Across biological systems, a number of genomic processes, including transcription, replication, DNA repair, and transcription factor binding, display intrinsic directionalities. These directionalities are reflected in the asymmetric distribution of nucleotides, motifs, genes, transposon integration sites, and other functional elements across the two complementary strands. Strand asymmetries, including GC skews and mutational biases, have shaped the nucleotide composition of diverse organisms. The investigation of strand asymmetries often serves as a method to understand underlying biological mechanisms, including protein binding preferences, transcription factor interactions, retrotransposition, DNA damage and repair preferences, transcription-replication collisions, and mutagenesis mechanisms. Research into this subject also enables the identification of functional genomic sites, such as replication origins and transcription start sites. Improvements in our ability to detect and quantify DNA strand asymmetries will provide insights into diverse functionalities of the genome, the contribution of different mutational mechanisms in germline and somatic mutagenesis, and our knowledge of genome instability and evolution, which all have significant clinical implications in human disease, including cancer. In this review, we describe key developments that have been made across the field of genomic strand asymmetries, as well as the discovery of associated mechanisms.  Although there are no clear evolutionary advantages associated with it, the second law has been observed across diverse organisms cis-regulatory logic, in evolution, and in disease. In this review, we provide an overview of multiple biological processes that result in the asymmetric distribution of genomic information and demonstrate the utility of strand asymmetries as a tool to decipher new biological mechanisms.Importantly, when investigating particular genomic localities, there are clear deviations from the second parity rule, which can be attributed to specific functional elements. Biological processes, such as transcription and replication, possess intrinsic directionality, therefore resulting in the heterogeneous distribution of information. Identification of strand asymmetries can therefore enable the detection of biological mechanisms, the identification of novel genomic elements, and the characterization of selective environmental constraints. At the same time, strand asymmetry analyses can improve computational models across biological domains, such as in the estimation of the likelihood of mutagenesis, the identification of driver mutations, in 2In transcription, which is a directional process, the elongating RNA polymerases synthesize nascent RNA complementary to the template strand a. DuringBorrelia burgdorferi, a bacterium that causes Lyme disease, is one of the species with the most pronounced leading / lagging nucleotide asymmetries Transcriptional and replicative strand asymmetries refer to the asymmetric distribution of information such as nucleotides or motifs between the leading and lagging strands or between the template and non-template strands respectively. Both forms of asymmetry have been observed in the genomes of diverse organisms including prokaryotes, eukaryotes, and viruses These asymmetries are frequently quantified with GC-skew and AT-skew, which measure statistical deviations of guanines or adenines between the two strands and which have been used to identify the location of replication origins, elucidate the direction of replication, and even to validate genome assemblies Bacillus subtilis, 75% of genes are transcribed in the same orientation as the direction of replication In both prokaryotes and eukaryotes, a larger number of genes are usually found in the leading orientation Bacillus subtilisBacillus subtilis have a higher rate of point mutations and non-synonymous mutations, indicating that they undergo faster adaptive evolution Further supporting this model, genes that are highly expressed and essential genes, such as ribosomal genes, which would experience more frequent collisions due to a higher density of elongating RNA polymerases, tend to be found on the leading strand In eukaryotic cells, multiple mechanisms are in place to limit collisions. These involve the separation of replication and transcription domains during S-phase Gene expression can be a mechanism that safeguards genome integrity. The testis is the tissue that expresses the highest number of genes in mammals; this results in a reduced mutation rate for the transcribed strand due to transcription-coupled repair, and in turn, leads to reduced population diversity across the expressed genes 3The orientation of genes is often biased, and one extreme case of this is polycistronic gene expression, in which all genes have the same directionality. Prokaryotic operons are polycistronic, while the vast majority of eukaryotic mRNAs are monocistronic. However, it has been noted that polycistronic mRNAs can be rarely found in eukaryotic genomes In addition, gene pairing has been observed to be common across eukaryotic species, with genes being found in different orientations Transcription in eukaryotes is inherently bidirectional, and antisense transcripts can arise from this process Furthermore, key transcription initiation and termination signals, such as the TATA-box and the polyadenylation signal, display not only positional constraints but also intrinsic directionalities Exons and introns display opposite nucleotide strand asymmetries. In introns, Ts are more frequent than As and Gs are more abundant than Cs, a trend that is reversed in exons in both humans and mice 4Throughout our lives, cells in the human body acquire and accumulate somatic mutations. Processes that cause the accumulation of somatic mutations can be divided into exogenous, such as UV light exposure, and endogenous, such as defects in DNA repair and oxidative damage a. TherefStrand asymmetric segregation of DNA lesions was observed in murine liver tumor genomes, resulting in chromosome-scale strand asymmetry of mutations 5Substitution mutations provide valuable information about underlying mutational processes. Previous research has used mutational classification of mutational signatures to further separate the standard 96 substitution classification system using the template and non-template orientation into 192 possible mutation classes DNA damage is preferentially repaired at the template strand of expressed genes through transcription-coupled nucleotide excision repair (TC-NER), which removes transcription-blocking DNA lesions The accumulation of tobacco-related carcinogens at guanines in lung cancer results in the mutational imbalance of G>T site substitutions due to the preferential repair of these adducts at the template strands of expressed genes 6e). Systematic examination of mutational processes has indicated that replicative strand asymmetries are more common than transcriptional strand asymmetries across the mutational signatures examined Replicative strand biases are observed in cancer genomes, with one study showing significant replicative strand asymmetries across fourteen cancer types Fig. 2MSH6, MSH2 and MLH1 resulted in replication strand asymmetry effects in isogenic cell models EXO1 and RNF168 showed specific transcription strand asymmetry effects In humans, leading and lagging strand DNA synthesis is performed primarily by polymerase ε and polymerase δ respectively Mutations associated with APOBEC, a cytidine deaminase with important roles in antiviral defense, cause off-target mutagenesis in the genome, especially at single-stranded DNA sites. There is evidence for episodic APOBEC mutagenesis across multiple cancer types 7Transposable elements, originally discovered by McClintock c); this has implications for both evolution and disease.In the human genome, long interspersed nuclear elements (LINE) and short interspersed nuclear elements (SINE) show significant transcriptional and replicative strand asymmetries, while long terminal repeats (LTRs) exhibit pronounced transcriptional strand asymmetries Fig. 3Similarly, LTRs are more frequently found in the template orientation, and Alu repeats, which are a subset of SINE elements, also show a preference for the template orientation 8The orientation of DNA motifs in the genome impacts diverse biological processes, including gene regulation, through its effect on co-operative transcription factor binding at cis-regulatory elements a. CombinAt short inter-motif distances, the TFBS orientations impact protein-protein interactions (PPIs). In addition, even though the consensus TFBS motif of many TFs is palindromic, providing two templates for binding, there are significant binding biases depending on the orientation when considering flanking nucleotides At the core promoter, a number of motifs are positioned with respect to orientation, distance, and order preferences. For instance, transcription initiation in TATA-box-containing promoters requires the orientation and correct positioning of promoter-related motifs, including the initiation element, the TATA-box, and the upstream and downstream promoter elements, among others With regards to enhancers, studies that have investigated their mechanism of function have led to the proposition of two models, and there is currently evidence to support both of them. The “enhanceosome model” states that the function of the enhancer is dependent on the orientation, positioning, and order of TF binding sites, with changes in them resulting in significant changes in the enhancer’s activity Second, the “billboard model”, which is also referred to as the information display model, proposes a more flexible structure for enhancer grammar in which the combination, orientation, order, and distance of cognate motifs are not fixed, but can instead vary without impacting enhancer function cis-regulation. In a breakthrough study, researchers performed consecutive affinity-purification systematic evolution of ligands by exponential enrichment (CAP-SELEX) experiments, with which they examined 9400 TF–TF–DNA interactions. Interestingly, they were able to show that both the orientation and distance between the TF motifs determined heterodimer formation for a plethora of TF pairs Multiple studies have provided experimental evidence for the effect of orientation and spacing in The transcription factor Yin-Yang can act as an activator or a repressor depending on motif orientation and positioning The positioning of TFBSs within a nucleosome influences transcription factor binding, which can subsequently stabilize or destabilize a nucleosome 9One of the most notable examples has been the CCCTC-binding factor (CTCF), which contributes to the formation of topologically-associating domains (TADs). Enhancer-promoter interactions are constrained within TADs, with the orientation of CTCF sites being important for their formation h. The vaLoop extrusion can increase contact between loci that would typically lie in different sub-compartments Disruption of the loop extrusion mechanism has been associated with cancer due to alterations in enhancer-gene interactions 10In this review, we have highlighted a number of genomic processes that are associated with strand asymmetries and have presented many of the underlying mechanisms that contribute to the asymmetric distribution of genomic features in organismal genomes. Strand asymmetries shape the nucleotide composition of viral, prokaryotic, and eukaryotic genomes and are genomic signatures of the biological processes that shape them. We have also highlighted the contribution of strand asymmetries in gene regulation, splicing, transcription factor binding, and retrotransposition. In addition, we summarize evidence regarding how mutational strand asymmetries reveal insights into DNA damage and repair in human health and disease. We argue that the implementation of sensitive methods to detect strand asymmetries in biological problems will enable breakthroughs in our understanding of genome biology.The directionality of information in the DNA molecule is reflected in the orientation of motifs, genes, and other genomic elements. To conclude, an analogy can be drawn between genomic strand asymmetries and the road code, which dictates the rules by which vehicles have to move around in cities and with traffic signs that give instructions to road users. Similar to that, the orientation of motifs, genes, and other genomic elements in the genome provides instructions on how they should be interpreted.I.G.S. conceived and supervised the study. C.M., A.Z. and I.G.S. wrote the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.


How the diverse neural cell types emerge from multipotent neural progenitor cells during central nervous system development remains poorly understood. Recent scRNA-seq studies have delineated the developmental trajectories of individual neural cell types in many neural systems including the neural retina. Further understanding of the formation of neural cell diversity requires knowledge about how the epigenetic landscape shifts along individual cell lineages and how key transcription factors regulate these changes. In this study, we dissect the changes in the epigenetic landscape during early retinal cell differentiation by scATAC-seq and identify globally the enhancers, enriched motifs, and potential interacting transcription factors underlying the cell state/type specific gene expression in individual lineages. Using CUT&Tag, we further identify the enhancers bound directly by four key transcription factors, Otx2, Atoh7, Pou4f2 and Isl1, including those dependent on Atoh7, and uncover the sequential and combinatorial interactions of these factors with the epigenetic landscape to control gene expression along individual retinal cell lineages such as retinal ganglion cells (RGCs). Our results reveal a general paradigm in which transcription factors collaborate and compete to regulate the emergence of distinct retinal cell types such as RGCs from multipotent retinal progenitor cells (RPCs). How diverse neural cell types emerge from multipotent neural progenitor cells during central nervous system development remains poorly understood. We address this question in the neural retina. The retina functions to receive visual signals, transform them into electrophysiological pulses, and transmit them to the brain. This task is carried out by the various retinal neurons, which form the visual circuitry ,2. All rAtoh7 and Neurog2 for RGCs, Foxn4 for horizontal cells and amacrine cells (H&As), and Otx2 and Neurod1 for photoreceptors (PHCs)  ,22. FurtAtoh7 leads to almost complete RGC loss . Our study reveals globally the enhancers, enriched DNA motifs, and possible interacting transcription factors underlying the progression of cell state/type-specific gene expression along the trajectories of individual lineages, including that of the RGC lineage. Further, we performed CUT&Tag to identify the binding sites, and thereby the associated enhancers and the target genes, of four key transcription factors, Otx2, Atoh7, Pou4f2 and Isl1, in the E14.5 retina. Analysis of the associated enhancers and target genes of Atoh7 and Otx2 supports the idea that transcription factors for different lineages compete and/or collaborate to drive tRPCs toward distinct retinal cell fates. The enhancers bound by Atoh7, Pou4f2 and Isl1 and the associated target genes allow us to uncover how these key transcription factors regulate the epigenetic landscape in a sequential and combinatorial fashion to control gene expression during RGC genesis. Overall, these results provide a comprehensive view of the shifting epigenetic landscape along the developmental trajectories, particularly that of RGCs, in early retinal development, and reveal the roles of key transcription factors in interacting and influencing the epigenetic landscape to promote RGC genesis.Conceivably, transcription factors define individual cell states/types in the different trajectories and promote the progression of different lineages by interacting with and thereby influencing the epigenetic landscape. The epigenetic landscape can be surveyed by examining the distribution of histone markers for specific chromatin states with ChIP-seq, the degrees of chromatin openness by accessibility assays such as Dnase-seq, FAIRE-seq and ATAC-seq, or expression of enhancer RNAs . Using tAtoh7lacZ, zsGreenCreERT2Atoh7 (referred to as zsGreenAtoh7 in the text), FLAGtdTomatoPou4f2 (referred to as tdTomatoPou4f2 in the text), HAAtoh7 and HAPou4f2 alleles have been described previously and were maintained in the C57B/L6 × 129 genetic background  were collected in cold phosphate buffered solution (PBS) after timed mating. They were then dissociated into single cell suspensions following the procedure described before ,53. BriescRNA-seq was performed on 10× Chromium single cell platform using the Chromium Single Cell 3′ Library & Gel Bead Kit v3.1, in keeping with the manufacturer's instructions . The sorted cells were loaded onto the 10× Genomics Chromium Controller targeting >5000 cells per sample to generate scRNA-seq libraries. The quantity and quality of the libraries were evaluated by the Qubit Fluorometer and Agilent Fragment Analyzer. High-quality libraries were subsequently sequenced on a NovaSeq6000 sequencer (PE 26 × 100).2, 0.1% Tween-20, 0.1% Nonidet P40, 0.01% Digitonin, 1% BSA), mixed by gently by pipetting then incubated for 2 min on ice. One milliliter of cold Wash Buffer  was then added immediately and mixed by gentle pipetting. The cells were then spun down at 500 g for 5 min at 4°C, and resuspended in cold 1× Nuclei Buffer . The nuclei concentration and quality were determined with a hemocytometer.Nuclei isolation was performed following the protocol from 10× Genomics (CG000169). Briefly, 100 000–1 000 000 cells were collected by FACS and centrifuged at 300 g for 5 min at 4°C. Cell pellets were resuspended in 100 μl cold Lysis Buffer  using the Chromium Single Cell ATAC Reagent Kits (10× Genomics). The libraries were cleaned using SPRIselect reagent (Beckman Coulter). The quantity and quality of the libraries were evaluated by the Qubit Fluorometer and Agilent Fragment Analyzer. The libraries were subsequently sequenced on a NovaSeq6000 (PE50).Analysis of the scATAC-seq data was performed using the ArchR package . Raw seq2 fold change higher than 1.25 and false discovery rate (FDR) <0.01. Genes with different gene scores between two clusters were similarly identified. We then imputed gene scores by MAGIC (The GeneScore matrix was generated using distance-weighted accessibility models with the 500 bp-bin tile matrix and gene window (100 kb on either side of the gene). To identify the marker genes, gene expression in each cell type was compared with bias-matched background cells using Wilcoxon test with logby MAGIC  for visu2 fold change higher than 0.5 and FDR <0.1. To identify the differentially accessible peaks between wild-type and Atoh7-null cells, we focused on the tRPC clusters and the early RGC clusters and used the method described above.Insertion counts from individual cells in each cluster were aggregated, which were considered pseudo-bulk replicates. Peak calling was performed on the pseudo-bulk replicates using MACS2 , and an scRNA-seq data analysis from FACS enriched E14.5 and E17.5 retinal cells were performed as previously described ,53. UncoWe performed supervised trajectory analysis by ArchR. In our study, we focused on the RGC trajectory which was defined based on the identities of the cell clusters relevant to this lineage, including nRPCs, tRPCs, early RGCs and late RGCs, which provided a trajectory backbone. An RGC trajectory was created based on the average positions of each cell cluster within n-dimension subspace, and individual cells were then aligned to this trajectory by calculating the nearest cell-to-trajectory distance. To identify the top variable genes or peaks along the trajectory, ArchR smoothed the gene or peak matrix using smoothing window, and the heatmaps with genes or peaks were plotted with variance quantile cutoff higher than 0.9.We used the chromVAR pipeline  to obtaik-mer frequency matrix that contains all possible k-mers across ±250 bp at the motif center was created. The normalized footprint profiles were plotted by subtracting the Tn5 bias within ±250 bp around the motif sites.Cells were grouped by clusters to get pseudo-bulk ATAC-seq profiles in order to accurately profile transcription footprints. The footprints were then computed for selected motifs. To account for the Tn5 insertion bias, a HA/HAAtoh7 and HA/HAPou4f2  embryos . The other antibodies included Anti-Otx2  and Anti-Isl1 . Control normal rabbit IgG was from Santa Cruz (SC-2027). E14.5 retinas were harvested, dissociated, and cells were collected as described above. For each experiment, nuclei were isolated from 1 × 105 cells by incubating them in the NE Buffer  for 10 min on ice. Concanavalin A-coated magnetic beads  were activated in cold bead activation buffer . The nuclei were bound to the activated beads by incubating at room temperature for 10 min. The nuclei-beads were then resuspended in 50 μl cold Antibody Buffer . 0.5 μg primary antibody or control IgG was then added to each sample and incubated on a nutator at 4ºC overnight. The beads were then isolated by a magnet, the Antibody Buffer was removed, and the beads were resuspended in 50 μl cold Digitonin Buffer . Then 0.5 μg anti-rabbit IgG secondary antibody  was added to each sample and incubated on a nutator for 30 min at room temperature. Samples were washed two times in cold Digitonin Buffer and resuspended in 50 μl cold Digitonin 300 Buffer . Then 2.5 μl CUTANA pAG-Tn5  was added and incubated on a nutator for 1 h at room temperature. Samples were then washed twice in cold Digitonin 300 Buffer and resuspended in 50 μl cold Tagmentation Buffer , and incubated on a nutator for 1 h at 37 ºC. After incubation, the supernatant was removed and the sample beads were resuspended in 50 μl TAPS Buffer  to stop the tagmentation reaction. After removing TAPS Buffer, 5 μl SDS Release Buffer  was added and incubated at 58 ºC for 1 h. Fifteen microliters of SDS Quench Buffer (0.67% Triton-X 100) was then added. CUT&Tag libraries were then generated by PCR amplification. Two microliters of uniquely barcoded i5 and i7 primers (10 μM) and 25 μl CUTANA High Fidelity 2× PCR Master Mix  were added to each sample and mixed. PCR was performed using the following conditions: 72ºC for 5 min, 98ºC for 30 s, and then twenty cycles of 98ºC for 10 s, 63 ºC for 10 s, followed by an extra 1 min extension at 72ºC. The PCR product was cleaned up using AMPure XP beads  following the manufacturer's instructions. DNA was eluted into 15 μl water and quantified using the Qubit fluorometer . Library fragments were analyzed on an Agilent Fragment Analyzer and then sequenced on an Illumina NovaSeq 6000 sequencer (PE50).The Cleavage Under Targets & Tagmentation (CUT&Tag) experiments  were perTo analyze the CUT&Tag data, sequence reads were aligned to the mm10 mouse reference genome using bwa-mem . Peaks wIntersection analysis of scATAC-seq peaks and CUT&Tag peaks and the resulting heatmaps were created with Deeptools/2.3.6 . HeatmapER1Pou4f2, ER2Pou4f2, ER3Pou4f2 and ER2/3Pou4f2. The target sequences of the crRNAs, the genotyping primers, and the genomic coordinates of the deleted regions can be found in the Supplementary Materials.Two or four crRNAs targeting the flanking sequences of the enhancer region to be deleted were designed using CRISPOR  in combiIn situ hybridization was performed using RNAscope double Z probes (Advanced Cell Diagnostics) on paraffin-embedded retinal sections. After timed mating, embryos of desired stages were collected, fixed with 4% paraformaldehyde, embedded in paraffin, sectioned at 6 μm, and de-waxed with methanol ,37,39,62zsGreenAtoh7  and tdTomatoPou4f2 , as Atoh7 marks all tRPCs , zsGreen-expressing cells from E14.5 and E17.5 zsGreen/+Atoh7 retinas , zsGreen-expressing cells from E14.5 and E17.5 zsGreen/laczAtoh7 (Atoh7-null) retinas (enriched with Atoh7-null tRPCs and their progenies), and tdTomato-expressing cells from E14.5 and E17.5 tdTomato/+Pou4f2 cells (enriched with wild-type RGCs). The stability of the fluorescent proteins and the low gating threshold we purposely applied , transitional RPCs (tRPCs), RGC precursors (early RGCs) and more differentiated RGCs (late RGCs), and each state is characterized by the expression of unique marker genes . Conside allele) . In mous allele)  53). Du. DuAtoh7ll tRPCs ,53. In t applied  in FACS  applied ,63 , and Crx and Neurod4 or photoreceptors (PHCs),  zsGreen positive cells from zsGreen/+Atoh7 retinas contributed to clusters representing all retinal cell types born at this stage, including RGCs (early and late), H&As and PHCs (cones), but Atoh7-null (MT) zsGreen positive cells from zsGreen/laczAtoh7 retinas contributed very few cells to the late RGC cluster, since RGCs die in the Atoh7-null retina  retinas existed mainly at this cell state. Similar but less obvious sample-specific contributions to the different clusters were observed in the E17.5 samples; noticeably, Atoh7-null cells did not contribute to the RGC cluster  early RGCs to wild-type early RGCs  contained two regions with distinct activity dynamics. The more distal part was active in nRPCs already, reached peak activity in tRGCs, but tapered off markedly in differentiated neurons including RGCs. The proximal part, on the other hand, was inactive in nRPCs, became most active in tRPCs, but remained active in many of the differentiated neurons. These enhancers may play different roles in the different phases of Atoh7 activation and function collaboratively and sequentially to ensure normal expression. For Otx2, the P2G analysis recognized a set of enhancers that were predominantly active in tRPCs and/or PHCs  (Onecut1 (Pcna and the Mcm genes (Mcm2-10), although constitutively accessible enhancers were often present  . For gen or PHCs (e.g. Neruod4)  RGCs  likely regulating the expression of n Figure . The dynn Figure . Thus, ts Figure .Pou4f2 were identified, and they were also located over a large genomic region (∼350 kb). Many of these enhancers were located within or beyond the neighboring gene Ttc29 on the 3′ side of Pou4f2. These enhancers likely mediated the expression of Pou4f2, instead of Ttc29, since their activity dynamics closely mirrored that of Pou4f2 expression , with or without Otx2 and/or Pou4f2 binding, those with just Pou4f2 binding but little Atoh7 binding , and those not bound by any of these transcription factors . No substantial binding for Isl1 was observed in these enhancers. Like the Isl1 enhancers, the Pou4f2 enhancers bound by Atoh7  were most active in tRPCs and/or early RGCs, whereas those bound by just Pou4f2  were most active in early and/or late RGCs. Again, the activities of many of these enhancers including ER2, 4, 7, 8, 9, 10 and 11, irrespective of Atoh7 binding, were markedly reduced in the early Atoh7-null RGCs  likely regulating n Figure , and Ttc retinas . The bins Figure .Isl1 and Pou4f2 suggested that the two genes are activated through similar sequential functions of Atoh7 and Pou4f2/ Isl1, with Atoh7 acting first by competing with Otx2 and binding to select enhancers associated with the two genes, leading to their initial activation. Once expressed, Pou4f2, Isl1, and likely other transcription factors then bind to distinct but overlapping sets of enhancers to maintain their expression. The contributions of the individual candidate enhancers to Pou4f2 and Isl1 expression could be tested experimentally. As a first step, we deleted three Pou4f2 enhancer regions (ER1-3) by CRISPR/cas9 and examined the effects on Pou4f2 expression by RNA-scope in situ hybridization at E12.5, E14.5 and E17.5  did not lead to appreciable changes in Pou4f2 expression at any stage  based on the differential accessibility/activity of enhancers across different cell states/types. The clustering results are highly consistent with those obtained with scRNA-seq and reveal the same developmental trajectories of the different lineages in early retinal development ,21,22, iAlong individual trajectories from nRPCs to tRPCs, and then to fate-specified neurons, cell state/type-specific enhancers become activated or inactivated, resulting in corresponding changes in gene expression. Thus, putative enhancer elements regulating cell state/type specific expression of individual genes can be identified based on the correlation of the gene expression and enhancer activity dynamics. The epigenetic landscape and thereby the correlating gene expression are determined by transcription factors expressed in individual cell states/types. Supporting this, we found that DNA motifs for many transcription factors known to function in the developing retina are enriched, and corresponding footprints are present, in enhancers active in distinct but often consecutive cell states along individual trajectories. Transcription factors functioning at one state likely also drive the cells to the next state, as many enhancers often are activated already at low levels in the state before the gene is expressed. Our findings are consistent with a couple of recent reports ,100 and Neurod1 and Neurog2, whereas Ptf1a represses Atoh7  in the absence of Atoh7, Atoh7 may be just one of multiple transcription factors interacting with them. On the other hand, Atoh7 is required for the full activities of many enhancers associated with genes critical for RGC differentiation and function. Atoh7 likely exerts its effects on the epigenetic landscape both directly and indirectly, as not all Atoh7-dependent enhancers are bound by Atoh7. Enhancers dependent on Atoh7 but not bound by it likely are activated by downstream transcription factors such as Pou4f2 and Isl1. The activity dynamics of these enhancers along the RGC lineage trajectory is consistent with the differential binding of Atoh7 and Pou4f2/Isl1 to them as well as their stage-specific functions; Atoh7 tends to bind to enhancers active in tRPCs and early RGCs (RGC precursors) and thus are involved in the initial activation of the associated genes when the RGC fate is specified, whereas Pou4f2 and Isl1 tend to bind to enhancers most active in early and late RGCs and likely are involved in activating and maintaining genes expressed in fate-specified RGCs. Thus, Atoh7 and key downstream transcription factors (Atoh7-null retina in an RGC-specific fashion, further support this idea.Nevertheless, our study already offers insights into how RGC-specific gene expression is achieved and the RGC fate is specified. Comparison of the wild-type and  factors , includi factors . We prev factors ,32,33. OPour4f2 and Isl1 activation coincides with the emergence of the RGC lineage. Pou4f2 and Isl1 are also critical for activating and maintaining the RGC-specific gene expression program and stabilizing the lineage (Pour4f2 and Isl1 are regulated is critical for understanding the establishment of RGC fate. We have identified multiple putative enhancers for each gene with distinct dynamics of activity and transcription factor binding profiles, which allow us to infer the regulatory mechanisms by which they are activated and maintained. Consistent with the two genes overlapping completely in developing RGCs, their regulation seems to follow very similar scenarios. The binding of Atoh7 to select enhancers in Pou4f2 and Isl1 likely is the first step toward the RGC fate. Atoh7 must overcome the repression of Otx2, and likely other non-RGC lineage-specific factors, to activate Pou4f2 and Isl1 by directly repressing Otx2 and these other factors and by competing with them directly at select Pou4f2 and Isl1 enhancers (Figure Atoh7 is turned off (Isl1 and Pou4f2 expression likely also requires other transcription factors, since some RGC-specific enhancers are not bound by either Pou4f2 or Isl1. Of note is that for both Pou4f2 and Isl1, their enhancers scatter over large genomic regions, and individual enhancers likely only contribute to specific aspects of the final expression outcome. Similar findings have been reported for enhancers regulating motor neuron-specific genes (Pou4f2, will shed light on how these two genes are activated and how the RGC fate is specified, although redundancies among different enhancers may pose a challenge. Future efforts should focus on understanding how inputs from multiple transcription factors are integrated via these enhancers both quantitatively and quantitatively to achieve the eventual expression outputs.As the earliest genes specifically activated in the RGC lineage,  lineage ,37,39. Ts Figure . As discrned off . The maiic genes . ExperimThe E14.5 and E17.5 scRNA-seq data, the E14.5 and E17.5 scATAC-seq data, and the CUT&Tag data have all been deposited into the NCBI Gene Expression Omnibus with an accession number GSE220587. The data are available to the public without restrictions.gkad026_Supplemental_FilesClick here for additional data file.


Drosophila but providing for greater plasticity.Development of multicellular animals requires epigenetic repression by Polycomb group proteins. The latter assemble in multi-subunit complexes, of which two kinds, Polycomb Repressive Complex 1 (PRC1) and Polycomb Repressive Complex 2 (PRC2), act together to repress key developmental genes. How PRC1 and PRC2 recognize specific genes remains an open question. Here we report the identification of several hundreds of DNA elements that tether canonical PRC1 to human developmental genes. We use the term tether to describe a process leading to a prominent presence of canonical PRC1 at certain genomic sites, although the complex is unlikely to interact with DNA directly. Detailed analysis indicates that sequence features associated with PRC1 tethering differ from those that favour PRC2 binding. Throughout the genome, the two kinds of sequence features mix in different proportions to yield a gamut of DNA elements that range from those tethering predominantly PRC1 or PRC2 to ones capable of tethering both complexes. The emerging picture is similar to the paradigmatic targeting of Polycomb complexes by Polycomb Response Elements (PREs) of   Multicellular animals rely on epigenetic mechanisms to repress alternative gene expression programs when their cells acquire specialized functions. This is common during embryonic development and critical in later life to replenish specific cell pools from multipotent stem cells. Polycomb group proteins make up the epigenetic system most widely used to repress alternative gene expression programs in differentiated cells ,2.Drosophila melanogaster as a critical regulator of homeotic selector genes, the Polycomb system was later shown to repress hundreds of genes encoding transcriptional regulators, morphogens, and signalling molecules that are involved in all main developmental pathways , SUZ12, EED and RBBP7 (or the related protein RBBP4). The PRC2 core may further associate with alternative sets of auxiliary subunits: PHF1   or JARIDDrosophila . These are short (∼1kb) DNA elements, which can be pinpointed from genomic Chromatin Immunoprecipitation (ChIP) profiles as distinct peaks co-occupied by PRC1 and PRC2 (kOFF) or facilitate their association (increase kON).Developmental genes repressed by Polycomb mechanisms are bound by canonical PRC1 and PRC2 and enriched in tri-methylated H3K27 (H3K27me3). What molecular mechanisms enable the specific binding of the complexes to these genes is an important open question. Neither canonical PRC1 nor PRC2 have subunits that contain sequence-specific DNA binding domains. In and PRC2 ,43. PREsand PRC2 ,45. Multand PRC2 . PREs maWhich DNA elements direct PRC1 and PRC2 to specific genes in mammals is less clear. Most of what we know about the genomic distribution of mammalian PRC1 and PRC2 comes from studies of mouse embryonic stem cells. In these cells, PRC1 and PRC2 seem to bind repressed genes in a broad and virtually identical fashion with no features to distinguish potential binding DNA elements ,42,46. MCyclin D2 (CCND2) gene is particularly interesting . Here, the term tether describes a process that leads to a prominent presence of canonical PRC1 at genomic sites that harbour such elements. The term does not imply the direct interaction of PRC1 with DNA. We and others speculate that sequence-specific DNA binding adapter proteins may mediate the PRC1 tethering  for 75 min harvested, incubated in hypotonic solution (KCl 75 mM) for 20 min at 37°C and fixed in methanol/glacial acetic acid (3:1). Metaphase spreads were analysed manually. Two sub-clones (F10 and D4) with the most similar karyotypes  were selected and the F10 sub-clone was used for most experiments.NTERA-2 (NT2-D1 ATCC® CRL-1973™), TIG-3  or SUZ12 (for MEL18-free PRC2-bound regions) but showed little conservation within mammalian species. We then substituted four of those nucleotides in the transgenic copies to create an annealing site for transgene-specific PCR primers see .ZIC2 PTE  vector  and cloned into the pMA-RQ vector. The fragments were excised from pMA-RQ constructs using EcoRI digestion, gel purified, and sub-cloned into the pENTR1A no ccdB (w48-1) vector for further shuffling into the pLenti-Gateway vector.The second strategy was used to test all other DNA fragments and was higher throughput. To this end, we generated the pLenti-Gateway vector by digesting the pLenti-ICR-Puro vector with AfeI and PmlI and recombining it with the DNA fragment containing Gateway attR cassette. This fragment was PCR amplified using primers Gateway_lenti_F and Gateway_lenti_R and DNA of the pLenti X1 Puro DEST (694–6) vector  and pCMV-VSV-G (3 μg)   were comFor the infection, cells were plated at a confluence of 40–60% 24 h in advance. Viral supernatants were added in serial dilutions to cells in combination with 8 μg/ml Polybrene (Millipore). Cells were subjected to single or multiple infections by adding either single lentiviral supernatant or supernatant pools . After overnight incubation, the medium was changed to remove Polybrene. Transduced cells were selected for 14 days by growth in a culture medium supplemented with 1μg/ml puromycin (Invitrogen).5 cells were mixed with 50pmol of Cas9 protein (IDT) and two gRNAs flanking a PTE sequence , subjected to an electric pulse (2500V for 10 ms) and plated in IMDM medium without antibiotics supplemented with 15% FBS. The medium was replaced with a complete medium  after 24 h, the cells were cultured for 2 more days and re-plated to a 96-well plate after limited dilution to obtain single cell clones. DNA of the recovered clones was subjected to PCR using primers flanking the expected deletion and the nucleotide sequences of PCR products from cells homozygous for deletion alleles were determined by Sanger sequencing. Another PCR analysis was performed using primers annealing to the deleted sequence to confirm the absence of the deleted fragment anywhere in the genome. Nucleotide sequences of the gRNAs and the primers for genotyping are listed in To generate cultured cell lines lacking specific PTEs, the cells of the F10 subclone of the KBM7-1-55-S2-24 line were transfected with the Cas9/gRNA complex using electroporation by Neon Transfection system (Thermo Fisher Scientific) according to manufacture recommendations. Briefly, 2 × 10RT-qPCR analysis was performed as previously described in . The priChIP reactions were performed as described in ,59. The Libraries for massively parallel sequencing (ChIP-seq) were prepared with NEBNext Ultra II FS DNA Library Prep Kit for Illumina (E7805) and NEBNext Multiplex Oligos for Illumina Index Primers Set 1 (E7335) according to the manufacturer's instructions. Briefly, 2 ng of ChIP DNAs were treated with NEBNext Ultra II FS Enzyme Mix for 20 min to the average length of 180 bp, followed by adaptor ligation and 8 cycles of PCR amplification. Pooled libraries from MEL18 and SUZ12 immunoprecipitations with chromatins from NT2-D1 and TIG-3 cells were sequenced at NGI Sweden, SciLifeLab, Stockholm. They were sequenced from single end using two lanes (one flow cell) of the Illumina HiSeq2500 instrument operated in rapid 50 bp read mode. Pooled libraries from all immunoprecipitations with chromatin from F10 cells and for immunoprecipitations with antibodies against RING and H3K27me3 and chromatins from NT2-D1 and TIG-3 cells were sequenced by NGI Sweden, SNP&SEQ Technology Platform in Uppsala. They were sequenced from single end using the 100 bp read mode of the Illumina NovaSeq 6000 instrument, v1 sequencing chemistry (Illumina) and two lanes of the SP flow cell.bowtie2 (v2.2.5) (–phred33 -p 8 after which reads with ambiguous genomic positions filtered using samtools (v1.3.1)  . Only regions for which the summit window was identified as significantly enriched in both replicate experiments were considered for further analyses. The ‘standalone’ SUZ12 bound regions were defined as those at a distance of >100 kb from any significantly enriched MEL18 region.The sequencing reads were aligned to the human GRCh38/hg38 reference genome using (v2.2.5)  set to –(v1.3.1)  view com(v2.1.2)  callpeakgeom_smooth function  of the ggplot2 package (https://ggplot2.tidyverse.org/). Local maxima (peaks) and local minima  in the smoothed ChIP-seq signals were identified with stat_peaks and stat_valleys functions of the same package. Each peak and valley were assigned a score representing the mean ChIP-seq signal over nine sequence positions centered on the corresponding peak or valley. The peaks with scores no lower than 50–60% of the highest peak in the same group and separated by valleys deeper than 30–35% of the scores for the flanking peaks were kept. This peak definition procedure returns similar number of peaks within the above range of parameters. To be conservative, only peaks called at all tested combinations of parameters and identified as significantly enriched in both replicate experiments were used for further analyses. Each of these peaks was assigned a ChIP-seq signal score calculated as the sum of sequencing reads within a 9bp window centered on the peak position.For each ChIP-seq data set, bound regions from above were grouped together if separated no further than 100 kb. ChIP-seq signals within each bound region were smoothed using the STREME version: 5.3.3  were used to discover 4–10 nucleotide sequence motifs in DNA underneath Q4 peaks defined from the NT2 MEL18 ChIP-seq profile of the replicate experiment with the largest dynamic range. DNA sequences of 110 randomly selected 5kb genomic fragments were used as the control data set. CentriMo version 5.4.1 ((–oc. –verbosity 1 –score 5.0 –ethresh 10.0 –bfile sequence.fasta.bg sequence.fasta motifs.meme) was used to calculate the number of motif matches per sequence position within 5kb DNA fragments centered on NT2-MEL18 peaks. The resulting profiles of motif matches were normalized to the total number of matches, divided into quartiles according to the corresponding peak scores, aggregated, and smoothed by a rolling mean of 500 positions.n: 5.3.3  and the on 5.4.1  executedGCF_000001405.38_GRCh38.p12_genomic.fna as long as they did not overlap with the 10kb DNA fragments centered on the NT2-D1 MEL18 peaks (any quartile). The difference between the di-nucleotide frequencies within two independently selected sets of control sequences served as a negative control reference 5 stretches within 100 bp sliding windows were calculated for 10kb DNA sequences centered on Q4-Q2 MEL18 peaks. For all instances when the putative target gene was located 5′ (upstream) of the MEL18 peak, the reverse complement sequence was used. Therefore, all target genes were oriented in the same direction, to the right of the peak. 685 control nucleotide sequences of 10kb DNA fragments not overlapping with 10kb DNA fragments centered on the NT2-D1 MEL18 peaks (any quartile) were randomly selected from GCF_000001405.38_GRCh38.p12_genomic.fna.The frequencies of poly(dA)bowtie2 . The alignments were first converted to bam format using Samtools (MACS2 (v2.1.2)  were dobowtie2  to the G(v2.1.2)  callpeakbowtie2 . Samtools . The resulting dataset was further reduced to putative PTE target genes. Those were identified as described in the Results section using gene annotation GCF_000001405.39_GRCh38.p13_genomic.gff table available from the National Center for Biotechnology Information (NCBI). Only annotations for which the feature column entry was either ‘mRNA’, ‘gene’ or ‘transcript’, the ‘gbkey’ attribute was ‘mRNA’ or ‘Gene’ and the ‘gene_biotype’ attribute was ‘protein_coding’ or missing were considered.RNA sequencing data for NT2-D1  and TIGbowtie2  to the GSamtools  was usedRsubread , the inbenrichGO function, from R Bioconductor package clusterProfiler  displayed strong MEL18 ChIP-seq signal (>3000 RPKM) comparable to that at the CCND2 locus, (ii) had the ratio between MEL18 and SUZ12 ChIP-seq signals higher than that at CCND2, (iii) showed strong binding of both MEL18 and SUZ12 (both >2000 RPKM) in TIG-3 cells. Three genomic sites, located in the vicinity of the ZIC2, NR6A1 and SOX21 genes, matched these criteria  transcription factor . Using this procedure, we identified 1014 peaks in the profile from the first MEL18 ChIP-seq experiment and 1152 peaks in the profile from the replicate experiment. We expect a genuine tethering element to be marked by a discrete MEL18 peak in each of the replicate profiles. In theory, corresponding peaks from replicate experiments should have the same genomic positions and ChIP-seq signal strength. In practice, both will differ, at least slightly, due to experimental variance. We, therefore, inspected peak sets from the two replicate MEL18 ChIP-seq experiments for pairs with the closest genomic positions. The survey of such pairs indicates that 84% of them have peaks located <500 bp apart, 61%—<250 bp, and in 33% of the cases, they reside as close as 100 bp . As expeCCND2  upper quartile (Q4) MEL18 peaks, our observations argue that DNA fragments centered on sharp (±300 bp location accuracy) Q4 MEL18 peaks represent the high-occupancy PTEs.We then split discrete MEL18 binding sites into quartiles based on the associated MEL18 ChIP-signal scores and selected six regions from the upper quartile (Q4). The selected regions had location accuracy of 168bp or better  or from individual cell-to-cell variation already present in the population of parental cells and fixed by cloning. To control for the latter, we derived four cell lines by expansion of single cells from the original F10 population . The RT-qPCR measurements show little variation in transcript levels of s Figure –F. In cos Figure , E. Onlye Figure . We specCCND2, ZIC2, and ALX3 PTEs are required for efficient tethering of PRC1 at these loci. Interestingly, the deletion of PTEs from ALX3 and ZIC2 loci causes a nearly complete loss of PRC1 ChIP signals even in the flanking regions  MEL18 ChIP-seq peaks from our data set could do the same. More specifically, we wished to ask whether DNA fragments underneath such MEL18 peaks could generate new PRC1 binding sites when integrated elsewhere in the genome.DNA sequences around the P-value < 0.05, unpaired, one-sided t-test) compared to the negative control  MEL18 peaks from the Q3-Q2 group  and re-il Figure , B. ChIPl Figure , D. Secol Figure –B, G–H.We take it to indicate that the majority of the DNA fragments underneath Q2 and Q3 MEL18 peaks can tether PRC1 when integrated elsewhere in the genome. However, compared to that of the high occupancy PTEs, the tethering ability is more dependent on the chromatin context. The information contained within the DNA underneath the Q2 and Q3 MEL18 peaks is essential for PRC1 tethering as neither the ‘empty’ backbone nor the constructs containing MEL18-free PRC2-bound CpG-islands or Spacer fragments generate PRC1 binding sites. Yet, our lentiviral transgenic system appears to favor tethering as some of the Q3-Q2 PTEs display stronger PRC1 ChIP-signals inside the transgenes compared to their endogenous locations.The information contained within PTEs is in some way encoded in their DNA sequence. What sequence features define this code? To address this issue, we first examined the overall nucleotide composition of the DNA underneath and around discrete MEL18 peaks. As illustrated by Figure 5 and poly(dT)5 tracts across the oriented DNA sequences revealed two noticeable biases 5 and poly(dT)5 tracts around the MEL18 peaks differ. Both kinds of tracts become less frequent, compared to the genomic average, in the DNA to the right of the peaks, presumably reflecting its elevated CG content 5 tracts remains high. It appears that selection pressure prevents the poly(dA)5 but not the poly(dT)5 tracts from being depleted within this CG-rich DNA. The biases are also evident for the DNA under the high-occupancy MEL18/BMI1 peaks identified by ChIP-seq with TIG-3 and F10 cells but not in the control frequency plots for ATATA and TATAT tracts  and the release of short DNA fragments  compared by sequencing . In suchNucleosome displacement may expose binding sites recognized by the sequence-specific DNA binding proteins that act as adaptors to tether of PRC1. To investigate this possibility, we combined the STREAM  and CentComparison with experimentally defined transcription factor binding sites from JASPAR  and HOCOCCND2 PTE contains one AAACGAAA nucleotide sequence, the highest-scoring match to the motif's position weight matrix, located directly underneath of the MEL18 peak summit  MEL18 ChIP-seq signal score and the position defined with accuracy better than ±301 bp . What kiCCND2 and ZIC2 genes, are highly transcriptionally active . As expected, most of these genes have chromatin around TSS decorated with H3K27me3. The extent of H3K27 methylation inversely correlates with transcriptional activity and it appears to be progressively lost with increased transcription starting from regions immediately around TSSs  of them. The majority of the genes have the closest PTE at a distance from 0.5 to 7 kb . Analysis Figure . The latCCND2 and ZIC2) become transcriptionally inactive in TIG-3  tracts within PTEs suggests that PRC1 tethering is somehow linked to chromatin remodeling by SWI/SNF (i.e. BAF or PBAF) complexes. In line with this notion, Weber and co-authors have found that rapid degradation of BRG1, the core ATPase subunit of BAF and PBAF complexes, leads to substantial loss of PRC1 from the most highly occupied binding sites in mouse embryonic stem cells . It woulThe work presented here advances our understanding of how human PRC1 binds to specific genes. It also raises several new questions. Do PTEs vary in their ability to tether PRC1 complexes composed of different CBX or PHC paralogues? How do deletions of individual PTEs affect the expression of their cognate genes in the context of the whole organism? What fraction of PTE-equipped genes linked to heritable human syndromes has alleles that carry PTE deletions or duplications? Our catalogue of human PTEs provides a valuable new resource to address these questions. Equally significant, it presents an opportunity to design transgenic assays to disentangle individual contributions of PRC1 and PRC2 to the repression by the Polycomb system and to screen for factors that enable PRC1 tethering.gkad889_Supplemental_FileClick here for additional data file.


PGE2 has been evaluated in various clinical trials to improve engraftment of stem cells. We identified that dmPGE2 mediates chromatin flexibility at HSPC-specific enhancers through histone-variant H2A.Z acetylation to promote master transcription factor (TF) binding and reinforce expression of genes involved in stem cell fate and engraftment.Hematopoietic stem and progenitor cells (HSPCs) require tight transcriptional regulation. In-depth knowledge on how epigenetic mechanisms regulate chromatin landscape in order to control gene expression, particularly in response to external stimuli, will be key to understand the pathogenesis of hematologic disorders and improve blood stem cell-based therapies. Prostaglandin E2 (PGE 2) and 16,16-dimethyl-PGE2 (dmPGE2) are important regulators of hematopoietic stem and progenitor cell (HSPC) fate and offer potential to enhance stem cell therapies . Here, we report that PGE2-induced changes in chromatin at enhancer regions through histone-variant H2A.Z permit acute inflammatory gene induction to promote HSPC fate. We found that dmPGE2-inducible enhancers retain MNase-accessible, H2A.Z-variant nucleosomes permissive of CREB transcription factor (TF) binding. CREB binding to enhancer nucleosomes following dmPGE2 stimulation is concomitant with deposition of histone acetyltransferases p300 and Tip60 on chromatin. Subsequent H2A.Z acetylation improves chromatin accessibility at stimuli-responsive enhancers. Our findings support a model where histone-variant nucleosomes retained within inducible enhancers facilitate TF binding. Histone-variant acetylation by TF-associated nucleosome remodelers creates the accessible nucleosome landscape required for immediate enhancer activation and gene induction. Our work provides a mechanism through which inflammatory mediators, such as dmPGE2, lead to acute transcriptional changes and modify HSPC behavior to improve stem cell transplantation.Prostaglandin E2 (PGE Hematopoietic stem cells are characterized by their unique ability to self-renew and differentiate into all mature blood cell lineages. During normal homeostasis and in conditions of stress such as injury or inflammation, HSPCs maintain an appropriate balance of the hematopoietic system. Hematopoietic stem and progenitor cells (HSPCs) sense and respond to a variety of extrinsic signals that regulate their quiescence, proliferation, and differentiation –3.2) is one of the most abundant prostaglandins produced in the body  outcomes in humans (2 predominantly exerts its effects by binding to the PGE2 receptor (EP) subtypes EP2 and EP4 on HSPCs2. Interaction with these G-coupled protein receptors enhances intracellular cAMP levels, which activates signaling cascades and downstream effectors, for instance Wnt and β-catenin -treated control cells . Among the up-regulated set were genes representative of dmPGE2/cAMP/PKA signaling, including PDE4B and PTGS2 . We validated the gene expression signature observed by RNA-Seq through RT-qPCR in HSPCs for representative genes . A total of 152 genes showed a ≤0.67-fold decrease in expression . Among the repressed genes, we found enrichment of genes that regulate cell division, such as HOXB4 and CCNF . These data showed that enhanced engraftment in vivo after dmPGE2 stimulation is, in part, driven by transcriptional induction of migration genes.We sought to define the molecular mechanisms that underlie the transcriptional response of HSPCs to dmPGE to PGE2 , 8. We erofiling A. Using ol cells B. The efnd PTGS2 ; severalnd CXCL8 ; and genand JUNB , 21 . CREB binds to cyclic-AMP response elements (CREs) near its target genes where it, upon phosphorylation at serine 133 (S133) by protein kinases, promotes the recruitment of coactivator proteins , while total TF protein levels remain unaltered . To correlate pCREB with gene induction, we performed chromatin immunoprecipitation sequencing (ChIP-Seq) with an antibody against pCREB. We found 31,198 binding sites  in CD34+ HSPCs treated for 2 h with dmPGE2 compared to 8,332 sites in DMSO . Correlation of pCREB occupancy to DEGs showed enrichment of pCREB at genes induced by dmPGE2 . Besides a higher percentage of up-regulated genes containing pCREB, the TF density was also >2 times higher at up-regulated genes compared to noninduced genes . We observed no clear correlation between the magnitude of the transcriptional response and the density of pCREB . Genes down-regulated by dmPGE2 showed no additional enrichment in pCREB compared to noninduced genes. Since both the phosphorylation status and TF occupancy of CREB may change in response to dmPGE2, we performed ChIP-Seq using a CREB antibody that is nondiscriminating against its phosphorylation status and compared the total CREB occupancy against pCREB-specific occupancy across the genome. These data demonstrated that, relative to total CREB levels, dmPGE2 preferentially induced pCREB binding near dmPGE2-responsive genes . Overall, these data suggested that dmPGE2 regulates pCREB activity and genomic binding near transcriptionally induced genes.The TF CREB has previously been associated with dmPGEignaling . Indeed,proteins . This inproteins . One of y dmPGE2 D. In facreatment D. This v2-treated HSPCs compared to control-treated cells. The other 7,812 (25%) pCREB sites were present in both control and stimulated HSPCs (“ubiquitous” pCREB regions). The majority of inducible pCREB sites were located distal to the TSS of genes, with a strong representation in intronic and intergenic sequences  sites displayed unique pCREB enrichment (“inducible” pCREB regions) in dmPGEequences F and G. 2 on distal regulatory elements, we determined the effects of a 2-h stimulation on the state of enhancers in HSPCs. H3K27ac distinguishes active enhancers from primed and poised enhancer elements that are marked by H3K4me1 alone or with H3K27me3, respectively . Enhancers that regulate stimulus-responsive gene programs can be distinguished from other active enhancers by their specific increase in H3K27ac upon receipt of the stimulus  enhancers that gained significant enrichment in H3K27ac following dmPGE2 treatment . A subset of these stimuli-inducible enhancers  were only detected as active regulatory regions after dmPGE2 stimulation. While these “de novo” enhancers were depleted of H3K27ac prior to stimulation, their H3K4me1high/H3K27me3low state indicated that de novo enhancers reside in a primed state prior to activation  displayed significant enhancement in H3K27ac enrichment after dmPGE2 treatment . Since the number of noninducible enhancers  is much larger than the set of inducible enhancers, we randomly sampled a comparable number of noninducible enhancers  to ensure that observed differences are not due to variations in the size of defined enhancer categories. To assess epigenetic transitions at inducible enhancers, we profiled genome accessibility by assay for transposase-accessible chromatin sequencing (ATAC-Seq). We found a profound increase in DNA accessibility at inducible enhancers compared to background enhancers after dmPGE2 stimulation . These data showed that dmPGE2-inducible enhancers gain both STF and additional MTF deposition following stimulation.It is well known that TFs act as anchors to recruit chromatin remodelers to regulate gene expression . Most STnhancers A and C. mulation B and C. 2 target genes is illustrated by the changes in the expression of genes regulated by stimuli-responsive enhancers compared to those regulated by background enhancers . We tentatively assigned enhancers to individual nearest genes. To reasonably limit arbitrariness in gene assignment, we only considered genes with a mapped TSS within 15 kb of an enhancer. The genes nearest to stimuli-inducible enhancers showed a greater transcriptional response than genes annotated to background enhancers . This indicates that differential enhancer activity is directly reflected in gene expression levels. Importantly, genes associated with inducible enhancers belonged to several pathways, including cell signaling and blood cell migration such as SGK1, CALCRL, CXCL2, CXCL5, and ITGA4 . We found a clear correlation between the number of genes showing differential expression and the changes in enhancer activity. Sixteen percent of genes nearest to de novo enhancers display ≥1.5-fold induction after dmPGE2 treatment, compared to 9% of enhanced and 3% of background enhancers . In total, 83 (16%) of the 535 up-regulated genes were regulated by at least one stimuli-responsive enhancer . This demonstrated that dmPGE2-mediated activation of stimuli-responsive enhancers correlates with modulation of gene expression.The contribution of distal regulatory regions for transcriptional activation of dmPGE2 treatment. We found that 208/498 (42%) de novo, 210/456 (46%) enhanced, and 125/486 (26%) background enhancers to contain at least 1 pCREB ChIP-Seq peak after dmPGE2 treatment. Chromatin accessibility and MTF binding increased more profoundly, but not exclusively, at pCREB+ stimuli-inducible enhancers . Together, our data suggested that increased enhancer chromatin accessibility and the cooperative binding of MTFs and STFs at stimuli-responsive regulatory regions drive transcriptional induction.To gain a better understanding of the role of pCREB at inducible enhancers, we further segmented enhancers based on the presence or absence of pCREB after dmPGEnhancers D. The re2 stimulation. Because nucleosomes have varying sensitivities to enzymatic digestion, MNase titrations were performed to obtain a comprehensive map of the nucleosome landscape within a genome . We prepared native nuclei from HSPCs treated with either DMSO or dmPGE2 and exposed them to increasing units of MNase. We selected four digestion points that generated increasingly larger fractions of mononucleosomal-size DNA. Mononucleosomal fractions comprised around 10%, 25%, 50%, and 75% of the input chromatin, respectively . Individual MNase titration profiles within a condition, as well as pooled average nucleosome occupancy profiles, revealed a TSS pattern similar to those previously reported . We observed lower average nucleosome occupancy at TSS-proximal regions of dmPGE2-responsive genes after treatment . This inverse correlation aligns with former studies which identified high levels of transcription to be concomitant with nucleosome eviction at the TSS, as elongation by RNA Polymerase II is thought to disrupt the nucleosomal organization . Light MNase conditions preferentially release accessible and unstable nucleosomes, whereas stable nucleosomes are more resistant to enzyme digestion and only released at higher MNase concentrations (2 stimulation (upper panel), whereas high MNase digestion degraded the nucleosomal DNA fragments (bottom panel). No changes in low MNase sensitivity were observed in background enhancers. Greater low MNase sensitivity after dmPGE2 treatment indicates a higher presence of MNase accessible nucleosomes. This shows that nucleosomes retained within inducible enhancers gained low MNase sensitivity upon stimulation.Nucleosome profiles from individual MNase titration points can be leveraged to determine how MNase sensitivity changes in specific regions . Inducible enhancers were not depleted of core histones prior to or after dmPGE2 stimulation. This is in line with our MNase-Seq data and suggested that nucleosomes are indeed present, and retained at, dmPGE2-responsive enhancers. In contrast to our expectations that increased chromatin accessibility at inducible enhancers . To exclude the possibility that the MNase-Seq fragments observed at pCREB sites within enhancers represent nonhistone proteins protecting from MNase digestion, we assessed fragment size distribution . We found a fragment periodicity that is characteristic for MNase-digested nucleosomes . Assessment of individual loci confirmed pCREB binding at stimuli-responsive enhancers that show greater low MNase signal and increased nucleosome occupancies after dmPGE2 treatment  of pCREB binding sites uniquely present after dmPGEleosomes , 38. Theleosomes . This anreatment C. Togeth2 stimulation, we investigated other mechanisms that influence accessibility to nucleosomal DNA. Weakening of internucleosomal interactions by covalent modifications of histones as well as the introduction of histone variants increases DNA accessibility . We noted minimal changes in H2A.Z enrichment following dmPGE2 stimulation. We did observe incorporation of the histone-variant H3.3 in the nucleosomes of enhancers . Additionally, NU9056 treatment of U937 cells demonstrated a substantial decrease of H2A.Z acetylation and under such conditions, dmPGE2 target genes appeared to show a reduction in gene induction compared to their respective control condition . While reduction in PGE2-mediated gene expression upon loss of Tip60 is moderate, we did not anticipate huge drop given very high fold-change observed under normal conditions. However, taken together, inhibition of Tip60 demonstrated a statistically significant trend toward compromised transcriptional responsiveness during dmPGE2 signaling.There has been a significant body of literature that suggests the importance of p300 in mediating H2A.Z acetylation , 50, 55.elopment , 58. As r NU9056 , 58. We 2 and its stable derivative dmPGE2 affect HSC function are critical to understand yet remain elusive. Here, we find that the TF CREB is a key player in the acute transcriptional response to dmPGE2 by binding to, and activating, distal regulatory elements. Specifically, we find that pCREB binds to H2A.Z-variant nucleosomes that are retained within stimuli-induced, active enhancers and is concomitant with acetylation of these histones. Upon dmPGE2 treatment, intensity of both H2A.Z and H2A.Zac increases specifically at pCREB-bound, dmPGE2-inducible enhancers, suggesting that acetylation of H2A.Z may likely increase on-chromatin interaction of pCREB and H2A.Z. Such interactions between pCREB and H2A.Z may further increase H2A.Z acetylation of enhancer nucleosomes to synergistically promote local chromatin accessibility, which may subsequently help recruit and/or stabilize other HSPC-specific TFs such as GATA2 to stimulate gene transcription and strengthen blood stem cell fate.PGE2 is an important regulator of HSPC homeostasis. The distinct molecular mechanisms through which PGECREB is a ubiquitously expressed nuclear, basic leucine zipper (bZIP) TF that regulates over 5,000 genes in the mammalian genome. This includes genes controlling proliferation, differentiation, and cell survival . The TF 2 works within the predetermined enhancer repertoire of HSPCs. dmPGE2 stimulation activates a set of preexisting H3K4me1+ enhancers through chromatin reorganization. dmPGE2-responsive enhancers rapidly gain accessibility and TF binding. Our work complements the studies that showed that STFs localize to binding sites adjacent to master regulators , isolated from granulocyte-colony-stimulating factor (G-CSF) mobilized peripheral blood of healthy volunteers, were purchased from the Fred Hutchinson Cancer Research Center. The cells were maintained in suspension culture as previously described by Trompouki et al.  heat-inactivated fetal bovine serum, 1× GlutaMax, and 1% penicillin–streptomycin at 37° in a humidified atmosphere of 5% CO2  or DMSO (vehicle control) for 2 h.16,16-dimethyl PGE2 was purchased, reconstituted in DMSO from Cayman Chemicals (cat. #14750), aliquoted, and stored in −80 °C until use. Cells were counted, collected, and resuspended in StemSpan medium with 2% PenStrep (CD34+ Cells) or RPMI with 1% penicillin–streptomycin, but in the absence of additional cytokines or growth factors. The cells were treated with 10 μM dmPGESI Appendix, Supplemental Materials and Methods.RNA was extracted using the RNeasy plus mini kit (Qiagen). cDNA synthesis was performed using the Superscript VILO (Invitrogen) and using equal amounts of starting RNA. The cDNA was analyzed with the Light Cycler 480 II SYBR green master mix (Applied Biosystems), and the QuantStudio 12K Flex (Applied Biosystems). All samples were prepared in triplicate. The PCR cycle conditions used are: (a) 95 °C for 5 min, (b) 95 °C for 10 s, 54 °C for 10 s, 72 °C for 15 s × 40 cycles. The analysis of Ct values was performed using 2^-ΔΔT method. Primers used are listed in SI Appendix, Supplemental Materials and Methods.These experiments are described in detail in SI Appendix, Supplemental Materials and Methods.Detailed description of RNA-Seq, ChIP-Seq, ATAC-Seq, and MNase-Seq methods is available in Appendix 01 (PDF)Click here for additional data file.


Hif1a gene was reported to affect the development of sympathetic ganglia and sympathetic innervation of the heart. This study characterized how the combination of HIF-1α deficiency and streptozotocin (STZ)-induced diabetes affects the cardiac sympathetic nervous system and heart function of adult animals.An altered sympathetic nervous system is implicated in many cardiac pathologies, ranging from sudden infant death syndrome to common diseases of adulthood such as hypertension, myocardial ischemia, cardiac arrhythmias, myocardial infarction, and heart failure. Although the mechanisms responsible for disruption of this well-organized system are the subject of intensive investigations, the exact processes controlling the cardiac sympathetic nervous system are still not fully understood. A conditional knockout of the Hif1a deficient sympathetic neurons were identified by RNA sequencing. Diabetes was induced in Hif1a knockout and control mice by low doses of STZ treatment. Heart function was assessed by echocardiography. Mechanisms involved in adverse structural remodeling of the myocardium, i.e. advanced glycation end products, fibrosis, cell death, and inflammation, was assessed by immunohistological analyses.Molecular characteristics of Hif1a alters the transcriptome of sympathetic neurons, and that diabetic mice with the Hif1a-deficient sympathetic system have significant systolic dysfunction, worsened cardiac sympathetic innervation, and structural remodeling of the myocardium.We demonstrated that the deletion of Hif1a deficient sympathetic nervous system results in compromised cardiac performance and accelerated adverse myocardial remodeling, associated with the progression of diabetic cardiomyopathy.We provide evidence that the combination of diabetes and the The online version contains supplementary material available at 10.1186/s12933-023-01824-5. Heart function is tightly controlled by the autonomic nervous system, which is comprised of parasympathetic and sympathetic neurons. The cardiac sympathetic nervous system uses the catecholamine norepinephrine as a neurotransmitter, and modulates heart rate, conduction velocity, myocardial contractility, cardiomyocyte size and structure –3. The sCompromised cardiac performance, sympathetic nervous system abnormalities, and aberrant myocardial remodeling are hallmarks of diabetic cardiomyopathy (as reviewed in –10). DiaHif1a), whose regulation is oxygen dependent, results in impaired responses to hypoxia and ischemia in mouse models [Hif1a haploinsufficiency and diabetes accelerated the pathological changes in the heart, such as the functional impairment of the left ventricle accompanied by cardiac remodeling [The transcription factor HIF-1 is required for physiological responses to hypoxia and plays a critical role in the pathophysiology of human diseases, such as cancer, cardiovascular disease, and diabetes , 16. Thee models –20. Althe models –25, revie models , 27. Cormodeling , and in modeling , 30.Hif1a–/– and Hif2a–/– mice, suggesting some functionally nonredundant roles [HIF2a results in a proliferative immature phenotype, whereas high expression of HIF1a leads to mature catecholamine phenotypic features. Thus, the requirements for HIF-1α and HIF-2α in the development and maturation of glomus cells, chromaffin cells, and sympathetic neurons are still ambiguous. Unlike HIF-1α, there are relatively a few papers reporting a role of HIF-2α in insulin signaling and diabetes [HIF-2α  is another closely related isoform, which also activates HRE-dependent gene transcription. HIF-1α and HIF-2α are essential for acute oxygen sensing and their coordinated transcriptional activation is critical for physiological adaptations to hypoxia, including increase in respiration and cardiac output, reviewed in . HIF-1α nt roles –35. A cont roles . In adulnt roles . Opposinnt roles . High exdiabetes –41.Hif1a deletion [Hif1a [Isl1Cre (Hif1aCKO). Previously, we found that Hif1a deletion impaired the survival and proliferation of preganglionic and postganglionic neurons of the sympathetic nervous system, survival of neuroendocrine chromaffin cells in the adrenal gland, the expression of tyrosine hydroxylase (TH), a direct target of HIF-1, and sympathetic innervation of Hif1aCKO hearts, without affecting heart development [Hif1a-deficient sympathoadrenal cell lineage may also play a role in various sympathetic abnormalities underlying cardiac pathologies, such as sudden cardiac death and heart failure. Therefore, we questioned whether the Hif1a-deficient sympathetic nervous system would affect the development of diabetic cardiomyopathy. We hypothesized that an early phase of diabetes-exposure would be the best option to uncover the effects of the Hif1a-deficient sympathetic system on functional and structural adaptations of the diabetic heart, and consequently, any acceleration of the early-phase pathological effects of diabetes on the heart. In the present study, using the same model, we report that Hif1a deficiency in sympathetic neurons in combination with diabetes resulted in worsened cardiac function, accelerated adverse structural remodeling and sympathetic denervation in the heart.We have previously revealed a key role for HIF-1α in the development of sympathetic neurons and sympathetic innervation of the heart . To bypadeletion , 43, we n [Hif1a  using Iselopment . These fHif1a (Hif1aCKO) we crossbred Isl1Cre/+ knock-in mice (Isl1tm1(cre)Sev/+) [Hif1atm3Rsjo with exon 2 of Hif1a gene flanked by loxP sites  [Isl1-Cre−/−; Hif1aloxP/+ or Isl1-Cre−/−;Hif1aloxP/loxP mice were used as controls. Control and heterozygous Isl1-Cre; Hif1aloxP/+ mice were born and survived according to the Mendelian ratio of genotypes. We also used tdTomatoAi14 reporter mice with Rosa-CAG-LSL-tdTomato allele 26Sortm14(CAG−tdTomato)Hze, Stock No: 7914 Jackson Laboratory). The reporter Hif1aCKO-Ai14  and control-Ai14 mice  were used in our molecular and phenotypic analyses. All comparisons were made between animals with the same genetic background. Phenotyping and data analysis were performed blind to the genotype of the mice Sev/+)  with micxP/loxP) . Isl1-CrDiabetes was induced in males at 6 weeks of age by 2 intraperitoneal injections of 100 mg/kg body weight of streptozotocin  within a one-week interval, as described , 29. The3 − LVDs3)/LVDd3. Diastolic and systolic cavity volumes  were calculated based on prolate spheroid geometry using the formula EDV = 0.001 * (4 * π/3) * k * LVDd3/8 and ESV = 0.001 * (4 * π/3) * k * LVDs3/8, where (k) is a ratio of long to short axis. Stroke volume (SV) = EDV–ESV, cardiac output (CO) = SV * HR, where (HR) is heart rate, and cardiac index (CI) = CO/BW, where (BW) is the body weight.The echocardiographic evaluation of the geometrical and functional parameters of the left ventricle (LV) was performed using the GE Vivid 7 Dimension  with a 12 MHz linear matrix probe M12L. The diastolic and systolic dimensions of the LV including anterior and posterior wall thickness  and cavity diameter  were measured in the 4-month-old animals as previously described , 42. ThetdTomatoAi14 fluorescent reporter were dissected from male mice. Sympathetic chains were homogenized in Hanks’ Balance Salt Solution , neuronal cells were dissociated using 0.1% collagenase for 5 min , then cells were treated by 0.25% trypsin in Dulbecco’s PBS for 7 min , and cells were washed between treatments using Dulbecco’s PBS  based on the protocol [+ cells from one sympathetic ganglion chain were collected into individual wells of 96-well plate containing 5 µl of lysis buffer of NEB Next single-cell low input RNA library prep kit for Illumina . Plates were frozen immediately on dry ice and stored at − 80 °C. The total time from euthanasia to cell collection was ∼ 3 h.Sympathetic ganglia with protocol . EnzymatHif1aCKO-Ai14 mutant (n = 5) and control-Ai14 (n = 3). Each sample contained 100 tdTomato+ sympathetic neurons. Following the manufacturer’s instructions, the NEB Next single-cell low input RNA library prep kit for Illumina was used for cDNA synthesis, amplification, and library generation [https://www.ncbi.nlm.nih.gov/geo/).RNA-seq libraries were prepared from neration  at the Gneration ). GRCm38neration , the numneration . Ribosomneration . A countadj < 0.01, FC > 1.5, and a base mean ≥ 50 was applied to identify differentially expressed genes between Hif1aCKO mutant and control neurons. The volcano plot was constructed using programming language R (packages ggplot2 version 3. 3. 6. and ggrepel 0.9.1.). The functional annotation of the differentially expressed genes was performed using GOTermFinder and g: Profiler. Complete query details are available in Query info tabs in Additional file DESeq2 (v1.26.0 ) defaultHprt1. All reactions were conducted in duplicates and the data were calculated using the ΔΔCp method, as previously described [RNA was isolated from dissected sympathetic chains of individual adult males using Trizol . The concentration and purity were quantified using NanoDrop . cDNA samples were prepared using Maxima H Minus First Strand cDNA Synthesis Kit with dsDNA  from RNA collected samples . Quantitative real-time PCR (qRT-PCR) was performed with 10 × diluted cDNA samples. 4 µl cDNA was added to 5 µl of SybrGreen  with 0.2 µM reverse and forward primers. Primers were designed using Primer 3 software and sequences are in Additional file escribed , 55.The diabetic and non-diabetic hearts, and adrenal glands were homogenized with RIPA (Radio Immuno Precipitation Assay buffer) containing phosphatase and protease inhibitors, as described , 29. Bri+, PAS+, and picrosirius red+ areas, and expressed as a percentage of analyzed total area. Positive WT1, NeuN, and F4/80 cells were counted using the “Cell counter” function ImageJ. TUNEL+ cells were counted in three consecutive transversal paraffin sections of the LV.Whole-mount immunohistochemical staining of adult hearts were performed on cleared tissue, as described . Adult hg at 4 °C. Subsequently, plasma was collected and quickly frozen at − 80 °C. Quantitative detection of epinephrine was performed using competitive EIA (enzyme immunoassay) kit  in 96-Well Strip Plate with the lowest detection limit 2.54 picograms/mL. The optical density of each sample was performed at 450 nm using high performance plate reader CLARIOStar . Concentration of each sample was calculated from the linear equation of standard using GraphPad Prism 9.4.1.The Enzyme-Linked Immunosorbent Assay (ELISA) was performed to measure epinephrine levels in plasma samples obtained from 4 months old diabetic and non-diabetic control and mutant male mice. Blood was collected from the facial vein directly to anticoagulant EDTA-treated tubes. Cells were removed from plasma by centrifugation for 15 min at 1500 ×Ai14 and Hif1aCKO-Ai14 mice (4 months-of age). We used an advanced CUBIC protocol [The secondary sympathetic chain was microdissected from non-diabetic and diabetic, control-protocol  for tissHif1aCKO) were analyze with two-way ANOVA testing differences among experimental groups based on the genotype and experimental condition (diabetic exposure or non-diabetic exposure) followed by post hoc comparisons tests. Data are expressed as mean ± SD or mean ± SEM. Sample sizes and individual statistical results for all analyses are provided in the figure legends and tables.Statistical analyses were performed using GraphPad Prism 9.4.1, results are considered significant at P values of < 0.05. Graphs showed only P values less than or equal to 0.05 in multiple comparison tests. Data sets with two groups (non-diabetic vs. diabetic) and two genotypes , which results in altered cardiac sympathetic innervation and formation of sympathetic ganglia [Hif1a-deficient sympathetic system on functional and structural adaptations of the diabetic heart. Due to sex differences in cardiac electrophysiological properties [Hif1a mutation  revealed no significant changes in relative cardiac size , and modulators of GABA synaptic transmission, Plcl1 [Hap1 [Sema4g, Plxna3), netrins (Ntn1) [; several members of the cadherin superfamily  [Itgae, Itgax, Adgrl4, Sdk2); and the ephrin receptor (Ephb4). The other top enriched GO-terms for upregulated genes in Hif1aCKO neurons were related to general cell processes, such as "metabolic process", "regulation of cellular process", and "biological regulation". Interestingly, genes encoding components previously associated with the epidermal growth factor (EGF) receptor-mediated signaling pathway were enriched in Hif1aCKO neurons, including Megf10, Sned1, Egflam, Egfl6, and Garem1. Functional enrichment analyses also identified the upregulation of the Reactome pathway associated with O-linked glycosylation in Hif1aCKO sympathetic neurons.To gain insight at the molecular level on how the ity data . Each bin, Plcl1 , and Hapl1 [Hap1 . In conts (Ntn1) ; severalCtnnal1) ; adhesioHif1aCKO phenotype, the RNAseq analysis identified differentially expressed genes previously described to be associated with sympathetic neurons, heart function, and hypoxia. For instance, Prkar2b is the HIF-1α target gene [Adra2a regulates neurotransmitter release from sympathetic nerves and its elimination results in worsened cardiac function [Brinp1 and Brinp2 are expressed in sympathetic neurons [Brinp2 levels are altered after myocardial infarction [Epha5, is important for axon guidance, survival and neurite outgrowth of sympathetic neurons [Sema4g [Dcn [Ntn [Tlr4 [Pros1 is a pro-neurogenic factor and neuroprotectant during ischemic-hypoxic brain injury [Hif1aCKO.Consistent with the get gene  that infget gene ; Adra2a function ; Brinp1  neurons , and Brifarction ; and a m neurons  were amo [Sema4g  and Dcn a4g [Dcn ), for syDcn [Ntn ), and imtn [Tlr4 ). Interetn [Tlr4 . Similarn injury , 75. We Hif1aCKO, as well as the significant role of sympathetic denervation in cardiac autonomic neuropathy associated with diabetes [Hif1aCKO hearts when compared to the control group , a marker of parasympathetic cholinergic neurons, remained unaffected in the heart . Altfunction , 91. To ols Fig. . As expeSystemic hyperglycemia and impaired LV function are associated with increased cell death and profibrotic responses. No significant differences in the number of apoptotic cells were found in the LV among the experimental groups Fig. . These rHif1aCKO animals showed significantly larger collagen accumulation compared to diabetic control hearts, indicating a synergistic effect of Hif1a-deficient sympathetic system and diabetes on collagen deposition. In addition, we quantified the protein levels of collagen type I, the major component of the myocardial extracellular matrix [Hif1aCKO mice showed the highest levels of collagen type 1 , itmyopathy , 92. Enhmyopathy . Consistc injury –97.Hif1a deficient cardiac sympathetic nervous system are less able to adapt in the longer term to the adverse diabetic environment. We propose that HIF-1α regulation is important for the maintenance of functional features of sympathetic neurons in physiological and pathophysiological conditions. Consequently, HIF-1α dysregulation may contribute to various sympathetic abnormalities underlying cardiac pathologies, including heart failure and sudden cardiac death.In conclusion, our results indicate that diabetic hearts with the Additional file 1: Table S1. Primer sequences for genotyping. Table S2. Primer sequences for quantitative real-time polymerase chain reaction. Table S3. Primary antibodies. Figure S1. Fluorescent Activated Cell Sorting of tdTomato+ neuronal cells. Gating strategy of the tdTomato+ (A) and tdTomato- sympathetic neurons (B). Figure S2. Basal left ventricular echocardiographic parameters. The echocardiographic evaluation of left ventricular systolic function of non-diabetic and diabetic, control and Hif1aCKO mice . Two-Way ANOVA followed by Tukey’s comparison multiple tests were used. Data are mean ± SEM; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; ns = non-significant. Abbreviations: AWTd, diastolic anterior wall thickness; AWTs, systolic anterior wall thickness; LV, left ventricle; LVDd, diastolic cavity diameter; LVDs, systolic cavity diameter; PWTd, diastolic posterior wall thickness; PWTs, systolic posterior wall thickness; RWT, relative wall thickness; SV, stroke volume.Additional file 2: Video S1. Non-diabetic control stellate ganglion. Microdissected stellate ganglion of tdTomato reporter control-Ai14 mice was cleared (CUBIC protocol), imaged, and reconstructed in 3D using light-sheet fluorescence microscopy (LFSM). Video shows the distribution tdTomato+ sympathetic neurons in the anatomical microenvironment of the ganglion.Additional file 3:Video S2. Diabetic control stellate ganglion. Microdissected stellate ganglion of diabetic tdTomato reporter control-Ai14 mice was cleared (CUBIC protocol), imaged, and reconstructed in 3D using light-sheet fluorescence microscopy (LFSM). Video shows the distribution tdTomato+ sympathetic neurons in the anatomical microenvironment of the ganglion.Additional file 4: Video S3. Non-diabetic Hif1aCKO stellate ganglion. Microdissected stellate ganglion of tdTomato reporter Hif1aCKO-Ai14 mice was cleared (CUBIC protocol), imaged, and reconstructed in 3D using light-sheet fluorescence microscopy (LFSM). Video shows the distribution tdTomato+ sympathetic neurons in the anatomical microenvironment of the ganglion.Additional file 5:Video S4. Diabetic Hif1aCKO stellate ganglion. Microdissected stellate ganglion of diabetic tdTomato reporter Hif1aCKO-Ai14 mice was cleared (CUBIC protocol), imaged, and reconstructed in 3D using light-sheet fluorescence microscopy (LFSM). Video shows the distribution tdTomato+ sympathetic neurons in the anatomical microenvironment of the ganglion.Additional file 6: Video S5. The secondary sympathetic chain of non-diabetic control. Microdissected the stellate and four upper ganglia of the thoracic sympathetic chain of tdTomato reporter control-Ai14 mice were cleared (CUBIC protocol), imaged, and reconstructed in 3D using light-sheet fluorescence microscopy (LFSM). Video shows the distribution tdTomato+ sympathetic neurons in the anatomical microenvironment of the ganglion.Additional file 7: Video S6. The secondary sympathetic chain of diabetic control. Microdissected the stellate and four upper ganglia of the thoracic sympathetic chain of diabetic tdTomato reporter control-Ai14 mice were cleared (CUBIC protocol), imaged, and reconstructed in 3D using light-sheet fluorescence microscopy (LFSM). Video shows the distribution tdTomato+ sympathetic neurons in the anatomical microenvironment of the ganglion.Additional file 8:Video S7. The secondary sympathetic chain of non-diabetic Hif1aCKO. Microdissected the stellate and four upper ganglia of the thoracic sympathetic chain of tdTomato reporter Hif1aCKO-Ai14 mice were cleared (CUBIC protocol), imaged, and reconstructed in 3D using light-sheet fluorescence microscopy (LFSM). Video shows the distribution tdTomato+ sympathetic neurons in the anatomical microenvironment of the ganglion.Additional file 9:Video S8. The secondary sympathetic chain of diabetic Hif1aCKO. Microdissected the stellate and four upper ganglia of the thoracic sympathetic chain of diabetic tdTomato reporter Hif1aCKO-Ai14 mice were cleared (CUBIC protocol), imaged, and reconstructed in 3D using light-sheet fluorescence microscopy (LFSM). Video shows the distribution tdTomato+ sympathetic neurons in the anatomical microenvironment of the ganglion.Additional file 10:Dataset S1. RNA sequencing: differentially expressed protein coding genes  > 1.5 cutoff values). Dataset S2. g: Profiler analyses of differentially expressed genes.