors had decreased inferred activity in AAC . Based on these transcription factors with altered activity, we constructed a TF-target gene regulatory network . This new data analysis now appears in Figure 6 of the revised manuscript.Reviewer #1 (Recommendations for the authors):Some specific points for the manuscript are as follows,1. The authors should provide all the technique details for the bioinformatics analysis presented in the manuscript. This is important because the reader should be able to reproduce all the results following their technique protocols. For instance, Figure 2D-E-F, Figure 3E-F, Figure 4E-F, Figure 5A-B-C-D-E and etc. This is mandatory.Special thanks to you for your suggestions. We apologize for not presenting technique details clearly. Relevant detail protocols have added to the Materials and methods section.2. Technique details for the predicted polypeptide structures in Figure 4H and Figure S4H should also be provided.We agree with the reviewer and the protocols about how to predicted polypeptide structures have added to the Materials and methods section (circRNA structure prediction and protein structure prediction of circRNA-encoded peptides).3. The authors claimed the identification of three patterns of m1A modifications. What are the underlying functional links of these patterns with neurons fate? The authors should discuss this in length in the manuscript.Thank you very much for the constructive comments. After reviewing the literature, we meticulously discussed the relationship between these three patterns and neurons fate in the Discussion section.4. Have the authors examined the m1A modifications of tRNA in neurons and OGD/R-treated neurons?We appreciate the suggestion. We did not explore m1A modifications on tRNA in this manuscript. Here we just focused on three RNA types, mRNA, lncRNA, and circRNA. tRNA was beyond the scope of this study. However, this comment suggests a good direction for our future studies.5. Have the authors examined the topology of the m1A modification in normal and OGD/R-treated conditions or on diverse RNAs? This should be discussed.We are very sorry that the topology of the m1A modification was not examined in this manuscript. The main purpose of this manuscript is to construct a comprehensive m1A modification atlas in normal neurons and OGD/R-treated neurons. The m1A topology is achieved by two enzyme systems: m1A methyltransferase and m1A demethylases. We added the expression analysis of these enzymes in the in Figure 1F of the revised manuscript. In-depth study of m1A topology is an important direction for our future research.6. In page 3, line 46, the authors claimed that the present work provide "a theoretical basis for treating and developing drugs for OGD/R pathology-related diseases". The authors should elaborate this point in the Discussion section.We thank the Reviewer for this advice. Clarification of the mechanism of m1A modification in OGD/R may provide some reference for the treatment of this pathological process. We provide an additional description in the Discussion section:“With the deepening of the RNA epitranscriptomic research studies, some m6A regulators have been selected as targets for the development of corresponding drugs, especially within the field of oncology. Nucleoside and non- nucleoside METTL3 inhibitors were designed to inhibit the function of METTL3 in different kinds of tumors and proved METTL3 was an efficient therapeutic target . METTL14 also plays an important role in a variety of tumors. Designing activators and inhibitors of METTL14 is also an important direction for drug development . In addition, m6A-modified noncoding RNAs are potential therapeutic targets . In our current research, we have not only identified differentially expressed m1A regulators  in OGD/R, but also constructed noncoding RNA regulatory networks for differential expression and differential methylation under OGDR treatment, and identified a number of noncoding RNAs that may play important roles, all of which may be targets for mitigating the effects of OGD/R.”7. Language inconsistences should be fixed throughout the manuscript. This is mandatory.We apologize for the inconsistent descriptions. We have read through carefully and changed the inconsistent contents. we have discussed the writing problems with a professional English supervisor, he gave us several suggestions, which have all been adopted and accordingly adjusted in the manuscript.Reviewer #2 (Recommendations for the authors):These findings deepened the understanding of m1A modification in neuron and provided new insights into the pathogenic mechanism and pathological process of OGD/R. In general, this study is innovative, practical and well designed. Despite the interesting results obtained, there are some concerns and criticisms with their observations/conclusions.1. Despite the authors' comprehensive exploration of m1A modifications in neuron, they seem to have overlooked an important aspect: are m1A regulators differentially expressed in normal neurons and OGD/R neurons? The currently known m1A regulators include: Trmt6, Trmt61A, Alkbh1, Alkbh3, Fto, Ythdf1, Ythdf2 and Ythdc1. The authors could also do more in-depth literature search about the m1A regulators and performed gene expression profile analyses to identify differentially expressed regulators.We thank the reviewer for their positive evaluation of our work and helpful comments. m1A regulators were carefully searched in published literatures. Subsequently, gene expression data of m1A regulators were extracted from the whole gene expression profile. We compared the differences in expression of those m1A regulators between normal neuron and OGD/R-treated neuron. The results showed that the expression of Alkbh3, Trmt10c, Trmt61a, Ythdf2, and Ythdf3 were statistically different . However, the trends of these m1A regulators were different under different OGD/R treatments. This new data analysis now appears in Figure 1F of the revised manuscript.2. Considering the research frontier, the biological role of m1A methylation modification should be described in detail in the Introduction section. M1A modification has been found to play a critical role in various biological conditions and diseases. Recent studies have also shown that m1A methylation regulates glycolysis of cancer cells through modulating ATP5D  and also participate in the modulating of macrophage polarization which promotes aortic inflammation , authors should review the latest literature on m1A modification and describe the role and significance of m1A modification in depth.Thank you very much for the constructive suggestions and comments. We have carefully searched latest literatures about m1A modification and the revised section about the biological role of m1A methylation modification has added to the Introduction:“Recent studies have revealed that m1A modification involved in various biological functions. Wu et al. found that m1A demethylase ALKBH3 regulated glycolysis of cancer cells and further affected tumor growth and cancer progression . Wu et al. revealed that m1A regulation is significantly associated with the pathogenesis of human Abdominal Aortic Aneurysm . However, the roles of m1A modification in neurons remain largely unknown.”3. In Figure 6, the authors presented three patterns regulate m1A modification in neurons and the functions of these three patterns are identified. I consider this result to be of great interest. I suggest that the authors could further analyze the transcription factors to clarify the upstream regulation of these three patterns. Transcription factor analysis can tell us at least two things: 1. whether there is specific transcription factor expression in different patterns; 2. whether there is a regulatory network of transcription factors in different patterns.Thank you for your suggestions. Transcription factor analysis does provide further insight into the characteristics of the three m1A patterns we identified. We extracted the genes in each pattern and predicted their transcription factor expression using the R package “decoupleR” (v 2.5.0). This new data analysis now appears in Figure 6 of the revised manuscript:“We found that the representative transcription factors enriched specifically in cluster. Further prediction of the activity of these transcription factors showed that different transcription factor activation and repression patterns exist in different clusters. In MAC and CAC, most of the transcription factors had increased inferred activity. However, most of the transcription factors had decreased inferred activity in AAC . Based on these transcription factors with altered activity, we constructed a TF-target gene regulatory network .”Reviewer #3 (Recommendations for the authors):Overall, this is an interesting and well performed study that described a comprehensive landscape of m1A modification in primary neuron and investigated the role of m1A in the circRNA/lncRNA‒miRNA-mRNA regulatory network following OGD/R. The focus on the two different complex regulatory networks for differential expression and differential methylation is important and it will be a valuable resource for the research community that focuses on epitranscriptomics and central nerve system diseases. Collectively, the authors present an exciting piece of work that certainly adds to the literature regarding epitranscriptomic features in neuron. While interesting results obtained and the paper is nicely written, I have several comments the authors might want to consider to improve the overall strength of their manuscript.1. In this manuscript, a meaningful conclusion is that the m1A modification on mRNA 3'UTR hinders the binding of miRNAs . I think this provides a good idea to study the possible roles of m1A modification in a variety of diseases. I would like to know what the authors envision for the continuation of research in this area.Thank you for your interest in our research. To our knowledge, the m1A modifications are similar to m6A modifications and might also be cell-type-specific, so in the future we may focus on m1A modifications in other cell types of the nervous system. In addition, we will also focus on the whole process of m1A modification occurrence given that the regulatory factors of m1A are currently not studied in depth.2. The authors present three patterns  of m1A modifications in the last part of RESULTS , which is an intriguing finding. However, it does not seem to explore the specific details of these three modification patterns further. I suggest that the authors could further analyze whether the genes in these patterns are regulated by some common transcription factors.Thank you very much for the constructive suggestions. Transcription factor analysis can help us explore the characteristics of these three patterns in greater depth. We analyzed the transcription factor expression in these three patterns and added the results to Figure 6 and the corresponding Results section.3. The m1A modification mainly depends on the dynamic regulation of RNA methyltransferases (writers), demethylases (erasers) and m1A-binding proteins (readers). However, in this manuscript the authors do not mention the changes in m1A regulators. I am curious about whether m1A regulators will be differentially expressed after OGD/R and suggest the authors to add this part of the analysis. Of course, if differentially expressed m1A regulators exist, the authors could further explore the role of these factors and, in particular, the association with the three m1A modification patterns.We totally agree with your argumentation. This would help understand the changes of m1A modification in normal neurons and OGD/R-treated neurons. The results showed that the expression of Alkbh3, Trmt10c, Trmt61a, Ythdf2, and Ythdf3 were statistically different . We have added the gene expression analysis of m1A regulators in the revised manuscript.4. The authors obtained interesting results at the two time points set in the OGD/R model, they should try to explain the possible effects of such changes on neuronal biological processes in Discussion.Thank you for the suggestion. In the Discussion section, we add our understanding of the m1A modifications at different time points:Drosophila development and mouse cerebellum development , the level of m6A modification changes dynamically with the developmental process, which is associated with various organogenesis and specific functions. In Alzheimer's disease, m6A modification levels also change as the disease progresses . However, whether the m1A modification is time-specific is still unknown. In the present study, we focused on the changes of m1A modification under different OGD/R treatments. Our results suggested that m1A regulators differentially expressed and mRNA and noncoding RNA has its own features in different OGD/R treatments. We suggested that this is partly indicative of the time-specificity of m1A modifications under different OGDR treatments, but more investigations are needed to determine whether m1A modifications are time-specific during development and in other disease progressions.”“It has been shown that m6A modifications are time-specific in developmental processes as well as in the development of some diseases. During both 5. Figure 3D and Figure 4D: The title of the Y-axis might be mislabeled.We apologize for our error in these figures, this has now been corrected.6. Figure 4 and Figure 6 has an additional caption at the bottom of the image, please double check.Thank you for your careful checks. Additional captions have been removed from the Figure 4 and Figure 6.7. P28 Line547 and P30 L575: The abbreviations of reference genome are inconsistent.We are sorry for the consistency about the version of reference genome. It has been corrected in the revised manuscript:“The high-quality reads were aligned to the mouse reference genome (mm10).”