N-acyl homoserine lactones (AHLs) as communication molecules, which are produced by LuxI-type synthases and sensed by cognate LuxR-type receptors. These receptors act as transcriptional regulators controlling the expression of specific genes. Some bacteria harbor LuxR-type receptors lacking a cognate LuxI-type synthases, designated as LuxR solos. Among many other LuxR solos, the entomopathogenic enteric bacterium Photorhabdus luminescens harbors a SdiA-like LuxR solo containing an AHL signal-binding domain, for which a respective signal molecule and target genes have not been identified yet. Here we performed SPR analysis to demonstrate that SdiA acts as a bidirectional regulator of transcription, tightly controlling its own expression and the adjacent PluDJC_01670 (aidA) gene in P. luminescens, a gene supposed to be involved in the colonization of eukaryotes. Via qPCR we could further determine that in sdiA deletion mutant strains, aidA is upregulated, indicating that SdiA negatively affects expression of aidA. Furthermore, the ΔsdiA deletion mutant exhibited differences in biofilm formation and motility compared with the wild-type. Finally, using nanoDSF analysis we could identify putative binding ability of SdiA towards diverse AHLs, but also to plant-derived signals, modulating the DNA-binding capacity of SdiA, suggesting that this LuxR solo acts as an important player in interkingdom signaling between P. luminescens and plants.In bacteria, group-coordinated behavior such as biofilm formation or virulence are often mediated via cell–cell communication, a process referred to as quorum sensing (QS). The canonical QS system of Gram-negative bacteria uses  The first flow cell of the chip was kept free and used to obtain a blank sensorgram for subtraction of bulk refractive index background for data evaluation. Then, different concentrations  at a final volume of 150 μL for each dilution of SdiA were prepared in HPS-EP+ buffer for SPR analysis. Additionally, SdiA binding properties to the different promoters were tested under the influence of 3.3% (v/v) PRE and the respective controls with 3.3% (v/v) methanol. The run started with an injection time of 180 s, a flow rate of 30 µL/min with a dissociation time of 420 s. Between every cycle the chip surface was regenerated by two regeneration steps, first injecting 2.5 M NaCl, for 60 s at a flow of 30 µL/min followed by an injection of 0.5% (w/v) SDS for 60 s at a flow rate of 30 µL/min. The resulting sensorgrams were recorded using the Biacore T200 control software 3.2.1 and analyzed with the Biacore T200 evaluation software 3.2.1 (Cytiva) to determine the binding kinetics parameter as well as the binding affinity of SdiA to the tested promoters using a 1:1 binding algorithm.SPR analysis allows real-time detection of different types of biomolecular interactions, where bindings, specificities, kinetics, and affinities can be determined. Here, we performed SPR analysis using Biacore T200  and precoated SA sensor chips (Cytiva), where streptavidin is covalently attached to a carboxymethyl dextran matrix. To test whether SdiA binds to the selected promoter regions, respective DNAs fragments were 5′biotinylated and amplified via PCR from ere used . Chip eqaidA real-time qPCR was performed by comparing the expression level of the respective genes of P. luminescens 2° ΔsdiA with the wild-type. For that purpose, from overnight cultures of both wild-type and deletion mutant, 50 mL cultures with an OD600 of 0.05 were prepared and aerobically cultivated for 24 h at 30 °C. In total, three independent biological replicates were sampled, and the total RNA was extracted via the Aqua/Phenol-chloroform-isoamyl alcohol  es esE. colWG-motif B Tyr73 oompounds ,44,45. PdA genes ,47. Althr region C harborieceptors . FurtherR. solanacearum, both motility strategies are critical for plant colonization and development of maximal virulence [P. luminescens, it is known that 2° cells are highly motile compared with 1° cells and react to PRE, which was suggested to be an important trait for plant colonization [In bacterial mutualism and virulence, not only biofilms, but also motility through swimming and twitching are essential for a successful host colonization. Especially twitching motility, which is a movement driven by pilus extension, attachment, and retraction on viscous or solid surfaces, plays a major role in pathogenesis ,51. Indeirulence ,53. For nization ,22. HoweVibrio harveyi [Escherichia, Salmonella, or Klebsiella, influence expression of genes related to virulence factors such as biofilm formation or motility [sdiA deletion on swimming and twitching motility as well as biofilm formation of P. luminescens. Remarkably, P. luminescens 2° ΔsdiA deletion mutant showed a totally impaired swimming as well as twitching capacity in comparison with 2° WT  was used (i) to exclude possible buffer-derived denaturing of SdiA during protein purification and (ii) to test the protein stability in the presence of PRE and AHLs. Furthermore, PRE were separated into 48 fractions (ON) on average of about 44 °C, where the protein started to unfold. The inflection point, on average at 53.3 °C, indicates the moment where half of the protein appears in an unfolded state and is equal to TM (M was determined as 46.1 °C) . However, upon binding of C12-AHL, thermostability of SdiA increased appearing as a ‘right-shifted’ curve with higher TM (47.7 °C). Surprisingly, SdiA was less stable in the presence of C4-AHL, as indicated by ‘left-shifted’ curve with lower TM (42.2 °C) compared with the control  which was visible as a ‘left-shifted’ curve compared with the SdiA-control  in the absence of AHLs [E. coli [Pseudomonas spec. that subsequently led to induction of the expression of different genes [P. luminescens SdiA.Additionally, it is known that plants produce molecules mimicking AHLs, which are sensed by, e.g., orescens ,19. To gugh HPLC A. Indeedexudates , it migh of AHLs , and tha of AHLs . Further[E. coli . Interesnt genes . TherefoP. luminescens can be classified into the class D of the LuxR family regulators, which represent LuxR solos responding to non-AHL exogeneous signals [Pantoea stewartii, where AHLs were found to block DNA-binding capacity [P. luminescens, which were similar in the ΔsdiA mutant or exposing the respective wild-type to PRE. However, it must be determined, which plant-derived compound(s) bind to SdiA of P. luminescens, and whether SdiA-DNA-binding capacity is really affected upon signal binding.Taken together, we suggest a new communication circuit where SdiA is involved in IKS communication with plants, whereupon SdiA modulation of gene expression might be affected by signal binding due to conformational changes. Thereupon, SdiA of  signals . Our fincapacity ,76,77,78lux-box conserved sites to control gene transcription [luxI, which is also designated as autoregulation [P. luminescens SdiA is a LuxR solo as the bacteria do not harbor any luxI gene, we hypothesized that SdiA might regulate activity of its own promoter region. To validate this idea and the hypothesis that SdiA regulation is signal independent, we first studied the binding affinity of SdiA to its respective promoter (PsdiA) via surface plasmon resonance (SPR) spectroscopy without any putative ligand. Furthermore, we also analyzed the binding of SdiA towards the promoter of neighboring gene aidA (PaidA) since this gene is located upstream of sdiA and encodes in the opposite orientation. Both genes share a 310 bp long promoter containing intergenic region harboring putative lux-box-like motifs with a 12 bp long variable motif between with a 5′CT and a 3′AG (fliE not containing any lux-box-like motif in the respective promoter region (sdiA due to a high association rate (ak = 1.1 × 105/M*s) and a dissociation rate of dk = 3.1 × 10−3/s resulting in an overall affinity of KD = 27.4 nM. This reveals a fast self-regulatory property of SdiA with rapid association and fast dissociation. However, SdiA bound with a ~3.5× higher association rate to the promoter of aidA (PaidA), caused by an extreme low dissociation rate (dk = 1.7 × 103/s) with comparable association rate to PsdiA (ak = 3.9 × 105/M*s), resulting in an overall affinity of 4.4 nM  resulting in an overall affinity of 109 nM (aidA occurred in the presence of PRE (dk = 1.1 × 10−3/s), whereas here also the association rate was affected (ak = 8.2 × 104/M*s), resulting in a lower overall affinity of 13.3 nM,  of nacearum B, and asP. luminescens 2° cells since the bacteria can colonize plant roots, protecting them from phytopathogenic fungi infection. As this cell variant does not re-associate with nematodes and interacts with plants, it was important to understand and to determine how the bacteria sense the rhizosphere environment. We can demonstrate that the LuxR solo SdiA harboring an AHL-like SBD is involved in IKS communication between P. luminescens and plants, potentially involving AidA in plant host colonization. First, in this work we demonstrated a regulatory crosstalk between SdiA motility as well as biofilm formation regulating a putative switch between a sessile and motile lifestyle of the bacteria (aidA and sdiA are under control of SdiA is unknown and must be determined. From our findings we conclude that SdiA regulates expression of aidA and sdiA in a signal-independent manner (P. luminescens with plants as DNA binding was also influenced upon PRE. We also identified that SdiA acts as a bidirectional regulator binding in the intergenic region of sdiA and aidA since the receptor binds both promoters with high affinity, most probably negatively affecting expression of aidA. Lastly, for AidA of P. luminescens, of which homologs are also found in plant-associated bacteria and are regulated by a LuxR, we suggest an involvement in P. luminescens plant colonization whereby SdiA controls the expression of aidA depending on IKS with the plant host (Recent research showed potential biocontrol ability of bacteria . Nevertht manner . Here, want host .


Human primary hepatocytes (PHCs) are considered to be the best cell source for cell-based therapies for the treatment of end-stage liver disease and acute liver failure. To obtain sufficient and high-quality functional human hepatocytes, we have established a strategy to dedifferentiate human PHCs into expandable hepatocyte-derived liver progenitor-like cells (HepLPCs) through in vitro chemical reprogramming. However, the reduced proliferative capacity of HepLPCs after long-term culture still limits their utility. Therefore, in this study, we attempted to explore the potential mechanism related to the proliferative ability of HepLPCs in vitro culture.In this study, analysis of assay for transposase accessible chromatin using sequencing (ATAC-seq) and RNA sequencing (RNA-seq) were performed for PHCs, proliferative HepLPCs (pro-HepLPCs) and late-passage HepLPCs (lp-HepLPCs). Genome-wide transcriptional and chromatin accessibility changes during the conversion and long-term culture of HepLPCs were studied. We found that lp-HepLPCs exhibited an aged phenotype characterized by the activation of inflammatory factors. Epigenetic changes were found to be consistent with our gene expression findings, with promoter and distal regions of many inflammatory-related genes showing increased accessibility in the lp-HepLPCs. FOSL2, a member of the AP-1 family, was found to be highly enriched in the distal regions with increased accessibility in lp-HepLPCs. Its depletion attenuated the expression of aging- and senescence-associated secretory phenotype (SASP)-related genes and resulted in a partial improvement of the aging phenotype in lp-HepLPCs.FOSL2 may drive the aging of HepLPCs by regulating inflammatory factors and its depletion may attenuate this phenotypic shift. This study provides a novel and promising approach for the long-term in vitro culture of HepLPCs.The online version contains supplementary material available at 10.1186/s12864-023-09349-7. The liver is the largest and most important metabolic organ and is critical to human health. Around 300 million people in China are affected by liver disease –3. AlthoIn our previous studies, we established a chemical reprogramming strategy to dedifferentiate mouse and human PHCs into expandable hepatocyte-derived liver progenitor-like cells (HepLPCs) , 13. HepUnderstanding the mechanisms of functional decline of HepLPCs during passaging is essential to overcome these limitations. Gene transcription could be regulated by controlling the accessibility of transcription factors , 19, andKRT19 and EPCAM was significantly increased in HepLPC-P3 and decreased in HepLPC-P10  culture condition in vitro . HoweverTo conduct genome-wide transcriptional analysis during the conversion and long-term culture of HepLPCs, RNA-Seq data from fetal hepatocytes, PHCs, HepLPC-P3, and HepLPC-P10 were used. Correlations and principal component analyses (PCA) of the whole-genome expression profiles showed that HepLPC-P10 and HepLPC-P3 clustered together, and were close to fetal hepatocytes but separated from PHCs Fig. A and B. CXCL2, CXCL3, CCL2, etc.), interleukins , proteases and regulators , etc.  between different cell states was further investigated. During the conversion of PHCs to HepLPCs, up-and down-regulated genes in HepLPC-P3 significantly overlapped with those in fetal hepatocytes Figure SB. Gene ODynamic changes in chromatin structure and the epigenetic code can affect gene expression and ultimately determine cell state . To idenDiffBind was used to identify DA chromatin regions. 24,637 DA regions were found between PHC and HepLPC-P3, with 12,628 regions showing increased accessibility and 12,009 regions showing decreased accessibility in HepLPC-P3. However, only 1,508 DA regions were found between HepLPC-P3 and HepLPC-P10, with 935 regions showing increased accessibility and 573 regions showing decreased accessibility in HepLPC-P10  and HepLPC-P10 versus HepLPC-P3   were up-regulated and showed increased promoter accessibility, while liver function-related genes  were down-regulated and showed decreased promoter accessibility. In HepLPC-P10, inflammatory response-related genes  were up-regulated and showed increased promoter accessibility, while some cell proliferation-related genes  were down-regulated and showed decreased promoter accessibility. Promoter accessibility of some proliferation-related and inflammatory-related markers is shown in Fig. Considering the direct effect of promoter accessibility on the transcriptional activity of genes, we further focused on differentially accessible promoter elements. DA regions within 3 kb upstream and downstream of transcription start sites (TSS) were considered as promoter regions, and GO-BP analysis was performed. As expected, processes involved in cell growth and proliferation were enriched among the genes with increased promoter accessibility in HepLPC-P3, whereas processes involved in inflammatory responses were enriched among the genes with increased promoter accessibility in HepLPC-P10  and HepLPC-P10 versus HepLPC-P3 (P = 2.04 × 10–11) , whereas only increased promoter (p = 1.02 × 10–6) and distal accessibility (p = 8.81 × 10–5) were significantly correlated with the increased gene expression in HepLPC-P10 versus HepLPC-P3. The lack of a significant association between decreased accessibility and inhibition of target genes in HepLPC-P10 versus HepLPC-P3 might be because the target genes were also activated by factors in the TEM.We then examined the association between chromatin accessibility and gene transcription. DA peaks were found to be located proximal to DEGs in HepLPC-P3 versus PHC  peaks located there Fig. A and B. BATF, FOXM1, NF1, TEAD1, and TEAD2 were found to be enriched and significantly up-regulated in HepLPC-P3 and HepLPC-P10. TEADs have been recognized as key transcription factors of the hippo signaling pathway. Transcriptional coactivators of the hippo signaling pathway, YAP and its paralog TAZ, activate TEAD-mediated transcription and play important roles in organ size control, cell proliferation, and stem cell self-renewal [TEAD1 and TEAD2 may play a critical role in the conversion of PHCs to HepLPCs. However, although TEAD1 and TEAD2 were highly expressed in HepLPC-P3 and HepLPC-P10, long-term culture led to the aging phenotype; the high expression of TEAD1 and TEAD2 in lp-HepLPCs could not maintain cell proliferation. This suggests that other mechanisms are involved in regulating the aging of lp-HepLPCs.To identify DNA binding transcription factors (TFs) that link differential chromatin accessibility and gene expression, motif analysis was performed at distal regions. The top 5 high-expressed known TFs whose motifs enriched in distal regions with increased accessibility in HepLPC-P3/P10 are shown in Fig. -renewal –30. Our To investigate the key TFs regulating the aging of lp-HepLPCs, dynamic changes in chromatin accessibility between the different cell types were analyzed. The chromatin accessibility of HepLPC-P3 was compared with that of PHC and HepLPC-P10, respectively. A total of 418 peaks showed higher accessibility in HepLPC-P3 than in PHC and HepLPC-P10, and 530 peaks showed lower accessibility in HepLPC-P3 than in PHC and HepLPC-P10  knockdown via lentiviral infection to verify its effect on the aging of HepLPCs. HepLPC-P3 were first transfected with the sh-FOSL2 and then compared with sh-GFP as a non-transfected control. After transfection, qRT-PCR showed that the expression of FOSL2 in HepLPC-P3 was significantly decreased  HepLPCs were then passaged to P10. We found that the knowndown of FOSL2 contributed to a partial attenuation of the aging phenotype in HepLPC-P10  is shown in Fig. To validate the expression changes of the core candidate TFs associated with the aging of HepLPCs, qRT-PCR was used. We found that PCs Fig. A. Consid10 Figs. E and 6A,The in vitro conversion of human PHCs into HepLPCs is an effective method to provide a reliable cell source for cell therapy and drug screening . HoweverIn eukaryotes, gene transcription is a complex regulatory network involving the binding of transcription factors, chromatin structure, epigenetic modifications, and even chromosome interactions , 44. As TEAD1 and TEAD2 were unable to maintain the proliferation of HepLPCs. During the aging of HepLPCs, genes proximal to regions of increased distal accessibility in HepLPC-P3 were also enriched in the hippo signaling pathway. However, the gene expression was not significantly related to chromatin accessibility  mouse model improved regeneration and greatly optimized survival. In our study, promoter accessibility of inflammatory factors was found to be increased, while distal accessibility of aging- and inflammatory-related genes was also increased in aged HepLPCs. Studying the binding sites of differential distal accessibility regions is expected to identify the key TFs that regulate the inflammatory responses that lead to HepLPCs aging. Motif analysis revealed enrichment of some inflammatory-related TF binding sequences in regions with decreased and increased distal accessibility in HepLPC-P3  was obtained from residual normal liver tissue after transplantation surgery at our hospital. The Ethics Committee of the Hospital approved the use of this material for research purposes, and informed consent was obtained from all participants. Primary human hepatocytes (PHCs) were isolated from these tissues using our previously reported method . Purified in TEM . The medSA-β-gal staining was used for cell senescence analysis. Cells were fixed in 4% paraformaldehyde and stained using the senescence β-Galactosidase staining kit .−ΔΔCt method and normalized to the housekeeping gene actin beta (ACTB). The sequences of the utilized PCR primers used are listed in Supplementary Table STRIzol reagent  was used to extract total RNA. Then the quantity and quality of the RNA was determined using a SpectraMax Plus 384 enzyme-labeling instrument. Reverse transcription reactions were performed using Prime Script RT Master Mix (Promega) according to the manufacturer’s instructions. In addition, real-time PCR was performed using a LightCycler® 96 Real-Time PCR System (Roche) and SYBR Green PCR kit (Roche). Gene expression was evaluated using the 2p-values ≤ 0.05). ClusterProfiler (V3.18.1) [https://www.gsea-msigdb.org/gsea/index.jsp) [Total RNA was isolated from the cells using a RNeasy mini kit . Paired-end libraries were prepared using the TruSeq® RNA Sample Preparation Kit  according to the manufacturer’s instructions. Transcriptome sequencing was performed on the Illumina platform  and 150 bp paired-end reads were generated. Raw sequencing reads were trimmed to remove low-quality reads and adapters using fastp (version 0.22.0). STAR  was used to map the cleaned reads to the hg19 reference genome (GRCh37) with default parameters. Read counts for each gene were summarized using GFOLD (version V1.1.4). DESeq2 was employed to normalize gene expression and detect differentially expressed genes (|log2(FoldChange)|≥ 1, adjusted dex.jsp)  were usep value less than 0.01 were considered as enriched motifs. The enriched motifs with the expression level of their corresponding TFs and the accessibility of their binding regions changed in the same direction were then identified.ATAC-seq libraries were generated following a previously published protocol . Firstlyp-value ≤ 0.05 was considered to indicate a significant correlation. Next, to investigate the regulation of chromatin accessibility of genes, GO-BP and KEGG pathways were used to perform enrichment analyses using ClusterProfiler (V3.18.1) [To evaluate the association between chromatin accessibility and gene expression changes, the ChIPseeker R package  was usedV3.18.1) .To investigate the regulatory relationship between transcription factors (TFs) and genes, the functional protein association networks STRING  was usedFOSL2 and scrambled shRNA sequences were ligated into the lentiviral backbone LV-U6-PGK/EGFP/T2A/Puro . The packaging plasmids pCMV-VSVG, pMDLg/pRRE, and pRSV-REV were used to transfect the lentivirus into HEK-293 T cells. The supernatant containing the lentiviruses was harvested and filtered through 0.45 μm PVDF filters 48 h after transfection. The virus was concentrated 100-fold by ultracentrifugation  and the virus-containing pellet was resuspended in HBSS.Oligo encoding shRNAs specific for humanFor the comparison of gene expression based on RT-qPCR analyses between two different states, a one-tailed unpaired t-test was used to calculate statistical significance. For the integrative RNA-seq and ATAC-seq analysis, a hypergeometric test was used to determine the significance of overlapping gene sets, and a one-tailed independent t test was used to compare the difference between gene expression foldchanges in differentially accessible promoter and distal regions.https://bigd.big.ac.cn/gsa) (accession number: HRA003006). The transcriptomic data of fetal hepatocytes (GSM1707674 and GSM1707675) were included for gene expression profile analyses.The original data for this study have been submitted to the Genome Sequence Archive (GSA)  at the BAdditional file 1: Figure S1. Functional analysis of DEGs between different cell states, related to Figure 2. Figure S2. Quality assessment of ATAC-Seq data, related to Figure 3. Figure S3. Association of expression levels and chromatin accessibility, related to Figure 3. Figure S4. GO-BP analysis of genes with significantly differential distal accessibility among different cell states, related to Figure 4-5. Figure S5. Protein-protein interaction networks of TFs. Figure S6. Functional analysis of DEGs between sh-FOSL2 affected HepLPC-P10 and ctrl HepLPC-P10, related to Figure 6. Table S1. Primer sequences used for RT-qPCR.


Circular RNAs (circRNAs) are a recently discovered class of RNAs derived from protein-coding genes that have important biological and pathological roles. They are formed through backsplicing during co-transcriptional alternative splicing; however, the unified mechanism that accounts for backsplicing decisions remains unclear. Factors that regulate the transcriptional timing and spatial organization of pre-mRNA, including RNAPII kinetics, the availability of splicing factors, and features of gene architecture, have been shown to influence backsplicing decisions. Poly (ADP-ribose) polymerase I (PARP1) regulates alternative splicing through both its presence on chromatin as well as its PARylation activity. However, no studies have investigated PARP1’s possible role in regulating circRNA biogenesis. Here, we hypothesized that PARP1’s role in splicing extends to circRNA biogenesis. Our results identify many unique circRNAs in PARP1 depletion and PARylation-inhibited conditions compared to the wild type. We found that while all genes producing circRNAs share gene architecture features common to circRNA host genes, genes producing circRNAs in PARP1 knockdown conditions had longer upstream introns than downstream introns, whereas flanking introns in wild type host genes were symmetrical. Interestingly, we found that the behavior of PARP1 in regulating RNAPII pausing is distinct between these two classes of host genes. We conclude that the PARP1 pausing of RNAPII works within the context of gene architecture to regulate transcriptional kinetics, and therefore circRNA biogenesis. Furthermore, this regulation of PARP1 within host genes acts to fine tune their transcriptional output with implications in gene function. Poly (ADP-ribose) polymerase 1 (PARP1) is a highly abundant nuclear enzyme that uses NAD+ to synthesize and catalyze the addition of ADP-ribose polymers to target molecules, namely, histones and itself. The modification of histones with negatively charged PAR molecules disrupts the electrostatic interactions between histones and DNA to open the chromatin structure. This phenomenon is well understood in the context of DNA damage and repair, where PARP1 acts as a guardian or surveyor of the genome . The recThe effect of PARP1 and its PARylation activity on gene expression is pleiotropic, regulating RNA biogenesis at multiple levels . PARP1 cBacksplicing occurs when a downstream splice site is connected to an upstream splice site that circularizes the transcript to form a circular RNA (circRNA) ,13. The All experiments were performed using Drosophila melanogaster S2 cells . The S2 cells were grown in Schneider’s Drosophila media  supplemented with 10% heat-inactivated fetal bovine serum  and 100 U/mL penicillin and 100 µg/mL streptomycin at 25 °C. All experimental samples and controls were matched in terms of growth time and cell density. The knockdown of PARP1 was performed as described in Matveeva et al. . WesternThe S2 cells were pelleted, washed, and resuspended in 0.5 mL RIPA buffer supplemented with 1 mM PMSF , 1X protease inhibitor cocktail , 10 mM 3-MBZ , and 0.5 mM BZA . The cell suspension was incubated on ice for 30 min, gently vortexed, sonicated for 6 cycles (10 s on/10 s off) , and spun down at 13,000 rpm for 10 min. The supernatant was used for Western blot analyses. The total RNA was extracted from the washed and pelleted cells using the Quick-RNA Miniprep Kit (Cat. #R1054) as per the manufacturer’s protocol  and eluted in RNase-free water. The total RNA was submitted  for ribosomal-depleted paired-end RNA sequencing.Protein lysates were prepared in 1X SDS loading buffer, loaded on 10% SDS-PAGE gel, and electrophoresed at 150 V for 45 min. The samples were transferred to PVDF membrane  and subsequently incubated with primary antibodies for PARP1 and β-actin to demonstrate PARP1 knockdown. The Western blot-based detection was performed using alkaline phosphatase-coupled secondary antibodies  with Amersham ECF substrate for visualization , and images were obtained using a Typhoon FLA 9500 . ImageQuant TL software version 10.2 was used to quantify the protein signals.The primary antibodies used were PARP1 C terminal, rabbit polyclonal , PAR rabbit polyclonal , and Actin, mouse monoclonal . The secondary antibodies used were both anti-rabbit (#A3687) and anti-mouse (#A3562) IgG (whole molecule) alkaline phosphatase antibodies .The paired-end raw sequencing files were downloaded in FastQ  format fFor each circRNA, two sets of primers were designed: (1) divergent primers to amplify across the backsplice junction, and (2) convergent primers to amplify a linear region within the circRNA. As exons appear in gDNA in a linear fashion, divergent primers should not amplify with this template . After aDrosophila melanogaster reference genome assembly (BDGP6) using STAR (version 2.6) [The concatenated FastQ sequence files were directly aligned to the ion 2.6) , generation 2.6) . The numion 2.6) . Then, tion 2.6) . The sigion 2.6)  was usedTwo separate methods involving the tools DEXSeq2  and rMATThe most frequent miRNA targets in each experimental circRNA group were identified from miRTarBase (Release 9.0)  relativeThe host gene lengths, circRNA internal exon/intron lengths, flanking intron lengths in each condition , and other Drosophila gene lengths were analyzed using custom Python scripts. The introns flanking the 5′ and 3′ backsplice sites were examined for RBP motifs using rMAPS . The G-qThe RNA-seq data from these experiments are found in GEO # GSE225445. The PARP1-mediated RNA pausing data came from our previous studies on nascent RNA-seq . The KD of PARP1 produced circRNAs from genes with very different architectural features generally compared to circRNAs produced in WT and PARPi conditions. Alu-transposable elements, implicated in circRNA biogenesis, are absent in the Drosophila genome [Sequence Composition: Sequence bias includes G-quadruplex content, inverted repeats, and RNA protein-binding motifs. To begin to understand whether the PARP1 regulation of circRNA biogenesis occurs via cis base pairing of sequences and/or in trans by recruiting RNA-binding proteins (RBPs), we used a comprehensive analysis in determining the sequence bias in the introns flanking the circRNAs. We first asked whether repeat elements (REs), which have also been implicated in circRNA biogenesis, could be important for PARP1-mediated backsplicing and circularization. Since a genome , we focua genome . We obsep < 0.05) enriched in each of the conditions  also mediate the biogenesis of circRNAs by binding to flanking introns in trans and homodimerization via protein–protein interactions . Since Pnditions D. Some onditions D. Since nditions D and onlSince PARP1 is known to bind G-quadruplexes , and G-qGene architecture regulates the movement of RNAPII in transcription to mediate co-transcriptional splicing. We previously showed that PARP1, as an alternative splicing mediator, regulates RNAPII pausing within gene bodies , specifiSince PARP1 regulates both transcription initiation and splicing, we next asked whether PARP1 regulates the types of isoforms from a particular gene. To answer this question, we calculated the circular to linear ratio of the circRNA host genes. For this analysis, we chose circRNAs that were common between the treatment groups and used the direct digital counting of RNA molecules by NanoString technology. This method bypasses the problems of concatemerization during reverse transcription in qRT-PCR A. We desA recent study showed that the differential movement of RNAPII between introns and exons dictates a C:L ratio and ultimately the transcriptional output . To undeOur previous studies ,10 and tThe low expression levels, the shared sequence identity of circRNAs with their cognate linear mRNAs, and the fact that they can only be detected via the backsplice junction reads makes detection and quantification challenging. Though several bioinformatic detection tools have been developed as well as the enrichment of circRNAs after RNAse digestion, the detection and quantification of circRNAs still suffers from low accuracy and high false-positive rates ,56. In oIn general there is no consensus mechanism for the biogenesis of circRNAs . It is wIs PARP1 acting to mediate the time needed for RNAPII to move along the gene body? Just as shown previously, the flanking introns of circRNAs in all conditions were long . In WT, How are circRNAs made in the absence of PARP1? We hypothesize that the downstream pausing observed in the KD conditions compensates for the reduced 3′ flanking intron length. Just like with WT, where the longer introns create a window of opportunity for the time needed for RNAPII to move further into the gene body, and the increased pausing downstream in PARP1 KD equally affords such time needed for circRNAs to be generated. These studies show that PARP1 mediates a chromatin structure that aids in creating the window of opportunity for RNAPII and its associated factors to make the splicing decisions. If this is the case, we analyzed the impact of PARP1-mediated RNAPII movement in host exons and introns on transcriptional output (circular to linear ratio). We used NanoString to measure the circRNA expression alongside the linear expression. We observed that there is a general increase in the pausing in exons. However, when the increase in exon pausing was accompanied by an increase in intron pausing, more circRNAs were made from the gene. On the other hand, when exon pausing was accompanied by decreased intron pausing, more linear transcripts were made from the same gene. Our results are consistent with those of the circular to linear ratio . OverallTaken together, these results show that PARP1 regulates not only regular splicing, but also backsplicing. We also show that the interplay of certain cis features and trans features and PARP1 regulate backsplicing. We further show that by binding to the chromatin structure, PARP1 modulates not only the rate of RNAPII, but also the dynamic movement within different regions of the gene for specific transcriptional output decisions. More studies will be needed to test the direct effect of PARP1 on RNAPII movement in circRNA host gene bodies. These studies, however, provide a first glimpse of PARP1–chromatin function in modulating backsplicing decisions, providing a platform to directly mechanistically examine the factors that mediate this function of PARP1.


We show evidence of the association of RNA polymerase II (RNAP) with chromatin in a core-shell organization, reminiscent of microphase separation where the cores comprise dense chromatin and the shell, RNAP and chromatin with low density. These observations motivate our physical model for the regulation of core-shell chromatin organization. Here, we model chromatin as a multiblock copolymer, comprising active and inactive regions (blocks) that are both in poor solvent and tend to be condensed in the absence of binding proteins. However, we show that the solvent quality for the active regions of chromatin can be regulated by the binding of protein complexes . Using the theory of polymer brushes, we find that such binding leads to swelling of the active chromatin regions which in turn modifies the spatial organization of the inactive regions. In addition, we use simulations to study spherical chromatin micelles, whose cores comprise inactive regions and shells comprise active regions and bound protein complexes. In spherical micelles the swelling increases the number of inactive cores and controls their size. Thus, genetic modifications affecting the binding strength of chromatin-binding protein complexes may modulate the solvent quality experienced by chromatin and regulate the physical organization of the genome. The spatiotemporal organization of the genome is of utmost importance for the normal functioning of cells. The genome of eukaryotic cells is packaged in the chromatin fiber, which is the biomolecular complex made of DNA wound around histone proteins . The chrDrosophila cells, both acetylated chromatin (commonly associated with active genes in euchromatin) and inactive chromatin (commonly associated with heterochromatin) are preferentially found in the periphery of the nucleus and depleted in the central region. Furthermore, at the nuclear periphery they observed a spatial pattern in which regions of chromatin with H3K9ac modifications and regions of high chromatin density were further demixed. Thus both types of chromatin tend to demix from the aqueous phase and then from each other. The experiments also show chromatin regions in which there are no H3K9ac modifications positioned near the lamina in an organization similar to a wetting droplet. In this paper, we address how the organization of active and inactive regions of chromatin can be regulated by the reversible binding of protein complexes. Hereafter, we use the name active for regions of chromatin which are found in regions of lower chromatin density that would commonly be associated with genes that may undergo transcription, It is important to note that this use of active is different from activity as used in the physics literature  on non-equilibrium forces in systems with internal energy sources.More recently, however, in vitro experiments have shown that the globular chromatin structures arising from self-association are related to an intrinsic tendency of chromatin to form liquid-like phases . This phThe observed patterns described above are reminiscent of microphase separation of block copolymers, in which two phases, each enriched in one or the other type of blocks, separate from each other. In contrast to a system undergoing macrophase separation, the microphases do not fully coarsen due to the chain connectivity and the length scales defining the spatial patterns are determined by the size of the blocks and their intrinsic properties, such as self-attraction and bending rigidity . Chromatlateral phase separation of nucleosome-rich and nucleosome-poor regions (Drosophila larvae muscle fibers), which exhibit peripheral chromatin organization and additionally exhibit microphase separation since the chromatin domains do not fully coarsen  and shows that the relatively uncondensed nature of active chromatin can be regulated by reversible binding of protein complexes, resulting in an inner core of condensed (inactive) chromatin connected to a corona of relatively uncondensed (active) chromatin to which protein complexes are bound. We predict the relative sizes and organizations of these regions into a structure that contains an assembly of such core-shell micelles. Those are all connected and thus localized in space since they arise from the block copolymer-like structure of the chromatin with regions of acetylated and non-acetylated  chromatin. Interestingly, the binding of protein complexes can lead to changes in the organization of both, active and inactive chromatin regions, despite protein complexes interacting only with the active regions.Our manuscript is organized as follows. We first show observations of a core-shell organization of chromatin and RNA Pol II. Inspired by these observations, we introduce our minimal model of regulation of microphase separation by the binding of protein complexes and discuss the equilibrium conditions in the system. We then discuss analytical and simulation results, which show that the binding of protein complexes to chromatin can regulate the solvent quality for the active chromatin regions. We conclude by discussing our findings in the context of chromatin organization in the nucleus and briefly comment on an extension to non-equilibrium binding of protein complexes to chromatin.Drosophila cells organizes peripherally, with a chromatin-depleted central region in the nucleus  and inactive (B) blocks, see solvent) , we modesolvent) . In genesolvent) . In Figusolvent)  with shasolvent) .In order to simplify our discussion, we focus on the layer geometry shown in The core composed of inactive blocks and some solvent has an interfacial area, artments . We now and the conservation of B monomers byIn the expression for the free energy of the core, The spatial extent of the active shell region is determined by the extension of the loops of active blocks, The first contribution is the mixing entropy of the solvent molecules and the protein complexes, which is given byThe second contribution is the decrease in free energy due to the binding of protein complexes to the active blockshere, the first and second terms are entropic contributions associated with the number of available active monomers to which a protein complex can bind, and the third term is the energy gain, where we introduced the interaction between active monomers, omplexes . Moreoveomplexes . Hence, omplexes . While aThe free energy we consider in The conservation of active monomers in the shell is expressed byIn writing the free energy of the brush as in The bulk solution of protein complexes occupies a volume where The osmotic pressure of the bulk solution iswhere we used the relation The total energy of the system, where The equalities of the exchange chemical potentials of protein complexes in the system can be expressed aswhere the exchange chemical potential of the free protein complexes isand the exchange chemical potential of the bound protein complexes is given byThe third condition is the equality of the osmotic pressures in the shell region and bulk solutionwhere the osmotic pressure in the shell, The free energy in the core-shell region also varies with changes in the interfacial area The equilibrium properties of the system are then found by simultaneously solving We first focus on the effect that protein complexes have on the stretching of the polymer brush made of active chromatin blocks; hereafter, we refer to such polymer brush as the active chromatin brush. In order to find analytical solutions, we work in the limit of dilute protein complexes in (i) the bulk where we introduced the binding parameter The equality of the osmotic pressures in the shell region and in the bulk solution where we introduced the interfacial area per block and an effective third virial coefficientIt is important to note that in our model the second and third virial coefficients can be tuned by the binding of protein complexes. Both virial coefficients increase with ectively . HoweverWe discuss below the different parameter regimes and find approximate solutions for the volume fraction of the active chromatin brush In the weak binding limit, the self-attraction of active chromatin blocks is reduced by the weak binding of protein complexes to the active monomers, but not enough to change the solvent of the active chromatin brush from a poor one to a good one. In this weak binding regime, characterized by and to an extension of the active chromatin brushThese solutions show that the volume fraction of monomers in the shell is reduced by the presence of protein complexes bound to active chromatin compared to a system without protein complexes. In the poor solvent regime, the extension of the active brush scales linearly with the number of monomers , similarWhen the protein complexes strongly bind to the active monomers, characterized by which predicts an extension of the active brush:This is equivalent to the scaling of the height of a polymer brush in a good solvent in a mean field approximation We have shown that depending on the binding strength or concentration of the protein complexes, the active chromatin brush can experience poor or good solvent conditions. The transition between the two occurs at the so-called theta pointde , where tand that the extension of the brush isIf we compare the three regimes in which the brush experiences poor, good, and theta solvent conditions, we find that in all of them, the brush height scales linearly with the number of active monomers in a block, but with different scalings as a function of the surface per block, σc, σe and We now explore the effect of the binding of protein complexes on the size of the inactive core in the different solvent regimes. We begin by solving the equation for the osmotic pressure balance that determines the interfacial area where we introduced the dimensionless interfacial tension, and finally, in the theta solvent regime the surface area per block isWe find that the surface area per block increases with the protein concentration in bulk solution and the binding energy  chromatin domains as we discuss above. It has also been shown recently in In this work, we have provided a generic description at the mesoscale, that is independent of the microscopic details that determine chromatin microphase separation. Instead, we suggest a minimal mechanism that can play a role in regulating the microphase separation between active and inactive chromatin regions. Although this has been previously observed experimentally , a modelIn addition to the observations provided in this article for chromatin being in a microphase-separated state, previous work  has focuOur equilibrium approach can be extended by considering non-equilibrium effects such as acetylation and methylation of the histone tails of nucleosomes, which would lead to different sizes of active and inactive chromatin blocks. The simplest non-equilibrium scenario, would be one where the fraction of protein complexes bound to chromatin, In order to model the chromatin system, we use the following potential energies. The stretching energy of the chromatin chain is provided by harmonic springs that connect adjacent beads and is given bywhere Non-bonded interactions between chromatin-chromatin and chromatin-protein beads are modeled using the standard Lennard-Jones potentials (LJ),rc refers to a cutoff distance beyond which LJ interaction is set to zero. Here, for all 0.3kBT . Chromatfor all where rc at which the LJ interaction also vanishes; therefore A major feature of the chromatin-protein complex interaction in our model is the bonding between active beads and protein complex beads. This depends on the availability of these proteins and the nature of their binding to the active chromatin. For the simplicity, we consider a total of software  and writfor all 7 time-steps that is much longer than the time it takes for the system to reach a steady state with constant mean energy and radius of gyration.To simulate the chromatin system, we performed Brownian dynamics simulations using the LAMMPS molecular dynamics package . The sim This fundamental work substantially advances our understanding of polymer physics underpinnings of genome folding, organization, and regulation. The conclusions are supported by both convincing computer simulations and analytical theory. The work will be of significant interest to the genome folding community. 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 Aleksandra Walczak as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Alexander Y. Grosberg (Reviewer #2).Thank you for submitting your article "Regulation of chromatin microphase separation by binding of protein complexes" 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) Explain to the reader the non-trivial and counter-intuitive character of their central assumption – that RNAPs, although attracted to DNA, make solvent effectively better, not worse.Reviewer #1 (Recommendations for the authors):1. The authors should additional data for the existence of the micro-domains studied in this work and for the dependence of their sizes on at least one parameter that also enters the theoretical analysis.2. Improve the explanation of physics concepts for biologists. This does not mean repeating passages from textbooks, but it should have the aim of providing intuition of various physical expressions to biologists.Reviewer #2 (Recommendations for the authors):As a polymer physicist, first and foremost, I encourage authors to clarify the confusion which may or may not be purely terminological, but definitely precludes a complete understanding of the work. The issue is most obvious around Equation 20, where authors explicitly claim that the negative second virial coefficient corresponds to the good solvent regime, while the positive one to the poor solvent. This is the exact opposite of the commonly accepted nomenclature, as any polymer physics textbook can confirm .My second recommendation is to take into account the fact that presently a lot of work is being done on the statistical physics of the so-called active systems. Due to this, when authors mention that some parts of the chromatin are active – the implication is that they are consuming energy by some mechanism, which would then preclude the use of equilibrium statistical mechanics. I encourage authors to clean up the terminology appropriately.My third recommendation is to pay significantly more attention to the discussion of applicability conditions of the (many!) approximations adopted in the work. This includes the assumption that core density is fixed and is not affected by proteins as well as the assumption that the shell phase consists of loops (starting from a core and coming back to the same core) rather than numerous strands connecting different cores.My fourth recommendation is to consider framing the work in terms of the strong segregation limit of block copolymers  instead of referring to the seminal papers treating the weak segregation limit. I think that the work really considers structures with sharp interfaces between microphases.My fifth recommendation for the authors is to emphasize the nature of their assumption about the role of proteins in changing interactions between parts of the chromatin fiber. The usual situation with DNA-binding proteins is that they are positively charged. As such, they can usually serve as cross-links or condensing agents for DNA, as evidenced by the example of DNA behavior in presence of even three- or four-valent ions (such as spermine and spermidine). Authors assume that RNA polymerase complexes do something almost entirely opposite to the fiber, they attach to one place on the fiber and somehow generate the repulsion from another section . This is a highly unusual assumption that is at the heart of the suggested model and should be properly stated and explained.Reviewer #3 (Recommendations for the authors):The paper is written very clearly. But please explain in more detail two points:2 in Equation 5: Do I understand right that this factor means that you assume that monomers that are bound by proteins are not attracted to each other anymore? This, I guess, might be the crucial place where you change the effective solvent quality of the active blocks through the binding of the proteins. If so, this should be explicitly stated close to the formula and a rationalization of this assumption should be provided.1) The factor (1-\Omega)2) On page 13 you write: "When any two beads of the polymer approach each other within a distance 2.5 \σ a […], they are attracted by a truncated LJ potential." For me this sounded like all monomers interact with the same strength which then made the micellar structure observed on top of Fig, 5A puzzling. Only after reading the appendix, it became clear to me that inactive beads attract each other stronger. This (namely that inactive monomers have a stronger attraction to each other) should simply be mentioned already in the main text. Essential revisions:Reviewer #1 (Recommendations for the authors):1. The authors should additional data for the existence of the micro-domains studied in this work and for the dependence of their sizes on at least one parameter that also enters the theoretical analysis.The image is a representative image and we have more evidence of RNA Pol II organizing at the periphery of dense chromatin regions. We include a new figure in Appendix 1 showing that the coreshell organization is also observed in salivary glands. There is published evidence in Hilbert, et al. Nat. Comm. 2021, showing that dissociation of protein complexes (RNA Pol II and transcription factors) from the chromatin fiber leads to the coarsening of chromatin domains, consistent with our model predictions. We refer to this in the discussion and write the following in line 567:“In Hilbert et al. (2021), the authors show that transcription organizes chromatin in microphase separated states, with RNA Pol II connecting an RNA-rich phase with a chromatin-rich phase. In that study, drug treatments that lead to unbinding of RNA Pol II from chromatin result in an increase in the sizes of high density chromatin domains; notably, those domains are fewer in number than in the wild type and they do not seem to fully coarsen. This is in agreement with our minimal theory, where in the absence of protein complexes, the dense cores become larger but not fully coalesce due to the selectivity of the solvent. Another treatment they used, stopped transcription but did not lead to the unbinding of RNA Pol II from chromatin; in those cells, they still observed a similar distribution of dense chromatin domains, pointing to the fact that chromatin remains in its microphase separated state despite the fact that there are no active processes regulating it.”2. Improve the explanation of physics concepts for biologists. This does not mean repeating passages from textbooks, but it should have the aim of providing intuition of various physical expressions to biologists.We thank the reviewer for this suggestion. We have now provided other explanatory remarks in the revised version of our manuscript, as follows:Lines 252 to 274v, which we consider to be attractive (in the absence of protein binding), that is v < 0. One core assumption in our model is that the presence of bound protein complexes reduce the effective attraction between active monomers. The rationale is based in the following: The protein complexes that bind to active chromatin tend to be relatively large, , in which a myriad of proteins assemble jointly with RNA Pol II in order to initiate transcription, which can lead to steric repulsion due to the large macromolecular volume of such complexes Hahn (2004). Moreover, while RNA Pol II interacts with the DNA in the chromatin fiber via positively charged patches at its surface, most of its surface is negatively charged Cramer et al. (2001). Hence, we assume that when RNA Pol II is bound to the chromatin fiber, those Pol II regions that are negatively charged would effectively provide a repulsive force within the chromatin fibers. Another possible explanation is that in the vicinity of active monomers, the transcription complex carries mRNA, which is known to segregate from chromatin Hilbert et al. (2021). While a more thorough analysis of the molecular interactions would be interesting, it is out of the scope of our paper that uses a coarse-grained, polymer physics approach. This approach also allows our model to be predictive as to the physical organization and growth of the domains, independent of those molecular details that are as yet unknown.“…where we introduced the interaction between active monomers, The free energy we consider in Equation 5 is appropriate in the limit that the surface density of the contacts between active and inactive blocks always remains relatively high; therefore active blocks are heavily influenced by the presence of neighboring blocks. In this limit, the system behaves like a polymer brush. In the other limit that the surface density of contacts of the active and inactive brushes is sufficiently low, the appropriate expression would be that of isolated mushrooms in which each block is independent of each other and their description is equivalent to that of single polymers which obey simple scaling laws, depending on the solvent quality Szleifer (1996).”Lines 330 to 339G ∼N3/5, RG ∼N1/2, and RG ∼N1/3, correspond to good, theta, and poor solvent regimes, respectively Rubinstein et al. (2003). However in the case of polymer brushes, the scaling laws also depend on the grafting density of such polymers. In our model, only the active monomers experience different solvent regimes depending on whether the protein complexes bind to them and their surface density is fixed from a balance of the different free energy contributions in the system. This self-assembling situation differs from a polymer brush in which the surface density is considered to be fixed.”“In general, the different solvent regimes correspond to different scaling properties of the radius of gyration of single homopolymers in solution as a function of the number of monomers in the polymer. The different scaling regimes RReferring to a theta solvent, we added some text between lines 378 to 380“…at the so called theta point de Gennes (1979), where the second virial coefficient in the system vanishes  and the active blocks behave as if they were ideal polymer chains.”Reviewer #2 (Recommendations for the authors):As a polymer physicist, first and foremost, I encourage authors to clarify the confusion which may or may not be purely terminological, but definitely precludes a complete understanding of the work. The issue is most obvious around Equation 20, where authors explicitly claim that the negative second virial coefficient corresponds to the good solvent regime, while the positive one to the poor solvent. This is the exact opposite of the commonly accepted nomenclature, as any polymer physics textbook can confirm .We thank the referee for pointing out the inconsistency with the polymer physics literature. This inconsistency stems from the fact that we explicitly wrote a minus sign in the definition of the interaction between monomers belonging to what we called active blocks in order to emphasize their attractive nature. In the revised version, we rewrote the equations in a manner that is consistent with the established literature. Namely we defined negative, zero and positive virial coefficients corresponding to poor, theta and good solvent conditions.To keep our nomenclature in line with the polymer physics literature, we made changes in Equations 5, 19, 22, 24, 25, 29, 31, 32, 35 and 36.My second recommendation is to take into account the fact that presently a lot of work is being done on the statistical physics of the so-called active systems. Due to this, when authors mention that some parts of the chromatin are active – the implication is that they are consuming energy by some mechanism, which would then preclude the use of equilibrium statistical mechanics. I encourage authors to clean up the terminology appropriately.The biology literature refers to active and inactive chromatin, and generally denote euchromatin (less condensed) and heterochromatin (more condensed) regions. The less condensed regions correspond to parts of the chromatin that can be transcribed whereas those that are more condensed are less or not transcribed at all. Thus, active and inactive in this literature refer to transcription and not necessarily to self-propelled systems etc. that are the concern of “active matter physics”. It is true, however, that energy consumption via ATP may be involved in transcription; but this is outside the level of description of our polymeric model. To prevent confusion, we have now added the following clarification when the terms are introduced in the manuscript to avoid confusion. In line 68 we write:“Hereafter we use the name active for regions of chromatin which are found in regions of lower chromatin density that would commonly be associated with genes that may undergo transcription, It is important to note that this use of active is different from activity as used in the physics literature  on non-equilibrium forces in systems with internal energy sources.”Our use of the terms active and inactive is consistent with the nomenclature used by different authors in the biological and biophysical literature who also denote active and inactive compartments, for example, Cremer, et al., BioEssays 42, 2020 and Hildebrand and Dekker, Trends Biochem Sci. 2020 May; 45(5): 385–396.My third recommendation is to pay significantly more attention to the discussion of applicability conditions of the (many!) approximations adopted in the work. This includes the assumption that core density is fixed and is not affected by proteins as well as the assumption that the shell phase consists of loops (starting from a core and coming back to the same core) rather than numerous strands connecting different cores.In the revised paper we point out the approximations more explicitly. For example, the approximation of protein complexes being unable to penetrate the dense chromatin cores due to its high density, is inspired by the experimental observations of the lack of penetration of RNA Pol II to the dense chromatin cores as shown in Figure 1. There is further evidence showing that large particles cannot diffuse within inactive chromatin domains, shown by Gelléri, et al., bioRxiv 2022.03.23.485308, 2022. In line 207 we included the following text:“Our assumption that protein complexes only bind to active monomers stems from the observed lack of penetration of RNA Pol II into the dense chromatin cores as shown in Figure 1. Additionally, there is further evidence showing that large particles cannot diffuse into inactive chromatin domains Gelléri et al. (2022).”We also state more clearly that our approximation of a fixed concentration of the core which contains the inactive chromatin blocks, is appropriate when the interactions between heterochromatic domains is the largest energy scale in the system, compared to the protein binding, polymer entropy etc. This is also related to a finding in Gelléri, et al., bioRxiv 2022.03.23.485308, 2022, where the chromatin density in heterochromatic domains is much higher than that of chromatin found in active compartments . We write in line 226:“Such an approximation is motivated by the fact that dense chromatin domains are known to have much higher concentrations that chromatin found in transcriptionally active compartments Gelléri et al. (2022).”Regarding considering the loop conformations instead of bridging between different micelles, the main reason to consider such a scenario is to simplify the calculations; we do not know the exact conditions in the nucleoplasm but we expect the qualitative trends of our model to apply independent of the as yet unknown details of the looping.My fourth recommendation is to consider framing the work in terms of the strong segregation limit of block copolymers  instead of referring to the seminal papers treating the weak segregation limit. I think that the work really considers structures with sharp interfaces between microphases.We thank the reviewer for referring us to the seminal work by Semenov. We now cite it appropriately and mention that our model considers the strong segregation limit with sharp interfaces. In line 201 we write:“Our model considers microphase separation in the strong segregation limit (Semenov (1985), Ohta and Kawasaki (1986)) with sharp interfaces, the strong incompatibility arises here from the strong selectivity of the solvent, in which inactive blocks experience very strong compaction, reminiscent of the much higher density of DNA in inactive compartments.”My fifth recommendation for the authors is to emphasize the nature of their assumption about the role of proteins in changing interactions between parts of the chromatin fiber. The usual situation with DNA-binding proteins is that they are positively charged. As such, they can usually serve as cross-links or condensing agents for DNA, as evidenced by the example of DNA behavior in presence of even three- or four-valent ions (such as spermine and spermidine). Authors assume that RNA polymerase complexes do something almost entirely opposite to the fiber, they attach to one place on the fiber and somehow generate the repulsion from another section . This is a highly unusual assumption that is at the heart of the suggested model and should be properly stated and explained.We thank the reviewer for highlighting the fact that the interactions that we describe are different in essence than that of cross-bridging molecules such as spermine and spermidine. One reason for this is that chromatin consists of DNA and positively charged histones (with hydrophobic tails) and in the cell, with additional chromatin-binding proteins. Chromatin is known to condense in aqueous solvent  and has been shown  to phase separate from the nucleoplasm. This means that water is a poor solvent for chromatin – in contrast to DNA. In our model, the binding of protein complexes to the chromatin disrupts the self-attraction of the chromatin and effectively results in good solvent conditions for those parts of the chain to which the protein complexes have bound. The less condensed nature of transcriptionally active chromatin (to which Pol-II binds) allows us to treat that part of the chromatin as being in good solvent conditions. The treatment of the electrostatics is more complex than for DNA and multivalent proteins since the histone proteins, histone tails, and other associated proteins must be taken into account. We therefore coarse grain all of these effects by distinguishing only between the poor solvent conditions of the transcriptionally inactive chromatin (to which the Pol-II does not bind) and the relatively good solvent conditions of transcriptionally active chromatin to which Pol-II binds. In addition, the main difference between the two molecules, spermine and spermidine and RNA Pol II, among others, is that the surface of RNA Pol II is mainly negatively charged, but with some patches of positive charges that allow for the specific association of RNA Pol II with DNA . Our model seems to be closely related to the observations made in Hilbert, et al. Nat. Comm. 2021, in which the association of RNA Pol II (and very likely other factors involved in transcription) prevent coarsening of high density chromatin domains even in the absence of transcription. We include a clarification of this effect in the description of our model in line 253:“One core assumption in our model is that the presence of bound protein complexes reduce the effective attraction between active monomers. The rationale is based in the following: The protein complexes that bind to active chromatin tend to be relatively large, , in which a myriad of proteins assemble jointly with RNA Pol II in order to initiate transcription, which can lead to steric repulsion due to the large macromolecular volume of such complexes Hahn (2004). Moreover, while RNA Pol II interacts with the DNA in the chromatin fiber via positively charged patches at its surface, most of its surface is negatively charged Cramer et al. (2001). Hence, we assume that when RNA Pol II is bound to the chromatin fiber, those Pol II regions that are negatively charged would effectively provide a repulsive force within the chromatin fibers. Another possible explanation is that in the vicinity of active monomers, the transcription complex carries mRNA, which is known to segregate from chromatin Hilbert et al. (2021). While a more thorough analysis of the molecular interactions would be interesting, it is out of the scope of our paper that uses a coarse-grained, polymer physics approach. This approach also allows our model to be predictive as to the physical organization and growth of the domains, independent of those molecular details that are as yet unknown.”Reviewer #3 (Recommendations for the authors):The paper is written very clearly. But please explain in more detail two points:2 in Equation 5: Do I understand right that this factor means that you assume that monomers that are bound by proteins are not attracted to each other anymore? This, I guess, might be the crucial place where you change the effective solvent quality of the active blocks through the binding of the proteins. If so, this should be explicitly stated close to the formula and a rationalization of this assumption should be provided.1) The factor (1-\Omega)We are grateful that the referee appreciated a crucial feature of our model: the chromatin monomers, in the absence of protein, are self-attractive (are in poor solvent) and the protein binding disrupts this selfattraction, resulting in relatively good solvent conditions for the parts of the chain bound to the Pol II proteins. We expanded our description of the model and make this assumption more explicit in line 252 we write:v, which we consider (in the absence of protein binding) to be attractive, that is v < 0. One core assumption in our model is that the presence of bound protein complexes reduce the effective attraction between active monomers. The rationale is based in the following: The protein complexes that bind to active chromatin tend to be relatively large, , in which a myriad of proteins assemble jointly with RNA Pol II in order to initiate transcription, which can lead to steric repulsion due to the large macromolecular volume of such complexes Hahn (2004). Moreover, while RNA Pol II interacts with the DNA in the chromatin fiber via positively charged patches at its surface, most of its surface is negatively charged Cramer et al. (2001). Hence, we assume that when RNA Pol II is bound to the chromatin fiber, those Pol II regions that are negatively charged would effectively provide a repulsive force within the chromatin fibers. Another possible explanation is that in the vicinity of active monomers, the transcription complex carries mRNA, which is known to segregate from chromatin Hilbert et al. (2021). While a more thorough analysis of the molecular interactions would be interesting, it is out of the scope of our paper that uses a coarse-grained, polymer physics approach. This approach also allows our model to be predictive as to the physical organization and growth of the domains, independent of those molecular details that are as yet unknown.”“…where we introduced the interaction between active monomers, 2) On page 13 you write: "When any two beads of the polymer approach each other within a distance 2.5 \σ a […], they are attracted by a truncated LJ potential." For me this sounded like all monomers interact with the same strength which then made the micellar structure observed on top of Fig, 5A puzzling. Only after reading the appendix, it became clear to me that inactive beads attract each other stronger. This (namely that inactive monomers have a stronger attraction to each other) should simply be mentioned already in the main text.We thank the reviewer for this comment, and agree that we should have definitely mentioned the stronger attraction of inactive beads in the main text already. We have added the following text in line 192 when introducing the model:“Attractions between inactive monomers are taken to be larger than those between active monomers and the cross interaction of active and inactive monomers”.List of changes marked in red in the revised manuscript.In line 17 we added “and transcription factors”In line 68 we added “Hereafter we use the name active for regions of chromatin which are found in regions of lower chromatin density that would commonly be associated with genes that may undergo transcription, It is important to note that this use of active is different from activity as used in the physics literature  on non-equilibrium forces in systems with internal energy sources.”In the caption of Figure 1. we added “Representative image”.Drosophila larva”In line 166 we added “In Appendix 1 we show another representative confocal image of the distribution of chromatin and RNA Pol II in a salivary gland of an intact In line 201 we added “Our model considers microphase separation in the strong segregation limit Semenov (1985); Ohta and Kawasaki (1986) with sharp interfaces, the strong incompatibility arises here from the strong selectivity of the solvent, in which inactive blocks experience very strong compaction, reminiscent of the much higher density of DNA in inactive compartments.”In line 207 we added “Our assumption that protein complexes only bind to active monomers stems from the observed lack of penetration of RNA Pol II into the dense chromatin cores as shown in Figure 1. Additionally, there is further evidence showing that large particles cannot diffuse into in active chromatin domains Gelléri et al. (2022).”In line 226 we added “Such an approximation is motivated by the fact that dense chromatin domains are known to have much higher concentrations that chromatin found in transcriptionally active compartments Gelléri et al. (2022).”v, which we consider (in the absence of protein binding) to be attractive, that is v < 0. One core assumption in our model is that the presence of bound protein complexes reduce the effective attraction between active monomers. The rationale is based in the following: The protein complexes that bind to active chromatin tend to be relatively large, , in which a myriad of proteins assemble jointly with RNA Pol II in order to initiate transcription, which can lead to steric repulsion due to the large macromolecular volume of such complexes Hahn (2004). Moreover, while RNA Pol II interacts with the DNA in the chromatin fiber via positively charged patches at its surface, most of its surface is negatively charged Cramer et al. (2001). Hence, we assume that when RNA Pol II is bound to the chromatin fiber, those Pol II regions that are negatively charged would effectively provide a repulsive force within the chromatin fibers. Another possible explanation is that in the vicinity of active monomers, the transcription complex carries mRNA, which is known to segregate from chromatin Hilbert et al. (2021). While a more thorough analysis of the molecular interactions would be interesting, it is out of the scope of our paper that uses a coarse-grained, polymer physics approach. This approach also allows our model to be predictive as to the physical organization and growth of the domains, independent of those molecular details that are as yet unknown.In line 252 we added “where we introduced the interaction between active monomers, ”The free energy we consider in Equation 5 is appropriate in the limit that the surface density of the contacts between active and inactive blocks always remains relatively high; therefore active blocks are heavily influenced by the presence of neighboring blocks. In this limit, the system behaves like a polymer brush. In the other limit that the surface density of contacts of the active and inactive brushes is sufficiently low, the appropriate expression would be that of isolated mushrooms in which each block is independent of each other and their description is equivalent to that of single polymers which obey simple scaling laws, depending on the solvent quality Szleifer (1996)RG ∼N3/5, RG ∼N1/2, and RG ∼N1∕3, correspond to good, theta, and poor solvent regimes, respectively Rubinstein et al. (2003). However in the case of polymer brushes, the scaling laws also depend on the grafting density of such polymers. In our model, only the active monomers experience different solvent regimes depending on whether the protein complexes bind to them and their surface density is fixed from a balance of the different free energy contributions in the system. This self-assembling situation differs from a polymer brush in which the surface density is considered to be fixed.”In line 331 we added “In general, the different solvent regimes correspond to different scaling properties of the radius of gyration of single homopolymers in solution as a function of the number of monomers in the polymer. The different scaling regimes In line 380 we added “and the active blocks behave as if they were ideal polymer chains.”In line 490 we added “block copolymer”N beads are divided between periodic blocks of active and inactive monomers. Attractions between inactive monomers are taken to be larger than those between active monomers and the cross interaction of active and inactive monomers. This gives rise to the core-shell structure observed in the experiments, shown in Figure 1.”In line 493 we added “These In line 497 we added “interact”.In lines 570 to 576 we added “drug treatments that lead to unbinding of RNA Pol II from chromatin result in an increase in the sizes of high density chromatin domains; notably, those domains are fewer in number than in the wild type and they do not seem to fully coarsen. This is in agreement with our minimal theory, where in the absence of protein complexes, the dense cores become larger but not fully coalesce due to the selectivity of the solvent. Another treatment used, stopped transcription but did not lead to the unbinding of RNA Pol II from chromatin, in those cells, they still observed a similar distribution of …”.We modified Equations 5, 19, 22, 24, 25, 29, 31, 32, 35 and 36 and changed the inequalities in lines 330, 331, 349, 364,Drosophila salivary gland epithelium.We added Appendix I with the title RNA-Poll II distribution in live We made small modifications to the table appearing in Appendix 5, to keep the consistency with the virial coefficients definitions.


To analyze the morphological and functional characteristics of primary macronodular adrenal hyperplasia (PMAH) nodules carrying or not carrying ARMC5 mutations and the consequences of the presence of mutations in terms of the pattern of macronodule composition and functional state.ARMC5 gene. The analyses were performed by hematoxylin-eosin staining, immunohistochemistry, microdissection of spongiocyte tissue and RT-qPCR of histological sections from 16 patients diagnosed with PMAH with germline (5) or germline/somatic mutations (5) and without mutations (6) in the  Hyperplastic nodules were predominantly composed of spongiocytes in mutated and nonmutated sections. ARMC5 mRNA expression in spongiocytes was higher in ARMC5-mutated nodules than in ARMC5-nonmutated nodules, and homogenous ARMC5 protein distribution was observed. The presence of arginine-vasopressin receptor (AVP1AR) and ectopic ACTH production were observed in both cell populations regardless of ARMC5 mutations; the numbers of serotonin receptor (5HT4R)- and proliferating cell nuclear antigen (PCNA)-positive cells were higher in macronodules carrying ARMC5 mutations than in those without mutations.ARMC5 gene, which may be relevant in understanding the pathogenesis and diagnosis of patients with PMAH. Our results suggest that the presence of ARMC5 mutations does not interfere with the pattern of distribution of spongiocytes and compact cells or with the presence of AVP1AR, gastric-inhibitory polypeptide receptor (GIPR) and ectopic ACTH. Nevertheless, the higher numbers of PCNA-positive cells in mutated nodules than in nonmutated nodules suggest that mutated ARMC5 can be related to higher proliferation rates in these cells. In conclusion, our results provide more information about the crosstalk among abnormal GPCRs, ectopic ACTH in steroidogenesis and the  The cel in vivo -6.ARMC5) gene were described in different cohorts of potentially sporadic and familial cases of PMAH  without mutations in the ARMC5 gene, with a mean age of 57.0 ± 6.8 years, an average baseline urinary cortisol of 14.7 ± 6.3 µg/dL and a range of plasmatic ACTH between < 2 pg/mL and 16.9 pg/mL; one of these patients was diagnosed with overt Cushing’s syndrome (CS), and five patients were diagnosed with subclinical hypercortisolism. Group 2 consisted of 10 patients  with ARMC5 germline or germline and somatic mutations, a mean age of 51.1 ± 8.2 years, an average baseline urinary cortisol of 22.3 ± 6.3 µg/dL and an ACTH of < 5 pg/mL and CS diagnosis. The diagnosis of overt Cushing’s syndrome was defined by autonomous cortisol secretion after the dexamethasone suppression test (DST) and/or an increased 24-hour urinary cortisol and/or suppressed ACTH levels accompanied by clinical signs such as proximal myopathy, skin fragility and facial plethora. Subclinical hypercortisolism was established when the patient presented abnormal cortisol levels with autonomous serum cortisol secretion after DST without the clinical signs of Cushing’s syndrome. The clinical data from all patients are shown in For this study, we selected 16 nonrelated patients diagnosed with PMAH . The secARMC5. The sections were stained with hematoxylin and eosin (HE)  and were analyzed with a Nikon light microscope  by using Neurolucida software. Histological sections were submitted to a deparaffinization and rehydration protocol followed by staining with HE. The total area of the sections was delineated, and the area of compact cells was randomly analyzed in relation to the total area. The spongiocytes were quantified by subtracting the compact area from the total section area.The spongiocytes and compact cells of hyperplastic nodules were quantified in sections of PMAH nodules with or without germline and/or somatic mutations in 2O2 (1:1) were used for 20 minutes, followed by blockade of nonspecific sites with phosphate-buffered saline containing 0.1% Tween 20 (PBST) and 5% horse serum  for 1 hour at room temperature (RT). After blockade, the sections were incubated overnight at 4°C with a primary antibody from Abcam  or Santa Cruz  at the following concentrations: anti-StAR (1:200), anti-3βHSD2 (1:250), anti-PCNA (1:100), anti-ARMC5 (1:150), anti-ACTH (1:50), anti-CYP17A1 (1:100), anti-AVP1AR (1:50), anti-GIPR , and anti-5HT4R (1:50) diluted in PBST containing 5% serum in a humid chamber. Next, the sections were incubated with a universal biotinylated secondary antibody for 1 hour at RT, followed by incubation with the ABC Vectastain Kit AB (1:100) complex for 1 hour at RT. Finally, the sections were incubated with 3,3’-diaminobenzidine (DAB)  and counterstained with hematoxylin.Sections (7 µm) of formalin-fixed paraffin-embedded adrenal nodule tissue were obtained for immunohistochemistry reactions. For antigenic recovery, immersion in citric acid (pH 6.0) or Tris-EDTA buffer (pH 9.0) was used for 30 minutes at 96 °C. For blockade of endogenous peroxidase, methanol and HThe images were captured with a light field microscope with a 20x and a 40x objective lens . Five images of each patient were randomly selected and analyzed by ImageJ 1.43j software . By using the hematoxylin and DAB (HDAB) built-in vector, the images were convoluted in three parts as follows: hematoxylin, DAB and background. Only the DAB-stained images were selected and, when submitted to the threshold filter, converted into a binary image. The software calculated the percentage of area stained by DAB using the same parameters for each image.®, LCM0522). This technique was performed on a Nikon Eclipse® Ti-E inverted microscope with a computerized Arcturus® AutoScanXT™ system, which allows for the identification of areas to be microdissected. After locating the cells of interest, a CapSure® LCM Cap was placed over the target area. The laser pulsed through the cap forming a thin protrusion that bridged the gap between the cap and tissue and adhered to the target cell. Lifting the cap transferred the target cells attached to the cap to a tube containing an RNA extraction buffer previously heated to 42 °C. The obtained material was centrifuged for 2 minutes at 12,300 rpm and stored at -80°C. Total RNA was extracted using the PicoPure® RNA Isolation Kit, and cDNA was synthesized with SuperScript III First Strand Synthesis Supermix . The cDNA obtained was amplified by the SYBR Green method, and real-time PCR (qPCR) was used to analyze the relative gene expression levels of ARMC5, 3BHSD2, CYP17A1, CYP11B1, 5HT4R, GIPR, and AVPRV1a. The GUSB gene was used as an endogenous control, and the relative expression was analyzed using the 2- method, where ΔCT is the difference between CT values selected from a given sample, and CT is from a commercial pool of human normal adrenal tissues . The primers used in the qPCR experiments are described in Frozen sections of PMAH tissue fragments were mounted on slides suitable for the ArcturusXT™ laser microdissection system (LCM) . Statistical significance was determined using a paired Student’s t-test, and for more than two groups, ANOVA was used. The results were considered statistically significant when p < 0.05.ARMC5 in the mutated and nonmutated PMAH patient tissues (ARMC5 mRNA than those in sections from nonmutated PMAH patients (ARMC5 showed more PCNA-positive cells than those not carrying mutations (p = 0.0005), with percentages of 14.7 1.3% and 7.2 ± 1.3%, respectively , with percentages of 36.7 2.8% and 31.1 ± 4.1%, respectively (3BHSD2 gene expression was not different between the spongiocytes of mutated and nonmutated patients (CYP17A1 gene expression showed no differences between the spongiocytes of mutated and nonmutated cells (ARMC5 mutations presented with more 5HT4R-positive cells than those who did not carry ARMC5 mutations (5HT4R gene expression between the mutated and nonmutated patient groups (To better characterize the tissue sections from patients who would be further analyzed, we investigated the cell types that constituted the PMAH nodules through histochemistry and immunostaining, and we determined the protein and gene expression levels of  tissues . We obse tissues  and that tissues . For compatients . To invepatients . We obsepatients . Moreoveectively . Additioectively . We obseectively  or preseectively  in the Aribution . We obseribution , and no ribution . The 3BHribution . Moreoveectively . Howeverpatients . The CYPpatients . The inved cells . We nexts p = 0.0, with peed cells ; this reed cells . The preed cells . Moreoveutations . For comutations . Howevert groups . Finallyt groups  despite t groups .ARMC5 gene could interfere with them. First, we observed the presence of the ARMC5 protein in the cell cytoplasm in both cell types of nonmutated and mutated patient sections, as done by Assié and cols. (ARMC5 mRNA expression in spongiocytes was higher in mutated patients than in nonmutated patients, while we observed a heterogeneous expression of ARMC5 in PMAH cell cultures, with a trend toward a lower expression of ARMC5 in mutated cells than in nonmutated cells (ARMC5 on transcription have not yet been elucidated; therefore, we can only speculate that distinct somatic mutations would have different consequences on ARMC5 expression; that is, some somatic mutations would be more deleterious than others. However, this remains to be established. Additionally, it is important to emphasize that the results of this study were obtained from spongiocyte microdissected tissue, while the results from the previous study of our group were obtained from cell cultures with both cell types, spongiocytes and compact cells, which could explain the difference between the results. However, this remains to be confirmed.In this study, we investigated the composition and functional patterns of PMAH nodules and whether mutations in the nd cols. , and in nd cols. . Throughed cells . The conARMC5 may be a tumor suppressor gene, but its function is still unclear. An analysis of the expression of the four ARMC5 isoforms in 46 normal human tissues showed that at least one was ubiquitously expressed throughout the body, whereas the adrenal gland expressed all isoforms (ARMC5 in different tissues suggests additional physiological functions as well as ARMC5 involvement in the pathologies of other tissues.isoforms . The extARMC5 gene than that of those without ARMC5 mutations. Since ARMC5 is considered a putative tumor suppressor gene and because the origin of macronodules in PMAH is not known, mutations in ARMC5 might influence proliferation or apoptosis (Moreover, our results from the PCNA protein analysis suggest a higher proliferative capacity of nodules carrying mutations in the poptosis ,14, causARMC5 did not interfere with the quantity of steroidogenic enzymes in our cohort, which is in contrast with the quantity of the CYP11A1 protein described in a mutated patient by Assié and cols. (The macronodules analyzed in this study presented the same characteristics as those described in a study by Sasano and cols. , in whicnd cols. . We hypoARMC5 mutations than in the nodules of patients without ARMC5 mutations. Bertherat and cols. (ARMC5, and this would contribute to a more severe clinical presentation.To date, there is no known direct relationship between ARMC5 mutations and the presence of aberrant receptors in PMAH. However, we interestingly observed a higher number of 5HT4R-positive cells in the nodules of patients carrying nd cols.  suggestend cols. ,19. We hGIPR mRNA was not sufficient to cause food-dependent CS. Here, we observed heterogeneous mRNA expression of GIPR among the investigated patients without detection of the GIPR protein, suggesting that the mRNA expression in these patients was not sufficient to produce detectable amounts of protein.Swords and cols.  reportedARMC5-mutated PMAH nodules are related to higher proliferation rates and to a greater presence of the 5HT4R than nonmutated nodules. These new data increase the understanding of the origin of macronodules and could help explain the clinical presentation in this subset of ARMC5-mutated patients.Finally, our findings suggest that