Gene transcription by RNA polymerase II (Pol II) is under control of promoters and distal regulatory elements known as enhancers. Enhancers are themselves transcribed by Pol II correlating with their activity. How enhancer transcription is regulated and coordinated with transcription at target genes has remained unclear. Here, we developed a high-sensitive native elongating transcript sequencing approach, called HiS-NET-seq, to provide an extended high-resolution view on transcription, especially at lowly transcribed regions such as enhancers. HiS-NET-seq uncovers new transcribed enhancers in human cells. A multi-omics analysis shows that genome-wide enhancer transcription depends on the BET family protein BRD4. Specifically, BRD4 co-localizes to enhancer and promoter-proximal gene regions, and is required for elongation activation at enhancers and their genes. BRD4 keeps a set of enhancers and genes in proximity through long-range contacts. From these studies BRD4 emerges as a general regulator of enhancer transcription that may link transcription at enhancers and genes. Here the authors reveal that high-sensitive nascent transcript sequencing provides an extended high-resolution view on transcription, including lowly transcribed enhancers. Widespread transcription at enhancers and their target genes depends on the BET family protein BRD4. Pol II transcription is not restricted to genes but also occurs in extragenic regions3. A main source of extragenic transcription is antisense transcription that originates in the opposite direction of a gene giving rise to non-coding transcripts4. Although antisense transcription from divergent promoters is widespread in mammalian cells9, it can also arise within genes10 leading to complex transcriptional architectures. A complete picture of the Pol II transcriptional landscape, especially at non-coding parts of the genome, has not yet emerged.Metazoan genomes are pervasively transcribed by RNA polymerase II (Pol II)13. Early transcription elongation has emerged as a regulatory hub when Pol II pauses in the promoter-proximal region at the majority of genes in mammalian cells16. Enhancers are required for the activated transcription of their target genes17 and are often located within the same local chromatin interaction domain, termed topologically associating domain (TAD)19. Due to the sometimes large distance between enhancers and their cognate targets, mechanisms exist that bring them into physical proximity22. Enhancers can be located between genes or within genes. The systematic identification of intragenic enhancers has been challenging due to the overlap with their host genes and our knowledge of these enhancers is incomplete.Gene transcription is under the control of proximal promoter and distal control elements such as enhancers, and is usually divided into different main phases. Following transcription initiation, the nascent RNA is produced during the elongation phase before transcription terminates at the termination zone27 and enhancer transcription has been linked with enhancer activity29. Enhancer transcription shares many features with Pol II transcription at genes including the formation of a pre-initiation complex prior to transcription initiation31 and Pol II pausing shortly after transcription initiation32. Similar to genes, enhancers are often divergently transcribed by Pol II34. The mechanisms of how enhancer transcription is regulated and coordinated with Pol II transcription at the corresponding target gene have remained unclear.Enhancers are transcribed by Pol II35. Despite our incomplete knowledge of these factors and the molecular mechanisms of enhancer-target gene communication, the BET family protein BRD4, which has been implicated in a range of human diseases and emerged as a therapeutic target38, is of particular interest in this context since it co-localizes to both target gene and enhancer regions44. At target genes, BRD4 helps to activate transcription elongation in mammalian cells48 whereas its function in enhancer transcription is less well understood. Although it has been suggested that BRD4 is involved in enhancer RNA synthesis, its specific implication is still unclear mainly because studies have used pan-BET inhibition which can’t discriminate between individual BET proteins51. Furthermore, BRD4 is thought to participate in higher-order genome organization and enhancer-promoter interactions56 although a recent study found that BET proteins are dispensable for enhancer-promoter contacts57. Direct BRD4-specific roles in enhancer function have remained elusive.According to the current view, transcription factors transmit signals between enhancers and their target genesHere, we developed a high-sensitive NET-seq approach to provide an extended high-resolution view on the genome-wide density of engaged RNA polymerase, including lowly transcribed enhancers. HiS-NET-seq identified thousands of new Pol II transcribed putative enhancers also within active genes of human cells. Using integrative functional multi-omics, this study reveals a direct and general role of BRD4 in enhancer transcription. The findings indicate that BRD4 is required for elongation activation at enhancers, similar to its role at cognate genes, and for maintaining proximity between transcribed enhancers and a set of target genes. This work is in line with the hypothesis of coordinated Pol II transcription at enhancers and their associated genes by BRD4 and its selected interactors.58, often suffer from low sensitivity. To overcome this main limitation and to reveal the fine structure of the Pol II density also at lowly transcribed genomic locations, we developed a high-sensitive NET-seq approach, called HiS-NET-seq. This approach includes short optimized labeling (10 min) of nascent RNA with the nucleotide analog 4-thiouridine (4sU) prior to chromatin isolation ; The position of transcribing RNA polymerase is revealed with nucleotide and DNA strand resolution by sequencing the regions corresponding to the 3’-ends of the original nascent RNA. HiS-NET-seq also uses spike-ins which allows quantitative comparisons between conditions.Current methods to profile the genomic position of transcriptionally engaged Pol II with nucleotide precision, such as native elongating transcript sequencing (NET-seq)ion Fig. –1f. 4sU ary Fig. 62,63; Thr = 0.99) indicating the robustness of this approach   the intensity was higher on one strand. When the signal at the opposite strand dropped below the detection threshold, enhancers appeared as uni-directionally transcribed 70. This analysis showed that BRD4 predominantly localized to putative enhancer and promoter-proximal gene regions  allows rapid BRD4-selective degradation in cells upon exposure to the degrader  analysis identified known regulators of the 3D chromatin architecture including subunits of the Cohesin complex , CTCF, NIPBL, WAPL and YY1 as significant BRD4 interactors Fig. . FurtherTogether, the results are in line with the hypothesis that BRD4 can coordinate Pol II transcription elongation at enhancers and putative target genes likely through maintaining their proximity Fig. .74, the identification of active enhancers within transcribed genes has been challenging since enhancer transcription overlaps with coding transcription and histone marks of the host gene. Here, we provide evidence that convergent antisense transcription (CAT) is indicative for transcribed intragenic enhancers and used this knowledge to systematically identify active enhancers within genes. CAT has been observed in previous studies76, but its origin and function have remained unclear. In this study, we found that CAT to a large extent originates from putative intragenic enhancers suggesting a role in enhancer function. Although CAT may interfere with the expression of the host gene77, more work will be required to clarify its role.The increased sensitivity of HiS-NET-seq, which is mainly achieved by a two-step enrichment of nascent RNA combined with an efficient state-of-the-art NET-seq library preparation, provided a more complete view on transcriptionally engaged Pol II at genes and non-coding transcription units such as enhancers. This approach uncovered 11,007 new putative enhancers in K562 cells, mainly intragenic and lowly transcribed extragenic enhancers. Although intragenic enhancers have been observed and studied before78. PRO-seq may miss a subset of paused/arrested RNA polymerases that may resist a restart of transcription79.HiS-NET-seq data correlates well with PRO-seq but also reveals differences. The main difference was detected in the promoter-proximal region of genes where Pol II usually pauses before it is released into productive elongation. HiS-NET-seq captures significantly more engaged Pol II in this region as compared to PRO-seq methods. A potential explanation for this discrepancy could lie in the labeling of the nascent RNA. HiS-NET-seq uses 4sU labeling of nascent RNA under natural transcription elongation conditions in intact cells and likely also captures RNA polymerases that enter into a paused state or are recovering from a pause/arrest to resume RNA synthesis during the labeling time. Consistently, the correlation between HiS-NET-seq and conventional NET-seq data, which does not require labeling of nascent RNA, was highest especially in promoter-proximal regions of genes. Nuclear run-on based methods require nuclei isolation or cell permeabilization and a restart of transcription in the presence of biotinylated NTPsDespite the increased sensitivity of HiS-NET-seq providing a more complete genomic occupancy profile of transcriptionally engaged Pol II, a current limitation is the relatively high amount of cells that are required as an input. This restricts the application of HiS-NET-seq to cell lines and primary cells at the moment that can be obtained in greater amounts. Since HiS-NET-seq uses 4sU labeling of nascent RNA the approach, similarly to other metabolic labeling-based RNA polymerase profiling methods, cannot be applied to tissues or whole multicellular organisms.81. Second, BRD4 occupancy at enhancers correlates with enhancer transcription. Third, enhancer marks such as H3K27ac and H3K4me1 are enriched at BRD4 binding sites. Fourth, acute BRD4-specific ablation in cells causes an immediate collapse of enhancer transcription. Notably, the reduction of enhancer transcription was strongest at BRD4-sensitive enhancers. The high temporal resolution of inducible and selective degradation of transcriptional regulators83, and HiS-NET-seq capturing the direct impact on Pol II transcription, before phenotypes are complicated by secondary effects, reveal a BRD4-specific function in enhancer transcription genome-wide.Enhancer transcription is regulated by BRD4. The BRD4-specific function in enhancer transcription has been unclear mainly because most prior functional studies have used pan-BET protein inhibitors or degraders disrupting all BET family proteins simultaneously. Here, we provide several lines of evidence that support a direct and general role of BRD4 in enhancer transcription. First, BRD4 binds to enhancers genome-wide. The binding of BRD4 to enhancers is in line with previous observations in other cellular contexts84.BRD4 may link Pol II transcription at enhancers and their putative target genes Fig. . The fol88 and enhancer function91. In this study, we found that BRD4 and PAF1 occupancy was most strongly reduced at genomic sites where 3D contacts were disrupted upon acute BRD4 ablation suggesting that BRD4 and PAF co-function in enhancer-target gene communication.We speculate that a BRD4-mediated link between transcription at enhancers and their cognate genes can occur at the level of elongation at a set of genes. Our finding that BRD4 is required for Pol II pause release at enhancers and associated genes supports this hypothesis. Other BRD4 interactors are likely also involved including PAF. PAF is an integral component of the Pol II elongation complex that has been implicated in pause release94. Our view is also in line with a recently proposed condensate model for enhancer-promoter communication according to which hubs of transcription factors, coactivators including BRD4, and Pol II are formed to connect enhancers and target gene promoters95. Despite this evidence in support of the hypothesis that transcription at enhancers and a set of cognate genes is coordinated, more research will be required to clarify the links.The proposed model on the coordination of enhancer and target gene transcription Fig.  is consiThe multi-omics approach that we have used can now be applied to other factors to identify the players and their functions in the potential co-regulation of enhancer and target gene transcription to shed new light on enhancer-target gene communication.47 cells were cultured in RPMI 1640  containing 10% FBS Superior , 5% penicillin-streptomycin . Cells were seeded at 5 × 105 cells/ml every two days. For Transfections, RPMI with 1x GlutaMAX (Cat.# 35050-038) was used. Cells were kept in culture for not longer than 4 weeks.K562  and K562 dTAG-BRD46 cells/T75 flask every two days.NIH/3T3  cells were grown in DMEM  containing 10% FBS , 5% penicillin-streptomycin . NIH/3T3 were diluted to 2 × 10All cell lines were routinely tested for the presence of mycoplasma.5 cells/ml density in RPMI. Labeling of K562 was performed at a final concentration of 500 µM 4sU  and a cell density of 1 × 106 cells/ml . For labeling, cells were exposed to 0.5 M 4sU for 10 min at 37 °C and 5% CO2. Cells were then placed on ice and fractionated according to the rapid fractionation procedure as described below.48 h before an experiment, K562 cells were seeded at 5 × 105 cells/ml in RPMI. For BRD4 depletion or matching DMSO control experiments 1 × 108 of K562 dTAG-BRD4 cells were collected and reconstituted at 1 × 106 cells/ml in fresh pre-warmed RPMI supplemented with 500 nM dTAG7 or an equivalent volume of DMSO (control). Degrader treatments were performed for 40 min or 2 h at 37 °C and 5% CO2. For the final 10 min of the treatment, 4sU was added to a concentration of 500 µM. Next, cells were placed on ice and fractionated according to the rapid fractionation procedure. Four and two biological replicate measurements were performed for 40 min or 2 h of acute BRD4 degradation, respectively.Forty-eight hours prior to an experiment, K562 dTAG-BRD4 cells were seeded at 5 × 106 NIH/3T3 cells were seeded on fresh plates. The total number of cells used per experiment was ~1 × 108. Cells were harvested and reconstituted to the 1 × 106 cells/ml in warm and fresh medium supplemented with 4sU  for 10 min at 37 °C. Cells were placed on ice and fractionated following the rapid fractionation procedure.Twenty-four hours before the experiment 4 × 10g. The pellet was gently resuspended in lysis buffer  and incubated for 2 min. Nuclei were collected at 500 × g for 3 min. Nuclei were washed with cytoplasmic wash buffer  and gently resuspended in 750 µl of glycerol nuclei buffer . Chromatin was precipitated by rapid addition of 750 µl of nuclei lysis buffer , and the sample was vortexed 5 times for 5 s, and then placed on ice for 2 min. Chromatin-associated RNA was collected at 18,000 × g for 2 min. The pellet was washed twice with PBS and dissolved in 1.5 ml QIAzol Lysis Reagent per 5 × 107 cells (QIAGEN) supplemented with 100 µM DTT and 1 mM EDTA. To dissolve the pellet completely the sample was incubated at 40 °C for 1 h at 1000 rpm and homogenized using QIAshredder  for 1 min at 20,000 ×  g. The sample was stored at −80 °C.Unless otherwise indicated, all procedures were performed on ice (4 °C) with pre-cooled buffers and under low light exposure. Cells were harvested for 2 min at 1150 × g and 4 °C for 20 min. The pellet was washed twice with 80% ice-cold ethanol, air-dried, and resuspended in nuclease-free H2O. The samples were treated with TURBO DNase (Thermo Fisher Scientific) according to the manufacturer’s instructions. The reaction was stopped by adding EDTA to the final concentration of 15 mM. RNA was purified using phenol:chloroform:isoamyl alcohol (25:24:1)  and Phase Lock Gel Heavy tubes (QuantaBio Cat.# 733–2478) by centrifugation for 5 min at 12,000 × g. The RNA was collected by isopropanol precipitation, washed with 85% ethanol and resuspended in nuclease-free H2O.Prior to RNA extraction 4sU-labeled spike-in RNA (in QIAzol) was added in a 1:8 ratio (NIH/3T3:K562). RNA was obtained by chloroform extraction using MaXtract High Density Phase-Lock-Gel tubes. RNA was obtained by isopropanol precipitation for 10 min on ice followed by centrifugation at 20,000 × 60. with the following modifications. The µColumn  was washed three times with 1 ml of 65 °C pre-warmed pull-out wash buffer ), followed by three washes with 1 ml pull-out wash buffer at room temperature. The eluted RNA was purified using the ZYMO Clean & Concentrator-5 kit .100 µg RNA was biotinylated using MTSEA biotin-XX linker  and purified by μMACS streptavidin MicroBeads  as described by Gregersen et al.62. with the following modification. 2–3 µg of 4sU-labeled and biotinylated RNA was used as an input for the library preparation.The HiS-NET-seq library preparation was conducted according to the nested-NET-seq protocol as described by Gajos et al.63 with the following modification. The ‘barcode DNA oligo’ contained a random decamer sequence.The conventional NET-seq library preparation were performed as previously described60 with the following modifications. First, Hybond®-N+ hybridization membrane  was used. Second, biotin was probed by IRDye® 800CW Streptavidin antibody  and visualized using a LI-COR Odyssey CLx imager.The dot blot procedure was performed as described previously7 cells were fractionated as described in the rapid fractionation procedure. The buffer volumes were downscaled according to the number of cells. For the dTAG7 time course experiment, whole cell lysates from 2 × 106 cells were prepared by incubation with 25–50 U benzonase and protease inhibitors  in PBS on a shaker. For the H3K27ac analysis, subcellular fractionation was carried out using 1 × 107 cells as described in Mayer and Churchman63. For whole cell extract (WCE) 3 × 106 cells were harvested, washed in PBS and lysed in 300 µl RIPA buffer (Sigma) supplemented with 1× protease inhibitor cocktail (Roche) and 1 µl Benzonase . For histone 2B (H2B) probing, the membrane was stripped for 10 min in Restore PLUS Western Blot Stripping Buffer , washed and blocked prior to incubation with anti-H2B antibody . The following primary and secondary antibodies were used: Pol II Ser2-P (3E10) , Histone H2B (A-6) , GAPDH , HA tag , H3K27ac , Tubulin , IRDye® 800CW Goat anti-Mouse IgG Secondary Antibody , IRDye® 800CW Goat anti-Rabbit IgG Secondary Antibody  and IRDye® 800CW Goat anti-Rat IgG Secondary Antibody . The signal was visualized using a LI-COR Odyssey CLx imager.To obtain cytoplasmic, nucleoplasmic and chromatin samples, 1 × 1062.. Briefly, the obtained sequencing reads were trimmed using cutadapt v3.496 (-a ATCTCGTATGCCGTCTTCTGCTTG -a AAAAAAAAAAGGGGGGGGGGGGGG -a GGGGGGGGGGGGGGGGGGGGGGG -e 0.2 -q 5 --max-n 0.9) to remove sequenced fragments from the primers. Starcode v1.197 collapsed identical fragments (-d 0) with the same UMI sequence to one consensus read, removing PCR duplicate reads. The UMI decamer sequences were trimmed from the 5’ regions but the sequence information remained associated. The obtained sequencing read fragments were aligned to the human reference genome (GRCh38.p12)97 using the STAR aligner v2.7.3a98 . Next, a custom python script removed potential artifacts produced by mispriming of the RT primer if the UMI sequence corresponded to the genomic sequence adjacent to the aligned sequencing read. Furthermore, the custom script masked RNA processing intermediates produced during RNA splicing and 3’-end RNA cleavage. We excluded sequencing reads mapping to the 3’ most nucleotide position of annotated introns and exons, including the polyadenylation site. We masked the same nucleotide positions in the corresponding metagene visualizations to avoid artificial signal drops and potential misinterpretations of the occupancy profiles. Bedtools v2.29.299 was applied to mask regions103, including transcribed regions of Pol I, Pol III, microRNA, miscellaneous RNA, rRNA, snoRNA, snRNA, tRNA, vault RNA, Y RNA, and blacklisted regions from ENCODE. Finally, the pipeline derives the Pol II occupancy tracks from the remaining uniquely mapped sequencing reads. We extracted the single-nucleotide 5’-positions from each sequencing read, corresponding to the 3’-end of the purified nascent RNA fragment.Data processing steps were applied as described by Gajos et al101 were removed. Next, RNA species from Pol I, Pol III, microRNA, miscellaneous RNA, rRNA, snoRNA, snRNA, tRNA, vault RNA, Y RNA, and blacklisted regions from ENCODE were masked in the human and mouse genomes. Finally, the sequencing reads mapping to the human genome were separated from those mapping to the mouse genome, resulting in a human and mouse Pol II occupancy data set for each sample. The Pol II occupancy in untreated mouse cells was used for data normalization.The standard data processing pipeline for HiS-/NET-seq data, described in the previous paragraph, was applied to data with spiked-in control cells from the mouse (NIH/3T3) with the following adjustments. For mapping the sequencing reads to the reference genome, a joint reference from the human (GRCh38.p12) and mouse (GRCm38.p6) genomes was used. Furthermore, splicing and 3’-RNA processing intermediates from both species using the GENCODE v28 and M18 annotations65 was performed in two steps. First, all data sets were processed as described in detail in the ‘Processing of NET-seq and HiS-NET-seq data’ section with the following adjustments for PRO-seq and qPRO-seq. We changed the parameter setting of the following applications to accommodate for the paired-end sequencing mode of PRO- and qPRO-seq data: cutadapt, starcode, STAR, and custom scripts. We trimmed PRO-seq-specific adapter sequences (-a TGGAATTCTCGGGTGCCAAGGAACTCCAGTCAC -A GATCGTCGGACTGTAGAACTCTGAACGTGTAGATCTCGGTGGTCGCCGTATCATT) and adjusted the UMI sequence length from 10 nt (HiS-NET-seq) to 6 nt at the forward and reverse strands. Second, we performed correlation and metagene analysis using DEseq2 v1.25.4104 and deepTools2 v3.2.1105, respectively. Data was normalized using the standard normalization strategies RLE104 and RPM. Pol II density right at transcription start sites (TSSs) and polyadenylation (pA) sites were masked.The systematic comparison of HiS-NET-seq, standard NET-seq, PRO-seq, and qPRO-seq data106 to randomly select a subset of sequencing reads of equal size for each sample. The sample with the lowest sequencing depth served as a reference to estimate the new sample size of 14,556,851 sequencing reads. This step allows the direct comparison of informative reads between data sets obtained with the different methods.Next, we combined the described processing steps with additional data downsampling in the performance analysis. We applied seqtk v1.3-r106107. A gene was classified as active using both the corresponding RNA-seq  and HiS-NET-seq data as follows. First, RSEM v1.3.1 113 quantified the number of transcripts produced by each gene and isoform in the respective single-end or paired-end mode using the RNA-seq data and the STAR v2.7.9a 105 alignment tool. Second, the genes with a TPM ≥ 1 were selected. Third, we refined GENCODE’s annotation based on active gene isoforms by identifying the first and last active TSS and polyA sites. An active gene isoform contributed at least 10% to the overall gene activity. Fourth, genes without nascent transcription were removed. We used HOMER’s v4.11.1 114 makeTagDirectory (-flip) and findPeaks functions  to annotate nascent transcription units using HiS-NET-seq data.This study defined cell line-specific active genes (K562 or NIH/3T3) as a subset from human v28 or mouse M18 GENCODE annotations107, which reported cell-type-specific FANTOM5 enhancer units and initiation sites identified by the NRSA application108.Actively transcribed enhancer units were identified for the K562 cell line from annotated FANTOM5 enhancers. Data sets were extracted from the HACER database109, as described in the previous paragraph ‘Identification of active genes and gene isoforms’. Antisense transcription units (ATUs), DAT and CAT units overlapped with annotated genes at the opposite strand relative to an actively transcribed gene. We distinguished between both types of antisense transcription units by their location, where DATs originate upstream (<1000 bp) and CATs downstream from the corresponding transcription start site of the gene. To eliminate the risk of spill-over effects from neighboring genes, we removed ATUs originating from overlapping genes. A transcript originates from an overlapping gene if either 50% or more overlap, or the transcription unit covers at least 90% of the gene. Finally, we removed transcription units overlapping the entire antisense gene region, as observed for many short genes.Convergent antisense transcription (CAT)-, divergent antisense transcription (DAT)-, and putative enhancer regions were derived from nascent transcripts of HiS-NET-seq data using HOMER v4.11.1ENCSR000AKP and ENCSR000EWC103).Assigning CAT and DAT units is more complex if an actively transcribed gene expresses several transcript isoforms from multiple active TSSs. We defined active gene isoforms as described in the paragraph ‘Identification of active genes and gene isoforms’. Some perceived CAT units originated from bidirectional transcription of downstream-located alternative TSSs. These sites were considered as DAT units. Putative intragenic enhancers were defined by the remaining CAT units that co-localize with the histone marks H3K27ac and H3K4me1  of active genes were removed. We classified divergently transcribed units as bi-directional and unpaired units as uni-directional enhancers. To classify as a potential extragenic enhancer, bidirectional transcription needs to originate within <500 bp.104 in different regions  <0.05.We tested for changes in the Pol II occupancy using DEseq2 v1.25.4110.For the Dual-Luciferase Assay potential enhancer DNA sequences were PCR amplified from K562 cells and cloned in both sense and antisense direction into the pGL3-promoter vector by either conventional restriction enzyme cloning using BamHI-HF or SalI-HF  or by Gibson Assembly . The primer sequences used for amplification are listed in Supplementary Data 5) cells were transfected with 0.6 ng Renilla luciferase internal control plasmid (pRL-TK) together with the Firefly luciferase test plasmid (enhancer construct) in a molar ratio of 1:700. In addition, 4 µM electroporation enhancer (IDT) were added to each transfection reaction. If necessary, additional electroporation enhancer was added to ensure an equal amount of total DNA in all transfections. K562 cells were transfected using SF Cell Line 4D-NucleofectorTM X Kit S and Amaxa Nucleofector 4D. Cells were harvested 24 h post transfection and lysates were prepared using 100 µl 1× Passive Lysis Buffer .K562  according to the manufacturer’s protocol using 20 µL cell lysate. The luciferase activity was measured using the GloMax® Navigator Microplate Luminometer with dual injector (Promega) in a 96-well plate with following settings: 100 µL of the Firefly Luciferase Reagent (LAR II) was injected to each sample with a 2 s measurement delay time and measurement of luminescence with a 10 sec integration time. Subsequently, 100 µL of the Renilla Luciferase Reagent and Firefly quenching (Stop & Glo) was added with a 1 s measurement delay time, and measurement of luminescence with a 10 sec integration time. The data were processed using Excel and finally represented as the ratio of Firefly to Renilla luciferase activity. Statistics were performed with Graphpad Prism using a 71. with the following modifications. Per condition, three biological replicates were generated. 5 × 106 K562 dTAG-BRD4 cells47 were treated with 500 nM dTAG7 or DMSO (control) for 2 h, followed by crosslinking with 1% methanol-free formaldehyde for 10 min at room temperature. The partially lysed nuclei were incubated with 375 U MboI  at 37 °C overnight. After biotin-dATP incorporation, chromatin shearing was conducted in a Covaris S220 sonicator for 6 min at intensity 4, duty cycle 5% and 200 cycles per burst. For IP, 5 µg of an H3K27ac-specific antibody  coupled to Pierce Protein A/G  were used per sample. The eluted DNA was heated to 65 °C for 5 h, treated with 80 µg RNase A  for 2 h at 37 °C and with 200 µg Proteinase K  for 45 min at 50 °C. 50 ng of the purified DNA was incubated with 2.5 µl of Tn5 . NEBNext Q5 master mix  was used to amplify the library by 8 cycles. Lastly, the libraries were size-selected using AMPure XP beads  at a ratio of 0.8 and subsequently 0.5 to enrich fragments of 200–750 bp and sequenced in PE75 or PE100 mode on an Illumina HiSeq 4000 or NovaSeq 6000 sequencer.HiChIP was performed as described by Mumbach et al.111 pipeline where raw interaction frequencies were extracted at a 5 and 10 kb resolution for the applied restriction enzyme (MboI: ^GATC) and the human reference genome (GRCh38.p12)101. For data normalization, we applied two alternative normalization approaches implemented by HiCcompare v1.8.0112 and FitHiChIP v10.0113.For HiChIP data processing, we used the HiC-Pro v3.0.0112. The application removes batch and sequencing depth biases and accounts for the linear distance between interacting regions. Briefly, we performed the following steps. First, QDNAseq’s v1.22.0114 build-in get_CNV function identified copy number variations in the K562 dTAG-BRD4 cell line and excluded the respective regions. Second, low-complexity regions (blacklisted) annotated by ENCODE103 were excluded. Third, we used the ‘hic_loess’ and ‘hic_compare’ functions to compute logarithmic interaction frequency changes, correct for systematic biases, calculate z-scores and p-values, and to perform multiple test corrections. Interactions with an average expression <9 were excluded for comparison. Interactions with a minimum distance of 10 kb and a padj value < 0.05 were identified as significant changes between conditions.For quantitative comparisons between conditions, we applied the global data normalization approach from HiCcompare113. Significant 3D interactions were identified using pooled HiChIP data with 5 kb resolution from the control experiments. FitHiChIP’s normalization method learns the relationship between coverage and genomic distance using a regression model. For this background model, we considered only interactions between H3K27ac genomic loci derived by ChIP-seq data from ENCODE103. 3D interactions that exceeded the expected coverage from the background model were considered significant, if the corresponding p-value was <0.01 after multiple test corrections. Target genes from putative enhancers were assigned by at least one significant 3D interaction.For 3D contact identification, we used the FitHiChIP v10.0 application73application to K562-specific chromatin marks from ENCODE data103, including H3K27ac (ENCSR000AKP), H3K4me1 (ENCSR000EWC), H3K4me3 (ENCSR000EWA), H3K27me3 (ENCSR000EWB), H3K36me3 (ENCSR000AKR), and H3K79me2 (ENCSR000APD). First, the chromHMM application divided the human genome into 10 kb bins using the BinarizeBam function. Second, chromHMM’s LearnModel function trained a multivariate Hidden Markov Model with ten states and reported the most likely states for each bin. In the last step, the resulting states were manually annotated into promoter , enhancer , 3’-end, gene-body, repressed, and low-signal states. The promoter, intragenic-, and extragenic enhancer states showed high levels of H3K27ac. However, a unique chromatin feature of the promoter state was the high level of H3K4me3, which is absent or reduced in the three enhancer states. As described by115, we assigned states with H3K79me2 to gene-body regions, whereas H3K36me3 was more abundant at the 3’-ends of genes  were used. ChIP-Rx libraries were prepared using the NEBNext Ultra II DNA kit  according to the manufacturer’s instructions. Libraries were purified using one volume of AMPure XP beads  and size-selected (200–500 bp) from an 8% TBE gel (Thermo Fisher Scientific). Sequencing in PE100 mode was done on an Illumina NovaSeq 6000 sequencer.ChIP-Rx was performed as described by Arnold et al.116 to align the sequencing reads to a joint reference which consisted of the human (GRCh38.p12) and mouse (GRCm38.p6) reference genomes using the paired-end mode with the parameter -k 1. We extracted the DNA-binding profiles (fold enrichment over matched input control (FE)) of BRD4 using MACS2’s v2.2.7.1117 bdgcmp function with the -m FE parameter. As recommended by the MACS2 developers, potential PCR duplicates were marked using PICARD’s v2.24.2118 markDuplicates function. Finally, the DNA-binding profiles of BRD4 corresponding to the human genome were separated from the BRD4 binding sites in the mouse genome.First, we used Bowtie2 v2.3.5.1119 to identify differentially bound occupancy sites. First, DiffBind’s ‘dba.blacklist’ function removed the signal from blacklisted regions of the human and mouse reference genomes . Second, peaks from all samples were summarized in consensus peaks and quantified . Third, DiffBind’s ‘dba.normalize’ function adjusted the human consensus peaks based on the binned mouse genome . Finally, differential binding sites were identified using the DEseq2 package104.We used DiffBind v3.0.15105 bamCompare function with the parameters -p 20 --ignoreDuplicates --scaleFactorsMethod None. The previously calculated scaling factors from DiffBind were applied using the --scaleFactors parameter.Furthermore, for the visualization of the occupancy changes (log2), we used the deepTools2’s v3.2.147. Briefly, the K562 dTAG-BRD4 cells were lysed in a native IP buffer  NP-40, 10% (vol/vol) glycerol, protease inhibitor cocktail (Roche), phosphatase inhibitor cocktail (Roche) supplemented with benzonase . Nuclei were isolated by centrifugation and lysed in native IP buffer supplemented with benzonase . For IP 10 µg of the BRD4-specific antibody  or of isotype-matched IgG  and 1,5 mg Dynabeads Protein G  were used per sample. After two washes with the native IP buffer, three washes were performed with a washing buffer  NP-40). Five biological replicates were performed per condition. Mass spectrometric analysis on a Q-Exactive HF Orbitrap (Thermo Scientific) and processing of the data was done as described previously47.IP-MS experiments were performed as described by Arnold et al.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting SummarySource Data


Epigenetic reprogramming represents a series of essential events during many cellular processes including oncogenesis. The genome of Kaposi’s sarcoma-associated herpesvirus (KSHV), an oncogenic herpesvirus, is predetermined for a well-orchestrated epigenetic reprogramming once it enters into the host cell. The initial epigenetic reprogramming of the KSHV genome allows restricted expression of encoded genes and helps to hide from host immune recognition. Infection with KSHV is associated with Kaposi’s sarcoma, multicentric Castleman’s disease, KSHV inflammatory cytokine syndrome, and primary effusion lymphoma. The major epigenetic modifications associated with KSHV can be labeled under three broad categories: DNA methylation, histone modifications, and the role of noncoding RNAs. These epigenetic modifications significantly contribute toward the latent–lytic switch of the KSHV lifecycle. This review gives a brief account of the major epigenetic modifications affiliated with the KSHV genome in infected cells and their impact on pathogenesis. Kaposi’s sarcoma (KS), the most common neoplasm of HIV-infected people, is caused by coinfection of Kaposi’s sarcoma-associated herpes virus (KSHV) . KSHV orThe genome of KSHV is 165–170 kb linear dsDNA and has complex gene organization, which includes overlapping genes, as well as polycistronic mRNAs ,5. The cKSHV employs two distinct life cycles consisting of latent and lytic phases; however, it utilizes a common tactic to establish latency in the host cells ,11. EscaIn addition to DNA methylation, histone modifications also play a determining role in regulating the expression of KSHV-encoded genes, and they are considered a central event during chromatinization of the KSHV genome, especially during the early stage of infection . The mosCancer initiation and development have been largely linked with congenital or acquired aberrant alterations that occur in the form of mutations, insertions, deletions, and recombinations in the chromosome or copy-number alterations, leading to persistent changes in the phenotype . This reCpG is refers to a cytosine–guanine sequence separated by one phosphate group. These islands are the sites in DNA sequences that are rich in CpG dinucleotides. CpG islands can be found in promoter and exonic regions in approximately 40% of the genes in mammals; however, other regions of the mammalian genome carry very few CpG dinucleotides as these are highly methylated . The humThe initial instance of methylation at CpG islands in a human tumor suppressor gene was recorded within the retinoblastoma gene in 1989. Following this, in 1994, Herman et al. showed that hypermethylation of unmethylated CpG islands in the VHL gene at the 5′ end could lead to the allelic loss and mutational inactivation of the gene in majority of spontaneous clear-cell renal carcinomas. Methylation of CpG islands in the promoter sequence of genes, particularly the tumor suppressor genes, homeobox genes, and other sequences, results in its failure to transcribe the genes, resulting in silencing of one or more concurrent transcript and, thus, leading to cancer ,65,66.In a study conducted in 1983, Gama-Sosa et al. claimed that the overall content of m5C in DNA from normal tissues varies considerably in a manner specific to different tissue. Restriction endonuclease digests of DNA from human tumor samples  showed that most of the metastatic neoplasm had significantly lower genomic m5C content than benign or normal tissues . In the KSHV-infected cells exhibit a distinct cellular gene expression pattern . The genYe et al., in 2010, demonstrated the role of DNA methylation in maintaining KSHV latency . It was In a recent study by Journo et al. (2021), the cellular CpG global methylation pattern was observed in KS infected biopsy samples . MethylaIn many instances, it has been reported that KSHV itself modulates cellular epigenome by utilizing its latent and lytic proteins. One such example is KSHV-encoded vGPCR. KSHV-encoded vGPCR can be transactivated by hypoxia-inducible factor 1 alpha (HIF1α). Under hypoxic conditions, stabilization of KSHV-encoded vGPCR induces production of reactive oxygen species, which in turns modulates the expression of cellular encoded DNMTs. The differential expression of DNMTs in response to vGPCR-mediated reactive oxygen species has been shown to modulate the expression of host nuclear-encoded genes .In cells, chromatin serves as a container for DNA. The fundamental component of chromatin is an assembly of histone octamers. The 145–147 base pair DNA fragment is wrapped around a 63 nm central solenoid. These nucleosomes are made up of two copies of each of the four core histone proteins H3, H4, H2A, and H2B, and the linker histone protein H1/H5. The side-chain of the big globular histone proteins is made up of basic lysine and arginine residues ,82,83. TOne of the hallmarks of cancer progression is the post-translational modification of histones which can regulate the expression and repression of associated genes . The pioHistone modifications can occur as part of histone acetylation, phosphorylation, methylation, demethylation, ADP ribosylation, ubiquitination, or sumoylation . In histRTA (replication and transcription activator), encoded by ORF50, acts as Kaposi’s sarcoma-associated herpes virus lytic switch protein . ChIP-onThe same conclusion was drawn on the basis of an experiment in which LANA knockout made KSHV incapable of recruiting PRCs to its viral genome . The recAlmost 80% of the eukaryotic genome has been transcribed, but only 2% (mRNA) is considered to have protein-coding function. The remainder of the genome does not code for protein but is transcribed at different levels, and 98% of all transcriptional products were previously considered junk DNA ,110. LatCaenorhabditis elegans and was found to be linked to embryogenesis [Additionally, there are covalent closed circular RNAs (circRNAs) which are labeled as the bridge between coding mRNAs and noncoding RNAs. Being expressed abundantly in eukaryotic cells, these RNAs are formed during post-transcriptional processing via a back-splicing mechanism. They act by modulating protein production, RNA transcription, and protein translation, and by sponging the miRNAs. Several studies have been conducted to understand the interaction between virus and host circRNAs, revealing an alteration in the expression of host circRNAs in virus-infected cells. It has been suggested that viruses may use these molecules for their own growth. It has also been observed that certain viruses produce their own viral circRNAs via back-splicing, but the viral genes encoding these circRNAs and their possible function have not been much studied ,115. Theogenesis . The relogenesis ,117. Varogenesis .siRNA is born out of double-stranded RNA molecules, which can be divided into RNA fragments comprising 19–24 nucleotides using the Dicer enzyme. The fragments exhibit functionality when loaded on Argonaute (AGO) proteins and involve transcriptional gene silencing . Dicer aMicroRNAs (miRNAs) are single-stranded RNAs comprising 19–24 nucleotides, of which 50% are positioned in chromosomal regions susceptible to structural changes . The modLncRNA lacks a protein-coding function, comprises transcripts longer than 200 nucleotides, and shares certain standard features with mRNAs such as 5′-methylguanosine capping, polyadenylation, and splicing . StructuThe expression of an array of viral miRNAs was noted in latently infected cells of KSHV by Cai et al. in 2005 . GrundhoCai et al., in 2004, suggested that, during the initial phase, miRNAs are transcribed as a largely unstructured precursor . This foChromatin isolation by RNA purification (ChiRP) assay was employed to investigate the role of PAN RNA in regulating viral latency by Rossetto et al., in 2013, who found that PAN RNA was present at multiple sites on the KSHV genome . K-RTA pThe morphological manifestation of Kaposi’s sarcoma is in the form of dark-brown macules, purpura, or spots on the skin that lead to ulceration and bleeding. For patients, who are untreated for AIDS, the pace of these spreading to the rest of the body is high and occurs within months. Currently, radiation-induced therapies are employed to treat soft-tissue sarcomas . EpigeneUnraveling the epigenetic landscape of Kaposi’s sarcoma-associated herpes virus is complex yet compelling. It is important to visualize the underlying molecular interplay and orchestration of various cellular mechanisms involving replication, transcription, translation, and gene expression. Epigenetic factors are instrumental in regulating KSHV’s latent-to-lytic switch that drives disease progression. It is an intriguing field of research that can pave the route for the development of targeted tumor therapies. Significant work has been performed in this line, but the therapeutical aspect has not been much explored. Epigenetic targeted therapy and treatments are still in their early phase and carry great potential for future research.


In cancer cells, the long non-coding RNA (lncRNA) MALAT1 has arisen as a key partner for the Polycomb Repressive Complex 2 (PRC2), an epigenetic modifier. However, it is unknown whether this partnership occurs genome-wide at the chromatin level, as most of the studies focus on single genes that are usually repressed. Due to the genomic binding properties of both macromolecules, we wondered whether there are binding sites shared by PRC2 and MALAT1. Using public genome-binding datasets for PRC2 and MALAT1 derived from independent ChIP- and CHART-seq experiments performed with the breast cancer cell line MCF7, we searched for regions containing PRC2 and MALAT1 overlapping peaks. Peak calls for each molecule were performed using MACS2 and then overlapping peaks were identified by bedtools intersect. Using this approach, we identified 1293 genomic sites where PRC2 and MALAT1 concur. Interestingly, 54.75% of those sites are within gene promoter regions (<3000 bases from the TSS). These analyses were also linked with the transcription profiles of MCF7 cells, obtained from public RNA-seq data. Hence, it is suggested that MALAT1 and PRC2 can concomitantly bind to promoters of actively-transcribed genes in MCF7 cells. Gene ontology analyses revealed an enrichment of genes related to categories including cancer malignancy and epigenetic regulation. Thus, by re-visiting occupancy and transcriptomic data, we identified a key gene subset controlled by the collaboration of MALAT1 and PRC2. The Polycomb Repressive Complex 2 (PRC2) is an epigenetic modifier that catalyzes the methylation of lysine 27 on histone H3 . The preDrosophila are not conserved in mammalian genomes, different research groups have unsuccessfully attempted to uncover DNA methylation- or protein-dependent mechanisms that apply to the whole genome. Two seminal papers from the late 2000s reported that the long non-coding RNAs (lncRNAs) Hotair  aner 2022) . Transcrr 2022))  and exprRaw SRA sequence files from ChIP- ,37,38 anWe first analyzed the binding profile of each molecule separately. The number of optimal peaks (peaks consistently found in two replicates) was determined using an irreproducible discovery rate (IDR) value below 0.01 as filter. We identified 26,916 peaks for SUZ12 enrichment and 24,508 for MALAT1 . These pWe next assessed whether SUZ12 and MALAT1 present overlapped enrichment peaks throughout the genome. To identify those overlapping peaks, we applied bedtools intersect to the BED files containing the optimal peaks of SUZ12 and MALAT1. We found 1293 shared peaks between both molecules a,b, assoMEN1, YWHAB, SENP3, MAPK15, and CADM4), oxidative stress (TXNIP), acting as transcription factors  or chromatin binders  in cancer cells. The enrichment of MALAT1  of actively-transcribed genes captured our attention. As mentioned above, reports proposing a MALAT–-PRC2 complex focus almost exclusively on gene silencing [tempering RNA Polymerase II elongation in actively-transcribed genes by binding to nascent transcripts [We report that, despite the ubiquitous localization throughout the genome of both MALAT1 and the PRC2 subunit SUZ12, a clear overlap between the molecules is found only in a subset of 839 genes in MCF7 cells. There could be multiple mechanistic implications for this finding, and here we offer a few hypotheses that need further testing in the laboratory. First, these data support a model where MALAT1 may be involved in the recruitment of PRC2 to a subset of genes in breast cancer cells. This resembles the roles of Xist and Hotair  except tilencing ,22,23, ailencing . Togethenscripts ,67. The nscripts  which maAmong the 403 genes that contain overlapping MALAT1–SUZ12 peaks at their proximal promoter regions (<1 kb from the TSS), special attention was given to genes involved in cancer progression, transcriptional regulation, and epigenetic activity. While the list presented in We are conscious that MCF7 is a human cell line representing a specific stage of a particular cancer. Nevertheless, it is also one of the most studied and characterized at the molecular level  and the A caveat of our analysis is the strict approach of bedtools intersect (the tool used to identify the regions occupied by both molecules). Intersect delivers a peak where independent peaks overlap by one or more nucleotides . This exIn summary, by analyzing key datasets of genome-wide and transcriptomic studies performed in MCF7 cells, we identified a novel set of genes where a potential MALAT1–PRC2 functional collaboration can be now further tested at the molecular level. As epigenetic regulation has become a well-established driver for cancer development and progression , underst


Unstable DNA repeat expansions and insertions have been found to cause more than 50 neurodevelopmental, neurodegenerative, and neuromuscular disorders. One of the main hallmarks of repeat expansion diseases is the formation of abnormal RNA or protein aggregates in the neuronal cells of affected individuals. Recent evidence indicates that alterations of the dynamic or material properties of biomolecular condensates assembled by liquid/liquid phase separation are critical for the formation of these aggregates. This is a thermodynamically-driven and reversible local phenomenon that condenses macromolecules into liquid-like compartments responsible for compartmentalizing molecules required for vital cellular processes. Disease-associated repeat expansions modulate the phase separation properties of RNAs and proteins, interfering with the composition and/or the material properties of biomolecular condensates and resulting in the formation of abnormal aggregates. Since several repeat expansions have arisen in genes encoding crucial players in transcription, this raises the hypothesis that wide gene expression dysregulation is common to multiple repeat expansion diseases. This review will cover the impact of these mutations in the formation of aberrant aggregates and how they modify gene transcription. Al. Al–dipoShort repetitive motifs, such as polyQ or polyA repeats, are low complexity regions, usually polymorphic, that form blocks of equal types of interactions thereby increasing multivalency, a feature normally associated with the formation of biomolecular condensates ,61. Intecis-regulatory elements. The formation of these concentrated dynamic compartments increases the proximity between components needed for transcriptional activation. Several TFs  have low complexity regions that allow them to phase separate and form discrete nuclear puncta in cells [in vivo  and co-activators. Their formation contributes to approximate the transcriptional machinery, DNA template, and respective in cells ,73,74,75in cells ,51. Furtin cells  and medi[in vivo  [40,47,7toxicity . As the toxicity ,85. In ftoxicity . In Alzhtoxicity . Thus, tHOXD13, HOXA13, RUNX2, and TBP, which are genes with repeat expansions encoding polyA or polyQ, associated with synpolydactyly, hand-foot genital syndrome (HFGS), cleidocranial dysplasia (CCD), and spinocerebellar ataxia type 17 (SCA17), respectively. For the HOXD13 protein, alanine repeat expansions were shown to enhance the phase separation capacity of its IDR, resulting in a decreased capacity of this TF to co-condense with mediator, a phenomenon the authors named “condensate unblending” [As the IDRs of proteins are known to favor protein clustering and, thus, drive LLPS, alterations in these regions might affect the formation of condensates. Repeat expansions of low complexity regions can modify the conformational features of the protein, interfering with protein/protein interactions, altering the phase separation capacity of these proteins, and, consequently, disrupting the formation of condensates or changing their material properties. It has been recently reported that repeat expansions occurring in IDRs of several TFs alter their phase separation capacity and capacity to co-condense with the transcription machinery. Basu et al., 2020  demonstrlending” . This colending” . Intereslending” . Curiouslending” . Thus, iThere is evidence of wide transcriptional dysregulation in several repeat expansion diseases, which are often called transcriptionopathies. Several of the disease-associated genes harbor pathogenic repeats that, when expanded, may alter the transcription of that specific gene and/or cause expression alterations in their target genes due to abnormal interactions with important players in the transcriptional machinery .>40 in the ATXN1 gene, which results in the expression of a protein with an expanded polyglutamine [SCA1, the first SCA with the gene assigned to a chromosomal location, is caused by an expanded (CAG)lutamine . Mutant lutamine , impairilutamine ,99,103. One of the first mechanisms explaining the transcriptional alterations in SCA1 was based on the role of PQBP1, which is a binding partner of RNA pol II that activates transcription . This prAs shown in The alteration of phase separation capacity by repeat expansions in coding gene regions is easily understandable. However, in noncoding gene regions, a question remains to be answered: How can a repeat expansion lead to alterations in phase separation capacity with consequent changes in gene transcription?RNA molecules play important functions in the formation of different cellular condensates, such as the nucleolus, nuclear speckles, paraspeckles, and stress granules . RNA cann in HD and SCAs (CUG)n in myotonic dystrophy and (GGGGCC)n in FTD/ALS, when expanded above a given threshold, can establish multivalent intermolecular base-pairing and electrostatic (requiring Mg2+) interactions, leading to the formation of nuclear puncta via LLPS or solid–gel transition [in vitro proving that both interactions are essential for RNA foci formation. The importance of RNA secondary structures in phase separation has also been reported by Fay and collaborators (2017) [in vitro [Furthermore, the length and sequence of RNAs are also important for the regulation of condensate formation as these factors are crucial for the binding capacity of RBPs. While high concentrations of RNA can dissolve phase-separated compartments , the samansition . The auts (2017) , showingin vitro ,138,139.in vivo, impairing their normal functions. It is widely known that RNA foci can sequester RBPs, key regulators of RNA metabolism, such as mRNA splicing [C9Orf72-FTD/ALS, in which RBP sequestration by RNA aggregates has been shown to be responsible for the formation of a variety of mRNA isoforms, completely changing the transcriptome in neuronal cells [PAPBN1 mutation leads to 3′-UTR shortening, and for FXS, caused by a repeat expansion in FMR1 that affects the choice of polyA signals [However, it is not known to what extent the RNA aggregates are toxic by themselves. The neurotoxicity issues appear when phase-separated RNA aggregates sequester key proteins splicing  and polysplicing ,142, andsplicing . Thus, tal cells , while a signals . The impConsidering that RBP sequestration, in abnormal phase-separated nuclear aggregates, is one of the major contributors to repeat expansion diseases and is identified in noncoding repeat disorders when transcription of the expanded repeat occurs , more efLLPS is crucial for many cellular processes such as the assembly of the transcriptional and mRNA processing machinery. It is becoming evident that changes in the propensity for local LLPS in specific cells or its precursors leads to aberrant RNA and protein aggregates, either nuclear or cytoplasmic, which are commonly associated with repeat expansion diseases. Alterations in protein:protein and protein:RNA interactions cause changes in condensate dynamics that consequently induce transcriptional and/or mRNA metabolism dysregulation. When these alterations occur in progenitor, neuronal, or muscle cells, they can lead to changes in the expression of cell-specific genes essential for developmental, brain, or muscle function.To better understand aberrant condensate formation in repeat expansion diseases, the assessment of sense and antisense gene expression levels of disease-associated genes in affected cells and tissues from asymptomatic and affected subjects during development to aging is imperative. The use of autopsy material from affected individuals is important to explore disease outcomes. However, this material is difficult to access and only allows depiction of the end stage of the disease, which can be a result of cellular responses to the accumulation of toxic structures and not specifically the response to the repeat expansion itself. While this limitation hampers the advances in knowledge of the pathogenic mechanisms of repeat diseases, the use of animal models is crucial to better understand the molecular and cellular basis of disease and have provided an enormous contribution to this field.In noncoding repeat expansion diseases, LLPS contribute to the formation of abnormal RNA aggregates with the sequestration of RBPs. The aberrant aggregation caused by alterations in the phase-separation behavior of these RNA:protein interactions results in an impairment of RBP function, which affects the pre-mRNA processing mechanisms, alters their binding to the transcriptional machinery or to the promoter itself, and inhibits their interaction with specific transcription factors. Altogether, this leads to a dysregulation of important players in transcription that can directly affect the formation of transcriptional condensates and alter the nuclear speckle conformation leading to mis-splicing and defective polyadenylation events, all resulting in a modification of the transcriptional profile.As such, it is vital to focus on understanding the molecular processes underlying the formation of LLPS aberrant condensates present in affected cells of subjects with repeat expansion diseases because the propensity for phase separation of RNAs, proteins, and RBPs is likely affecting the expression of genes in these cells and is at the core of pathogenicity.


Caenorhabditis elegans. To facilitate the analysis of ATAC-seq data, we introduced two tools: SRAlign, an extensible NGS data processing workflow, and SRAtac, a customizable end-to-end ATAC-seq analysis pipeline. Our results revealed highly comparable changes in chromatin accessibility following both RPOA-2 and GRWD-1 perturbations. However, we observed a weak correlation between changes in chromatin accessibility and gene expression. While our findings corroborate the idea of a feedback mechanism between ribosomal RNA synthesis, nucleolar ribosome large subunit biogenesis, and chromatin structure during the L1 stage of C. elegans development, they also prompt questions regarding the functional impact of these alterations on gene expression. The nucleolus is a large nuclear body that serves as the primary site for ribosome biogenesis. Recent studies have suggested that it also plays an important role in organizing chromatin architecture. However, to establish a causal relationship between nucleolar ribosome assembly and chromatin architecture, genetic tools are required to disrupt nucleolar ribosome biogenesis. In this study, we used ATAC-seq to investigate changes in chromatin accessibility upon specific depletion of two ribosome biogenesis components, RPOA-2 and GRWD-1, in the model organism The online version contains supplementary material available at 10.1186/s12864-023-09576-y. While many general next-generation sequencing (NGS) analysis pipelines have been developed to simplify and increase the reproducibility of data analysis, there is still a need for fully customizable end-to-end pipelines that can cater to specific data types. One widely used NGS analysis technique is ATAC-seq, which identifies regions of high chromatin accessibility across the genome and enables differential analysis of chromatin structure between groups , 2. HoweHere, we present SRAlign and SRAtac, two NGS data processing pipelines for ATAC-seq analysis that are reproducible, configurable, and extensible. Built on the Nextflow platform , SRAtac Caenorhabditis elegans, a commonly used model organism for genome-wide ATAC-seq and ChIP-seq experiments, SRAtac offers unique functionalities not found in existing approaches. These functionalities include accounting for bacterial contamination, which is important since C. elegans is typically grown on a lawn of E. coli.To better analyze ATAC-Seq data from C. elegans. The relationship between nuclear chromatin and ribosomal RNA (rRNA) transcription is an essential aspect of cellular function, as it is directly related to the regulation of gene expression and protein synthesis. rRNA transcription occurs in the nucleolus, a specialized subnuclear compartment where early ribosome biogenesis takes place. The nucleolus forms around tandemly repeated ribosomal DNA (rDNA) sequences, which are transcribed by RNA polymerase I (Pol I) to produce rRNA.There is increasing evidence suggesting that rRNA transcription and ribosome biogenesis can impact global chromatin structure and dynamics. For instance, changes in ribosomal DNA (rDNA) copy numbers, which can affect nucleolus size, have been linked to the loss of heterochromatin-induced gene silencing throughout the genome , 10. ThiC. elegans [C. elegans could have broader implications for our understanding of how cells regulate gene expression and adapt to changes in their environment.To address these potential mechanisms, we depleted the RNA polymerase I component, RPOA-2, and a nucleolar ribosome biogenesis factor, GRWD-1, in embryogenesis and the first larval stage of post-embryonic development in  elegans . We hypoC. elegans [Our findings demonstrate that the depletion of proteins involved in ribosomal RNA transcription and nucleolar ribosome large subunit biogenesis leads to similar changes in chromatin accessibility during the L1 stage of post-embryonic development. We specifically observed comparable changes in chromatin accessibility upon the specific depletion of RPOA-2, and GRWD-1 in  elegans . These rIt is known that the nucleolus is an essential organelle that plays a critical role in ribosome biogenesis, which is necessary to produce functional ribosomes and the regulation of protein synthesis. Our results suggest that nucleolar ribosome synthesis also has a significant impact on the structure of chromatin, which can play a crucial role in shaping the gene expression program of cells. This is particularly important during post-embryonic development, as the organism is transitioning from a growth quiescence capable stage to adulthood with fully differentiated somatic and germline tissues. Our results highlight the importance of studying the relationship between ribosome biogenesis and chromatin structure to gain a deeper understanding of the regulation of gene expression.SRAlign and SRAtac are two pipelines for NGS data processing that are built on the Nextflow platform. Nextflow is a workflow management software that simplifies the development of portable and reproducible bioinformatics workflows . These pSRAlign consists of a central processing workflow that carries out a range of NGS pre-processing steps, including read quality control, sequence alignments, library complexity, and sample reproducibility Figure S. This woSRAtac is an extension of SRAlign specifically designed for ATAC-seq data analysis , 2. SRAtProcessing steps include filtering uninformative reads , generating coverage tracks in bigWig format, peak calling with MACS2 , peak mehttps://github.com/trev-f/SRAlign,https://github.com/trev-f/SRAtac), allowing users to access the code, make modifications, and contribute to the development of these pipelines. Overall, these pipelines provide fully customizable and reproducible NGS data processing pipelines for specific data types, making them valuable tools for researchers in the field of genomics.Both SRAlign and SRAtac are available as public repositories on GitHub,  and proC. elegans . Our an2715418, . Insert Our evaluation of publicly available datasets demonstrates that SRAtac accurately captures important characteristics of ATAC-seq data. Notably, these results also indicate that SRAlign is not limited to processing RNA-seq data, as it performs equally well on DNA-seq data. Therefore, SRAlign is a versatile and adaptable tool for short read NGS alignment, capable of serving as the foundation for more specialized pipelines.C. elegans. The AID system utilizes an AID degron cassette integrated into the genomic loci of a coding region of interest, where its protein product can be depleted upon expression of an auxin receptor F-box protein TIR1 and in the presence of auxin  [rpoa-2) and the C-terminus of a chaperone protein coding gene (grwd-1/Y54H5A.1) required for nucleolar export and assembly of RPL-3 to the large ribosomal subunit [C. elegans strains expressing degron-GFP integrated RPOA-2 and GRWD-1 exhibited homozygous viability with nucleolar RPOA-2 and nuclear GRWD-1 localization patterns  system to specifically target RNA polymerase I and nucleolar large ribosome assembly at L1 stage in ic acid) , 23. We  subunit . The resibosomes  but becoibosomes . At thisDuring embryogenesis, nucleolar assembly dynamics are altered without ribosomal RNA transcription, but passive phase separation of nucleolar components still occurs , 26. ConTo assess whether nucleolar ribosome biogenesis affects chromatin structure, we used ATAC-seq to measure chromatin accessibility changes upon depletion of RPOA-2 and GRWD-1. To define differentially accessible regions (DARs) upon RPOA-2 and GRWD-1 depletion, counts of reads within peaks were produced by SRAtac v0.5.0, which were then separately run through the DESeq2 workflow with batch effects controlled for by RUVseq .After removal of unwanted variations likely due to batch effects, principal component analysis showed that samples were primarily divided into two groups: those with a depleted ribosome biogenesis component and those without Figure SA, B. Difp < 1e-81; hypergeometric test)   Fig. A. Differst) Fig. B. The dist) Fig. C. Log2 fon) Fig. D. Clusteon) Fig. E, F. AltC. elegans regulatory elements [To understand what type of genomic regions were affected at the chromatin level upon GRWD-1 and RPOA-2 depletion, ATAC-seq peaks were merged with a list of experimentally determined elements . SignifiWe questioned whether DARs were enriched in any motif sequences. We discovered that the increased accessibility of regions upon RPOA-2 and GRWD-1 depletion corresponded with a significant enrichment of 3 similar motifs . In contrast, a mere 6% of all ATACseq peaks coincide with EOR-1 bound regions [As significantly more accessible DARs upon RPOA-2 and GRWD-1 depletion are slightly biased towards promoters and are enriched for multiple motif sequences, we searched for similar motif sequences among known transcription factor binding sites. Among regions with increased accessibility, a distinct motif, GAGACGCAGASA, is notable for its significant resemblance to the transcription factor EOR-1 Fig. A, right. regions . Consequ regions , (2) dri regions , 34, and regions . These oWe further investigated whether other transcription factors or histone modifications were significantly enriched among DARs following the depletion of RPOA-2 and GRWD-1, using the ChipAtlas database . We focuAmong the histone modifications, the most prominent one was H3K4 methylation. H3K4 methylation status has previously been reported to be associated with genome stability and DNA damage , 37. It rpoa-2::degron background with globally expressed TIR1 or absent altogether. DESeq2 analysis was performed on RNA-seq counts of reads within genes to identify differentially expressed genes (DEGs). To directly compare the ATAC-seq dataset with the RNA-seq dataset, ATAC-seq peaks were paired with their associated genes through C. elegans regulatory elements  . The presence of a low correlation is not surprising as (1) most of the open regions likely require the presence of a transcription factor or enhancer for active transcription, and (2) posttranscriptional processes such as splicing, and RNA stability likely also affect mature RNA transcript levels in the cytoplasm. Given these caveats, significant but low correlation between ATAC-seq and RNA-seq suggests the presence of a relationship between nucleolar ribosome biogenesis and chromatin, which has an impact on overall gene expression in C. elegans L1 larvae.The correlation between the log2 fold change of ATAC-seq peaks and their associated cytoplasmic gene expression is low but significant  terms . Among tWhile there is a low global correlation between ATAC-seq and RNA-seq datasets, the correlated genes had significant enrichment in functional categories suggesting the role of chromatin organization in regulating functions such as stress response, metabolic process, and development.C. elegans research community. Secondly, SRAtac is organism and reference genome agnostic, meaning that any custom reference genome can be integrated, which is an improvement over other pipelines such as AIAP [C. elegans. Thirdly, SRAtac can run in either end-to-end mode (from raw reads to counts within peaks) or in post-alignment mode (from BAM files to counts within peaks), which provides additional flexibility for further analysis without requiring computationally expensive steps such as read trimming and alignments to be repeated. This modular architecture enhances the existing pipelines that require aligned files such as ataqv [SRAlign is a flexible and reliable next-generation sequencing data processing workflow that can efficiently process both RNA-seq and DNA-seq data. SRAtac, built on top of SRAlign, offers a unique set of features that are not available in other ATAC-seq pipelines. Firstly, SRAtac includes a built-in analysis of contaminants which is particularly useful for the  as AIAP  and the  as AIAP  that havas ataqv  or thoseas ataqv  or PEPATas ataqv . FinallyC. elegans has a feedback mechanism between ribosome biogenesis and chromatin structure. Disruptions in ribosomal RNA transcription and nucleolar large subunit assembly result in altered chromatin structure in L1 larvae. Selective depletion of either of the ribosome biogenesis factors – the RNA Pol I subunit RPOA-2 or GRWD-1, which is responsible for large subunit biogenesis – causes changes in chromatin accessibility at specific regions. Interestingly, most of these regions have increased accessibility, suggesting that heterochromatin is disrupted  [Chromatin accessibility changes were highly similar overall between RPOA-2 depleted larvae and GRWD-1 depleted larvae, indicating that the mechanism by which these two ribosome biogenesis components alter global chromatin accessibility is likely similar at the L1 stage. These regions are notably enriched (~ 40%) within the binding areas of the transcription factor EOR-1. Serving as a positive regulator of Ras/Wnt signaling pathways, EOR-1 is instrumental in the lineage specification of neurons, vulval cells, and excretory cells –34. Thesrtholog) . ConsequAmong histone modifications, we found significant overlap between regions that become differentially more accessible following GRWD-1 and RPOA-2 depletion and regions enriched in H3K4me2 following UV exposure . SimilarChromatin accessibility changes were only weakly correlated with gene expression changes, suggesting that chromatin accessibility changes alone are not sufficient for transcription activation. Furthermore, it is likely that other factors, such as the availability of transcription factors or post-transcriptional processes, also contribute to gene expression changes. A limitation of this study is that it relies heavily on the results of one assay, ATAC-seq, which has limited power to answer certain questions due to technical and analytical constraints. For example, ATAC-seq only provides an indication of the overall openness or closedness of chromatin. Potentially informative techniques to further probe these questions include chromosome conformation capture derived NGS techniques to interrogate global genome contacts, and imaging techniques such as 3D ATAC-PALM and immunofluorescence imaging of chromatin marks to visualize changes in chromatin accessibility and spatial distributions within the nuclear volume.Overall, our results suggest the existence of a feedback loop to chromatin upon disruption of ribosome biogenesis, specifically at the stages of ribosomal RNA transcription and nucleolar large subunit assembly during the L1 stage of post-embryonic development. Considering that the depletion of RPOA-2 influences the overall levels of ribosome synthesis, including 40S, it is reasonable to hypothesize that this mechanism extends to other aspects of nucleolar ribosome biogenesis. This feedback mechanism likely plays a role in regulating functions such as stress response, metabolic process, and development, as suggested by the enrichment analysis of differentially accessible genes , 45, 46.https://github.com/trev-f/SRAlign, https://github.com/trev-f/SRAtac).SRAlign and SRAtac are available as a public repository hosted on GitHub (Project name: e.g. My bioinformatics project.http://sourceforge.net/projects/mgedProject home page: e.g. Operating system(s): e.g. Platform independent.Programming language: e.g. Java.Other requirements: e.g. Java 1.3.1 or higher, Tomcat 4.0 or higher.License: e.g. GNU GPL, FreeBSD etc.Any restrictions to use by non-academics: e.g. license needed.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213367, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218280, respectively.ATAC-seq and RNA-seq libraries are available through NCBI GEO, with accession code GSE218280 and GSE213367, with these links: C. elegans strains were maintained and grown at 16 °C or 20 °C on peptone enriched NGM agar plates seeded with a lawn of E. coli OP50 bacteria. To obtain synchronized embryos, a standard bleaching procedure was used.Datasets GSM3142773 and GSM3142774 for SRAlign and GSM2715417 and GSM2715418 for SRAtac were downloaded as raw reads fastq files from the Sequence Read Archive. Raw reads were sampled by processing both ends of the paired end reads files with the command line tool `seqtk sample` and option `-s100` to set the seed and preserve read pairs. For pipeline testing, datasets were sampled to 200,000 reads each for both SRAlign and SRAtac testing. These sampled datasets were exclusively used for this purpose.RNA-seq reads were run through the SRAlign workflow version v1.0.2 to align reads to reference genome assembly ce10. The run was performed using the Docker profile with default parameters except `–alignmentTool hisat2 –skipPreseq` were used to perform the alignment of reads to the reference genome with HISAT2 and avoid errors with Preseq that can occur when alignment is performed with HISAT2. Visualizations for the dataset QC were saved directly from the final MultiQC report, and genome browser plots were produced using plotGardener .48. Accessible sites were obtained from a published report compiled from accessible regions at numerous developmental and aging timepoints [ATAC-seq reads were run through the SRAtac workflow version v0.5.0 to align reads to reference genome assembly ce10. The run was performed using the Docker profile with all default parameters. Visualizations for the dataset QC were saved directly from the final MultiQC report, and genome browser plots were produced using plotGardener mepoints .tir-1 and 24 h for strains with tir-1 as these have a growth delay. Synchronized larvae were collected in 50 mM NaCl. Bacteria were cleaned from worms by sedimentation through 5% sucrose in 50 mM NaCl at 400 g for 1 min with subsequent washes in 50 mM NaCl. At least 40,000 larvae were collected for each condition. Batches of larvae were evenly split into four replicates and buffer was exchanged to Nuclei Wash Buffer (NWB): 10 mM Tris–HCl pH 7.5, 40 mM NaCl, 90 mM KCl, 3 mM MgCl2, 0.2 mM DTT, 0.5 mM Spermidine, 0.25 mM Spermine, and 1 X Protease Inhibitor Cocktail. Larvae pelleted in NWB were snap frozen in liquid nitrogen and stored at -70 °C.Bleach synchronized embryos were placed on NGM agar plates with a lawn of OP50 bacteria and supplemented with 1 mM auxin. Larvae were synchronized at the L1 stage by allowing the embryos to hatch and develop at 23 °C for 18 h for strains with no  g at 4 °C. To pellet nuclei, the supernatant was taken from the low speed spin and sedimented for 10 min at 2000 g at 4 °C. Meanwhile, more nuclei were released by a second round of 25 strokes with pestle B, a short rest on ice, and 25 more strokes with pestle B. Debris was pelleted as above, and nuclei were pelleted as above in the same tube as the previous nuclei pellet. Nuclei yields were further increased with light sonication. Debris was resuspended in 300 μL total NPB and sonicated in a Diagenode Bioruptor on Low power for 2 cycles of 30 s ON / 30 s OFF. Volume was brought to 1 mL total, and debris was removed, and nuclei were pelleted in the same tube as above. Nuclei were subjected to a buffer exchange and cleanup step to further remove any debris. The nuclei pellet was resuspended in 1 mL Tagmentation Reaction Buffer (TRB): 1 X Tagment DNA buffer , 0.33 X PBS, 0.01% Digitonin, 0.1% Tween-20. Debris was pelleted as above, and the supernatant was moved to a new tube and nuclei were pelleted as above. Nuclei were cleaned a final time by resuspending in TRB and pelleting. The supernatant was carefully removed to leave about 50 – 60 μL of supernatant without disturbing the nuclei pellet.In order to minimize the potential confounding effects of batch on the experimental design, one replicate of each of the strains was processed concurrently for four total batches of four replicates. To isolate nuclei from larvae, worm pellets were thawed on ice. Buffer was transferred for thawed larvae to Nuclei Prep Buffer (NPB): 10 mMTris-HCl pH 7.5, 40 mM NaCl, 90 mM KCl, 3 mM MgCl2, 0.2 mM DTT, 0.5 mM Spermidine, 0.25 mM Spermine, 0.1% Triton X-100, 0.1% IGEPAL CA-630, and 1 X Protease Inhibitor Cocktail. Worms were transferred to a 2 mL dounce homogenizer that was pre-chilled in the refrigerator and kept on ice. Worms were initially broken by 15 strokes with homogenizer pestle A, a short rest on ice, and 15 more strokes with pestle A. Nuclei were released from broken worms by 25 strokes with pestle B, a short rest on ice, and 25 more strokes with pestle B. Debris was pelleted by a low speed spin of the homogenate for 5 min at 100Nuclei were stained with DAPI and counted using a hemocytometer. Nuclei were tagmented by taking an aliquot of 50,000 nuclei, bringing the total reaction volume to 47.5 μL with TRB, and adding 2.5 μL TDE1 tagmentase. The tagmentation reaction was incubated for 30 min at 37 °C with shaking. Immediately after the reaction concluded, reactions were cleaned up using the Zymo DNA Clean & Concentrator-5 kit.Cleaned up tagmented DNA was added to a PCR reaction mix with 1.25 µM each Nextera i5 and i7 adapters and NEBNext High-fidelity 2X PCR Master Mix. Libraries were amplified with an initial extension and denaturation step of 5 min at 72 °C and 30 s at 98 °C followed by 12 cycles of 10 s at 98 °C, 30 s at 63 °C, and 1 min at 72 °C, with a final extension of 10 min at 72 °C. Adapter contamination was initially removed with the Zymo DNA Clean & Concentrator-5 kit, and library size distributions were checked by running on a gel. Libraries were subjected to a final cleanup with 1.8 X AMPure XP beads.C. elegans ribosomal RNA were used to deplete rRNA from 100 ng total C. elegans RNA [RNAseq library preparation is discussed . Larvae gans RNA . RNA-seqC. elegans reference assembly WBcel235 and contamination levels checked against the E. coli genome assembly EB1. Reads were trimmed using fastp with the Nextera adapter sequence specified. Trimmed reads were aligned using bowtie 2 with parameters `–very-sensitive -X 2000`. Duplicate reads were marked with SAMBLASTER [ATAC-seq raw reads were processed using the SRAtac pipeline version v0.5.0 with the Docker profile on the Google Cloud Life Sciences executor. Reads were aligned to MBLASTER , and SAMMBLASTER . BAM fil52. The set of empirical control peaks for `ruvg` were obtained by running DESeq2 on the counts matrix. Peaks with an adjusted p-value greater than or equal to 0.75 in the comparison rpoa-2::degron; eft-3p::tir-1 vs. rpoa-2::degron were extracted as empirical control genes. Two factors of unwanted variation were computed for the data. Differential peaks were called using DESeq2 with the two factors of unwanted variation and the genotype as variables in the design 28. The R package apeglm was used for shrinkage of log fold changes [The counts matrix of reads within peaks was read into R. The `ruvg` function from the R package RUVseq was used to remove unwanted variation that was likely due to batch effects  changes .Illumina Nextera XT protocol was followed by using a suggested amount of genomic DNA. The resulting PCR product was cut from the gel at approximately 300 bp. The gel was eluted using Zymo gel purification kit and sequenced on the same Novaseq platform.rpoa-2::tir-1 vs. rpoa-2 or grwd-1::tir-1 vs. grwd-1 as the sequences in which to find motifs. Transcription factor binding motifs were inspected and downloaded from JASPAR 2022 [Motifs were identified using the STREME software from MEME Suite (version 5.5.2) . To extrPAR 2022 .EOR-1 transcription factor binding bed format data from L1 larvae was downloaded from ChipAtlas. The bedtools intersect was used with parameters -wa and -u to count the number of differentially accessible regions (DARs) that intersected. Fisher's exact test was used for DARs that intersect or do not intersect with EOR-1 binding sites.For specific transcription factor and histone modification enrichments, Chip Atlas enrichment analysis tool was used . SpecifiAdditional file 1: Figure-S1. SRAlign consists of a central processing workflow that carries out standard NGS processing steps, and writes key output files. Quality control (QC) workflow generates QC metrics for reads, alignments, library complexity, and sample reproducibility. Intermediate MultiQC reports and a final pipeline report includes output files as summarized. Figure S2. SRAlign effectively aligns RNA-seq data to a reference genome. A. Percentage of reads aligned [ Figure S3. Validation experiments for RPOA-2 and GRWD-1 depletion. A. Depletion of GRWD-1::degron::GFP after 18 hours of auxin treatment. The numbers on top of the blot represent the ratio of GFP to actin. B. RT-qPCR showing the relative expression levels of the internal transcribed spacer of ribosomal RNA ITS1 and ITS2 in response to RPOA-2 depletion. The relative transcript levels were determined for each sample by normalizing them to mature 26S rRNA. Embryos of were treated with or without auxin for 18 hours. Data were obtained from three biological experiments with three technique replicates for each biological replicate. Statistical significance was determined using an independent t-test. Figure S4. Ribosome biogenesis disruption leads to significant changes in chromatin accessibility. A-B. PCA plots for the first principal components computed by DESeq2, prior to (A) and after (B) batch effect removal using RUVseq and limma::removeBatchEffect. Green: no RPOA-2 depletion (RPOA-2); dark green: RPOA-2 depleted  light orange: no RRB-1 depletion (RRB-1); dark orange: RRB-1 depleted  C-D. Number of differentially accessible regions (DARs) with increased–left– and decreased–right– accessibility for RPOA-2 (C) RRB-1 depletion (D). E-F. Heatmaps of log transformed normalized peak counts for the top 25 DARs in response to RPOA-2 (E) and RRB-1 (F) depletion. Figure S5. Most gene expression changes are not associated with chromatin accessibility changesVolcano plot of -log10 of adjusted p-value vs log2 fold change of ATAC-seq peaks overlapping annotated regulatory elements in response to RPOA-2 depletion. Points colored according to whether a particular peak is a differentially accessible region , or differentially expressed gene , or both (navy). aligned . B.Propo


Recent advances in next-generation sequencing and data analysis have provided new gateways for identification of novel genome-wide genetic determinants governing tissue development and disease. These advances have revolutionized our understanding of cellular differentiation, homeostasis, and specialized function in multiple tissues. Bioinformatic and functional analysis of these genetic determinants and the pathways they regulate have provided a novel basis for the design of functional experiments to answer a wide range of long-sought biological questions. A well-characterized model for the application of these emerging technologies is the development and differentiation of the ocular lens and how individual pathways regulate lens morphogenesis, gene expression, transparency, and refraction. Recent applications of next-generation sequencing analysis on well-characterized chicken and mouse lens differentiation models using a variety of omics techniques including RNA-seq, ATAC-seq, whole-genome bisulfite sequencing (WGBS), chip-seq, and CUT&RUN have revealed a wide range of essential biological pathways and chromatin features governing lens structure and function. Multiomics integration of these data has established new gene functions and cellular processes essential for lens formation, homeostasis, and transparency including the identification of novel transcription control pathways, autophagy remodeling pathways, and signal transduction pathways, among others. This review summarizes recent omics technologies applied to the lens, methods for integrating multiomics data, and how these recent technologies have advanced our understanding ocular biology and function. The approach and analysis are relevant to identifying the features and functional requirements of more complex tissues and disease states.  A long-studied model for identifying mechanisms controlling organ development and cellular differentiation is the ocular lens that functions to focus light on the retina. Key to its utility for revealing novel mechanisms of development and differentiation, the lens is composed of two cell types including a single layer of undifferentiated cuboidal epithelial cells that overlay a core of elongated terminally differentiated fiber cells . During Numerous elegant studies conducted over many years have identified important growth factors, signaling pathway, and downstream transcriptional mechanisms within the lens fiber cell nuclei regulating lens differentiation ,7,8,9. TInsights into the genome-wide gene expression occurring during lens development and differentiation have been generated using mainly chicken and mouse models. Other detailed studies of frog, zebrafish, and medaka fish models have also been reported ,87,88. DThe main difference between the chicken and mouse lens is the higher accommodative power of chicken and avian lenses that are adapted to providing the acute vision needed for flight and survival. By contrast, nocturnal mice have much more rigid lenses. The main lens structural proteins responsible for its transparency and light refraction are crystallins ,90,91,92Recent advances in multiomics approaches including RNA-seq, assay for transposase accessible chromatin with sequencing (ATAC-seq), whole-genome bisulfite sequencing (WGBS), chromatin immunoprecipitation with sequencing (ChIP-seq), and cleavage under targets and release using nuclease (CUT&RUN) have enabled the assembly of genome-wide data encompassing a wide-range of features and regulatory pathways important for lens differentiation ,103,104.RNA-seq is a powerful tool for analyzing gene expression during cellular differentiation due to its ability to provide the high-throughput, unbiased, and quantitative measurement of transcriptome-wide gene expression. The RNA-seq process involves isolation of total RNA from the sample of interest, cDNA synthesis, and sequencing of the generated cDNA using high-throughput sequencing technology . RNA-seqIn the lens, RNA-seq has been used to examine changes in gene expression during lens development and differentiation. Studies that have used RNA-seq analysis of lens differentiation mechanisms can be broadly split into two categories. RNA-seq studies comparing epithelial cells versus fiber cell transcriptomes provide insight into the gene expression changes that occur during the differentiation process ,112,113.Gallus gallus (chicken) eye lens. These differentiation-state-specific regions contain morphologically distinct cell populations including a monolayer of undifferentiated epithelial cells at the center of the lens anterior (EC cells), epithelial cells at the lens equator that withdraw from the cell cycle to initiate differentiation (EQ cells), a zone of newly formed fiber cells at the lens cortex that undergo a series of remodeling events including elongation (FP cells), and finally, a core of elongated fiber cells from which all organelles are eliminated and which make up the bulk of the lens (FC cells) . D. D192]. Whole-genome bisulfite sequencing (WGBS) has been instrumental in understanding the complex mechanisms of lens differentiation, as demonstrated by two recent studies examining the role of DNA methylation in this process ,104. TheIn a study by Disatham et al. in 2022, the authors used an E13 chicken lens model to investigate the genome-wide relationships between methylation at cytosines (mCG), chromatin accessibility, and gene expression during the differentiation of eye lens epithelial cells into fiber cells. They identified over 7000 genomic loci with significant differences in mCG levels between lens epithelial and fiber cells and found that these changes in mCG levels were inversely correlated with differentiation-state-specific gene expression and chromatin accessibility  as quantSimilarly, another study by Chang et al., 2023 investigated the dynamics of DNA methylation and chromatin changes during mouse lens fiber and epithelium differentiation between embryos (E14.5) and newborns (P0.5) using WGBS of microdissected lenses . By compIn summary, these genome-wide DNA methylation patterns between lens epithelial and fiber cells of both chicken and mouse lenses are highly associated with corresponding chromatin accessibility and gene expression patterns. Several transcription factor consensus sequences were identified in these studies. HIF1a was identified as a transcription factor bound to differentially methylated regions in both chicken and mouse lenses during lens differentiation, suggesting that HIF1a may play a conserved and significant role in the regulation of lens differentiation across species. This also implies that DNA methylation might be involved in controlling HIF1a-mediated gene expression during lens differentiation in these organisms. Indeed, previous studies have functionally validated the role of HIF1a-dependent gene regulation in lens cells  and haveTogether, these studies highlight the importance of whole-genome bisulfite sequencing in revealing the complex interplay between DNA methylation, chromatin accessibility, and gene expression during lens differentiation. The findings provide valuable insights into the molecular mechanisms underlying lens differentiation and pave the way for future investigations into the role of epigenetic mechanisms in this process.Other recent advances in genomic technologies and their much broader feasibility allow comparative studies of gene expression across multiple species and the confirmation of transcription factor binding predicted from integration of RNA-seq and ATAC-seq data . As tradRNA-seq, ATAC-seq, and other omics techniques provide a comprehensive view of the molecular landscape during lens differentiation. To identify novel mechanisms regulating lens differentiation, it is essential to integrate and compare the results from multiomics analyses with existing databases of genes with validated lens functions, such as CATMAP  and iSyTp > 0.05, hypergeometric test) epithelial cell genes and 47 of 995  fiber cell genes have known associations with cataracts. Additionally, 36 of 1239  epithelial cell genes and 74 of 995  fiber cell genes are known to be highly lens-enriched genes. These analyses reveal that epithelial cell genes are not as significantly associated with cataract-associated genes or lens enriched genes as compared to the significantly associated fiber cell genes. However, this does not rule out the epithelial cell gene functions required for lens differentiation, structure, and homeostasis. Indeed, several epithelial cell genes have well-defined roles in lens biology, such as Pax6. This analysis only assesses if there is an enriched relationship between the gene list of interest  and the phenotype (cataract-associated or lens-enriched). It is also important to emphasize that evolutionarily conserved genes that are not in the CATMAP or iSYTE lens-enriched databases do not eliminate them as genes with critical functions in lens processes. Instead, the list of genes that overlap with these databases  or lens-enriched genes (iSYTE). This can be evaluated using the Fisher’s Exact test of the hypergeometric test. These tests evaluate the probability that the observed overlap between the two lists occurs by chance alone. As an example, we used the list of evolutionarily conserved epithelial cell genes and fiber cell genes obtained from the cross-comparison of chicken  and mousatabases  should bIn conclusion, omics data analysis has revolutionized the understanding of biological systems, and multiomics integration has become a powerful approach for generating a comprehensive understanding of complex biological processes. In the context of lens differentiation, multiomics integration has allowed for the identification of novel regulatory mechanisms, novel transcription factors, and epigenetic factors driving gene expression changes required for differentiation. Cross-species comparisons between mice and chicken have further elucidated evolutionarily conserved mechanisms of lens differentiation. The identification of novel gene regulatory networks (GRNs) and biological pathways provides new insights into the mechanisms that regulate lens differentiation.Use of these technologies is in general “hypothesis-generating” research and typically leads to unexpected discoveries that are subsequently tested using gene loss-of-function and gain-of-function experiments using the most appropriate model organism(s). Another application of these methods is to compare human ES/iPS cell-derived organoids with authentic human tissues at multiple level of gene control and use of this knowledge to generate more advanced organoids for modeling of human diseases as well as for potential cell replacement therapies.cis-mutations in 5′-UTR of ferritin light chain  mRNA, known as the iron response element (IRE) for iron regulatory protein (IRP), which inhibits ferritin translation, cause congenital cataract [Current human genetic studies identify many variants outside of protein-coding regions or well established regulatory through mutations in 5’-UTRs of individual genes. For example, cataract . Likewiscataract . Long-racataract ,203,204,cataract ,206. In the last decade, remarkable progress has been made in the application of these technologies for single-cell analysis. The first breakthrough was development of drop-seq platform for single-cell transcriptomic analysis used to uncover even more complex heterogeneity of mouse retinal ganglion cells . In paraCryaa) locus [Not covered in this review but also important is the three-dimensional (3D) organization of individual chromosomes that can also provide clues for important gene regulatory events. Though not yet specifically established for the lens, remarkable compartmentalization patterns are involved directly or indirectly in transcriptional regulation . The fora) locus . In addia) locus ,217,218.Despite the numerous benefits of multiomics integration analysis, there are still several limitations and potential sources of bias that need to be addressed. Technical limitations include the quality of the omics data, the choice of statistical analysis methods, and the challenges of integrating multiple types of data. However, the strengths of this approach far outweigh its limitations, and advancements in technology and analysis methods will likely continue to improve the accuracy and reliability of multiomics integration analysis.In addition to advancing the field of lens research, the findings from multiomics integration analysis and cross-species comparisons can have broader implications for other fields. For example, the identification of novel gene regulatory networks and biological pathways can shed light on the mechanisms that drive other tissue differentiation processes. Similarly, cross-species comparisons can be used to identify evolutionarily conserved mechanisms in various biological processes, ranging from development to disease. Therefore, the integration of multiomics data and cross-species comparisons will likely continue to be a valuable tool for understanding complex biological systems and developing novel therapeutic targets in disease models.