Cap analysis of gene expression (CAGE) tag sequencing from left ventricles of a rat model of hypertension identifies genes that use alternative promoters in a disease condition. Insr promoter usage was significantly associated with insulin levels and blood pressure within a panel of HXB/BXH recombinant inbred rat strains, suggesting that hyperinsulinemia due to insulin resistance might lead to hypertension in SHR. Our study provides a preliminary evidence of alternative promoter usage in complex diseases.The role of alternative promoter usage in tissue-specific gene expression has been well established; however, its role in complex diseases is poorly understood. We performed cap analysis of gene expression (CAGE) sequencing from the left ventricle of a rat model of hypertension, the spontaneously hypertensive rat (SHR), and a normotensive strain, Brown Norway to understand the role of alternative promoter usage in complex disease. We identified 26,560 CAGE-defined transcription start sites in the rat left ventricle, including 1,970 novel cardiac transcription start sites. We identified 28 genes with alternative promoter usage between SHR and Brown Norway, which could lead to protein isoforms differing at the amino terminus between two strains and 475 promoter switching events altering the length of the 5′ UTR. We found that the shift in  Mammalian gene transcription is tightly regulated. The core promoter, defined as a region ∼40 bp upstream and downstream of the transcription start site (TSS) is sufficient for initiation of the transcription . The genThe transcriptional diversity due to alternate promoters could be assessed by precisely mapping 5′ ends of the transcripts. Cap Analysis of Gene Expression (CAGE), which takes advantage of the 7-methylguanosine cap structure of the transcripts, allows accurate genome-wide mapping of TSSs at single base-pair resolution , 2006. UThe spontaneously hypertensive rat (SHR) strain is one of the most widely used animal models to study hypertension, which also shows many metabolic phenotypes, including insulin resistance, dyslipidemia, and central obesity, collectively known as metabolic syndrome (MtS) . By crosWe performed CAGE tag sequencing from left ventricles (LVs) of SHR and BN rat strains. Along with precisely mapping TSSs in rat heart, we also show that a large number of genes use alternate promoters between the SHR and BN strains, suggesting a role of alternative promoter usage in complex disease.To understand the role of alternate promoter usage and promoter shift in complex diseases, we performed CAGE tag sequencing from the LVs of the rat model of hypertension SHR and a normotensive rat strain BN in three replicates each from both male and female rats. We performed “non-amplifying, non-tagging Illumina CAGE” (nAnT-iCAGE) because no tagging protocol allows the sequencing of longer reads (100 bp) and a non-amplification protocol eliminates biases due to PCR amplification . On averCAGE-defined promoters were annotated by overlapping them with the rat gene annotations from ENSEMBL (Version 93) . We defiUsing CAGE, the FANTOM consortium has mapped precise TSSs across multiple tissues and primary cells of mouse and humans . In addiTo compare the rat heart CAGE data with the human and mouse CAGE data from the FANTOM consortium, we first identified human (hg19) and mouse (mm9) genomic regions orthologous to the rat heart CAGE-defined promoter regions (see Methods). A total of 15,729 (59.22%) rat heart CAGE-defined promoter regions overlapped with the mouse heart CAGE tag clusters, whereas 12,387 (46.64%) rat heart promoters overlapped with the human heart promoters identified by the FANTOM consortium using CAGE. In addition, a small proportion (n = 140 and n = 214) of the rat heart CAGE tag clusters overlapped with the mouse and human CAGE promoters from the non-cardiac tissues. This result suggests the significant conservation of promoter usage between humans, mouse, and rat heart.P < 2.2 × 10−16 and P < 2.2 × 10−16, respectively; P = 0.4066, The promoters can be broadly classified into two categories: TATA box–rich and GpG-rich promoters. More than half of the mammalian genes initiate transcription from GpG dinucleotide-rich region referred to as CpG islands . MoreoveWe evaluated CAGE-defined TSSs against the ENSEMBL TSS annotations for the rat genome. Of 21,353 CAGE-defined TSS that were within the gene regions, only 35.71%  matched perfectly with the ENSEMBL TSS annotations. Additional 1,965 tag clusters were within the 100 bp of ENSEMBL annotated TSS, whereas 3,827 tag clusters were between 100 bp to 1,000 bp away from the ENSEMBL annotated TSS. Surprisingly, no TSS annotations were found in ENSEMBL for the remaining 37%  tag clusters that were located within the protein coding gene regions and were on the same strand as that of the gene . Most ofList of CAGE-defined tag clusters located in gene body win no ENSEMBL transcription start site annotation but supported by de novo transcript assembly.Table S1 Next, we investigated whether genic rat heart CAGE tag clusters without ENSEMBL TSS annotations were represented in other rat cell types/tissues or in cardiac tissue from human and mouse. Of 7,934 tag clusters, a small proportion of tag clusters (n = 730) were also present in FANTOM rat CAGE data. Furthermore, 2,326 rat heart promoters overlapped with mouse heart promoters, and 2,177 overlapped with human heart promoters. In total 3,317 (41.81%) rat heart tag clusters located within the gene regions with no ENSEMBL TSS annotations overlapped with CAGE-defined promoters either from other rat tissues, human heart, or mouse heart. This suggests that most unannotated genic rat heart CAGE tag clusters indeed represent ubiquitous and/or conserved cardiac TSSs.Mef2c), a member of the MEF2 family of transcription factors, is a key regulator of cardiovascular development . Interestingly, no CAGE tag cluster from rat heart overlap with the ENSEMBL annotated TSS of Mef2c. On the contrary, three CAGE-defined tag clusters were located within the Mef2c gene body. The de novo transcripts assembly identified six transcripts for Mef2c with three distinct TSSs, all of them overlapped with the CAGE-defined TSSs (Mef2c transcript that shows the highest expression (58.44 TPM) at the promoter was located 20 kb downstream to the ENSEMBL annotated TSS. The Mef2c_P1 was present in FANTOM rat cage data, whereas all three rat Mef2c promoters were observed in mouse and two of them were present in human CAGE data. Human and mouse orthologs were annotated as Mef2c promoters in both species. Furthermore, their activity levels were also conserved as mouse and human orthologous regions of predominant (P1) rat Mef2c promoter were also the most predominant promoter in both species. However, in ENSEMBL, none of the three CAGE tag clusters were annotated as a Mef2c TSS. This suggests that our CAGE data could help in improving TSS annotations of important cardiac genes in rat.A gene myocyte enhancer factor 2  and the second strongest promoter of the gene P2; , suggestList of intergenic CAGE-defined tag clusters with supported by de novo transcript transcription start site.Table S2 Plekhg1, no CAGE tag cluster was found at the ENSEMBL annotated TSS. The nearest CAGE tag cluster to gene Plekhg1 was more than 100 kb upstream to ENSEMBL TSS, which was supported by our de novo transcript assembly . For gene assembly . For theated TSS . This noIn summary, our results show that rat heart transcript annotations could be significantly improved using CAGE data by precisely mapping TSSs.To understand the role of alternative promoter usage in disease, we explored CAGE tag data from SHR and BN rat strains. We hypothesised that if a gene is predominantly transcribed from two different promoters in SHR and BN, respectively, then the tag clusters associated with both promoters should show differential expression but with expression difference in the opposite direction. A total of 4,490 (41%) of the heart-expressed genes showed more than one CAGE-defined promoter. We selected the promoters that showed a minimum of 20% expression of the total expression of all promoters of a gene. The majority  of the genes had only one predominant promoter, whereas 918 genes had at least two promoters with more than 20% activity. Of these 918 genes, for 471 genes none of the promoters show any difference in expression between SHR and BN, whereas for 447 genes at least one promoter showed statistically significant differential expression. For 419 genes, all the promoters associated with a gene showed expression differences in the same direction, whereas for 28 genes, at least two promoters showed differential expression in the opposite direction , suggestSynpo2l, Abhd16a, Ece1, Shroom3  and chloride transport (P = 0.005). The genes that showed a switch in TSS usage within the same promoter between SHR and BN include Insr, Endog, Vnn1, and Serpina3c.It has been shown that the switch in TSS usage within the same promoter, often within 100 bp, happens between maternal and the zygotic transcript during zebra fish development . We inveOf 475 heart transcripts that showed a shift in the TSS usage between SHR and BN, human orthologous regions of 182 promoters showed CAGE tag signals in FANTOM data. Of these 182 transcripts, for 89 transcripts only one of the two (SHR or BN) promoters was represented in humans, whereas both SHR and BN promoters were represented in humans for 93 transcripts. Similarly, a total of 200 rat shifting promoters were conserved in mouse. In mouse, 144 transcripts showed both SHR and BN promoters, whereas 56 mouse transcripts showed only one of the two (SHR or BN) promoters.The transcripts in which the shift in TSS usage between two rat strains was observed produce a longer transcript in one strain compared with a shorter transcript in another, in most cases affecting the length of the 5′ UTR. Being inbred rat strains, SHR and BN are completely homozygous, hence the F1 cross between SHR and BN rat strains tend to be heterozygous at all the variable loci between SHR and BN. The F1s inherit both longer and shorter transcripts of the genes that show promoter switching between the two rat strains, thus both the promoters show intermediate expression in F1s . We reasP < 2.2 × 10−16). In F1s, allelic imbalance in shifting promoters was comparable to the allelic imbalance of variants located in promoters that were strongly differentially expressed  in parental strains , of which 110 variants were located in shifting promoters, remaining in normal promoters. The variants located in shifting promoters showed a strong allelic imbalance in F1s  as compaInsr), a transmembrane protein with tyrosine kinase activity . In SHR rat strain, the TSS was observed 414 bp upstream  to the Insr start codon, resulting in a longer transcript in SHR with extended 5′ UTR as compared with BN. To validate this observation, we investigated RNA-seq data from SHR and BN heart. No RNA-seq reads were observed in the BN heart in the extended region that was specific to SHR, supporting the findings from CAGE data  in F1 crosses with most reads overlapping the SHR specific region showed the SHR allele at the variant position (Insr in the heart. The HXB/BXH panel of recombinant inbred (RI) rat strains, generated by crossing SHR and BN rat strains, have been widely used for genetic mapping (A genomic variant (g.1816552:A>G) was located in the SHR specific region of position , suggest mapping . Recentl mapping . Using t mapping .P = 0.041), systolic blood pressure , and diastolic blood pressure  in RI strains. The RI strains containing the SHR allele  showed significantly elevated levels of insulin, systolic and diastolic blood pressure .Next, we investigated several the cardio-metabolic phenotypes measured in RI strains . We founpressure . InteresInsr promoter, whereas respective control strains Fawn Hooded Low blood pressure, Sabra Normotensive, and Wistar Kyoto contain BN allele  gene in SHR might be associated with the disease phenotypes observed in the SHR rat strain. However, strong experimental support is required to establish a causal relationship between differential Insr TSS usage in SHR and the disease phenotype.Taken together, multiple lines of genetic evidence suggest that the switch in TSS usage for the insulin receptor  has been widely used to precisely map TSSs in humans and mouse . Using CMef2c). In this study, we could identify thousands of novel TSS/transcripts even though we performed CAGE tag sequencing from only one rat tissue. This suggests that CAGE tag sequencing from a wide range of rat tissues could significantly improve rat TSS/transcript annotations. In addition, it will help to understand tissue-specific usage of TSS in rats and it would facilitate comparative analysis with human and mouse CAGE data.Rat is a widely used animal model to study various disease phenotypes specifically cardio-metabolic phenotypes . HoweverRat heart CAGE data have been generated from the whole LV. The heart is a complex organ with various cell types . In abseTo understand the role of alternative promoter usage in disease, we used CAGE tag data from SHR and BN rat strains. We identified two types of TSS switching events between SHR and BN. First, where genes use completely different promoters between two rat strains that are located more than 100 bp away from each other and affect the coding region of the gene. This even leads to a shorter protein sequence because of truncated N terminus in one strain compared with the other. Both the transcripts show expression in both strains; however, one transcript tends to be predominantly expressed in one strain, whereas the other transcript shows predominant expression in the other strain. We identified 28 genes with alternative promoter usage between SHR and BN, a proportion of them were known to be associated with cardiovascular disorders. This suggests that alternative promoter usage could potentially result in a complex disease phenotype; however, extensive experimental validations are required to confirm these findings.In the second type of TSS switching event, transcripts use different TSS within the same promoter. In most cases, the TSS switch happened within 100 bp of each other. The TSS switching event does not affect protein coding regions of the gene, but they alter the length of 5′ UTRs. 5′ UTR plays a major role in translational efficiency as 5′ UTR is critical for ribosome recruitment to mRNA and choice of start codon . The 5′ Insr) gene between SHR and BN rat strain resulting in a longer transcript with extended 5′ UTR in SHR compared with BN. The genomic variant located in SHR specific region showed a significant allelic imbalance in F1s, confirming the TSS switch. Furthermore, Insr TSS usage was strongly associated with the insulin levels, systolic and diastolic blood pressure in the HXB/BXH panel of recombinant inbred (RI) strains derived by crossing SHR and BN rat strains. The SHR rat strain shows various phenotypes such as hypertension, insulin resistance, dyslipidemia, and central obesity, collectively known as metabolic syndrome (INSR) lead to severe insulin resistance in humans  were maintained in an animal facility at the Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic. All the experimental procedures were carried out as per European Union National Guidelines and the animal protection laws of the Czech Republic and were approved by the ethics committee of the Institute of Physiology, Czech Academy of Sciences, Prague.LVs from 6-wk-old SHR  and BN  were harvested at the Institute of Physiology, Czech Academy of Sciences, Prague. Reciprocal F1 crosses were generated by crossing SHR females with BN males (SHRxBN) and BN females with SHR males (BNxSHR). LVs from si6-wk-old F1s were harvested. We used a total of 12 F1s with six  F1s from each reciprocal cross.The LVs harvested from rat strains were sent to DNAform, Japan. The RNA extraction and non-tagging, non-amplification (nAnT)-CAGE tag sequencing was performed at DNAform, Japan following a previously described protocol .The CAGE tags were sequenced using 100-bp paired-end sequencing technology on the Illumina HiSeq2500 platform. The adaptors were removed using cutadapt . The addAll the replicates from parental strains (SHR and BN) were used to identify CAGE-defined tag clusters (TCs). Only uniquely mapped reads, reads mapped with quality score 255 extracted using samtools-1.2 , were usThe gene regions were defined as a genomic region between the start and end position of the longest transcript of a gene plus 1,000 bp upstream of the start position. All the CAGE-defined promoters located within the gene regions on the same strand were assigned to the gene. The promoters were then ranked based on the expression levels (TPM) in descending order. The promoter that showed the highest expression was called P1 and subsequent promoters were called P2, P3, and so on for each gene. All the tag clusters that were located in intergenic regions of the genome were considered independent promoters with a P1 rank.https://genome.ucsc.edu/). Similarly, orthologous regions in mouse (mm9) were identified using LiftOver tools and corresponding chain files. The human and mouse orthologous regions of rat heart CAGE tag clusters were then overlapped with the human and mouse GACE tag clusters from the FANTOM consortium using bedtools . The rat heart CAGE tag clusters extended by 200 bp on both sides were overlapped with the list of CpG islands using bedtools intersectBed (A list of rat (rn6 assembly) CpG islands was downloaded from the UCSC genome browser  between SHR and BN were extracted. All the promoters that showed at least 20% expression compared with the aggregate expression levels of all the promoters of the same gene were selected for the downstream analysis. The genes where two or more promoters showed differential expression in opposite direction, that is, of the two promoters of the same gene, one promoter showing up-regulation in SHR compared with BN, whereas another showing down-regulation in SHR as compared with BN, were selected. A gene was called to use alternate promoters only when two promoters that showed expression difference in opposite direction included P1 promoter.To identify genes with alternate promoter usage between SHR and BN, first differentially expressed tag clusters were identified using DEseq2-1.32.0 . The proD must be greater than 0.3. (ii) The test statistics D must be greater than the critical value at α 0.05. The genomic position where the highest D score was achieved was selected as the position of the switch.To identify switching promoters, for each TC (promoter) the CTSS data (count of unique 5′ ends of CAGE tags within TC at base-pair resolution) from six replicates of each parental strain (SHR and BN) was aggregated. The Kolmogorov–Smirnov test was used to identify switching promoters. Following filtering criteria were used to select statistically significant promoter switching events between SHR and BN. (i) The test statistics The publicly available RNA-seq data from SHR and BN LV and liver  was downDe novo transcriptome assembly was performed using stringtie-2.1.4  with defPreprint) following the recommended guidelines for variant calling from transcriptome data. Reads that spanned intronic regions are mapped to exons by splitting reads. To avoid calling variants in such regions, split reads spanning intronic regions were pre-processed using GATK SplitNCigarReads. Then variants were identified in each sample independently using GATK HaplotypeCaller. The gVCF files were then merged using GATK CombineGVCFs.Variants in CAGE-derived tag clusters were identified using GATK-4.0.7 PreprintFollowing filtering criteria were used to select variants for allelic imbalance analysis. (i) Variants must be covered with a minimum of 10 reads in all six replicates of both the parental strain. (ii) Variants must be variable between the two parental strains SHR and BN. (iii) In F1, variants must be covered with a minimum of 10 reads in at least nine out of 12 replicates.P-value ≤ 0.05 were called allelically imbalanced variants in F1s.To identify the variants that show an allelic imbalance in F1s, reference (BN) allele and alternate (SHR) allele counts were extracted for each high-quality variant. A binomial test was used to identify variants with an allelic imbalance in F1s. The variants that showed The LV and liver RNA-seq data from RI strains were obtained from the previous publication .Insr transcript region . The RNA-seq read count from the SHR specific Insr transcript region was then normalised to the read counts from the first exon of the Insr gene . The Mann–Whitney test was used to identify a significant association between genotype and normalised read count in SHR specific Insr promoter region.Based on the genotype at g.1816552:A>G recombinant inbred (RI) strains were grouped into two groups, where AA represents RI strains that inherited the BN allele, whereas GG represents RI strains inherited the SHR allele. RNA-seq read count was obtained from the SHR specific The phenotypic measurements such as insulin levels, systolic, and diastolic blood pressure in RI strains were obtained from the previous publications . The Manhttps://maayanlab.cloud/Enrichr/).Gene enrichment analysis was performed using the Enrichr Web browser (https://www.ebi.ac.uk/ena/browser/home) under accession number PRJEB47228. The codes used for the data analysis are available on GitHub (https://github.com/santoshatanur/ratCAGE). The CAGE peaks identified in this study along with the raw and normalised counts are also available on GitHub (https://github.com/santoshatanur/ratCAGE/tree/main/data).The CAGE data generated in this study have been submitted to the European Nucleotide Archive (ENA;


CRABS CLAW (CRC) to study factors contributing to its sophisticated expression pattern. We combine in silico promoter analyses, global TF-DNA interaction screens, and mutant analyses. We find that miRNA action, DNA methylation, and chromatin remodeling do not contribute substantially to CRC regulation. However, 119 TFs, including SEP3, ETT, CAL, FUL, NGA2, and JAG bind to the CRC promoter in yeast. These TFs finetune transcript abundance as homodimers by transcriptional activation. Interestingly, temporal–spatial aspects of expression regulation may be under the control of redundantly acting genes and require higher order complex formation at TF binding sites. Our work shows that endogenous regulation of complex expression pattern requires orchestrated transcription factor action on several conserved promotor sites covering almost 4 kb in length. Our results highlight the utility of comprehensive regulators screens directly linking transcriptional regulators with their targets.Angiosperm flowers are the most complex organs that plants generate, and in their center, the gynoecium forms, assuring sexual reproduction. Gynoecium development requires tight regulation of developmental regulators across time and tissues. How simple on and off regulation of gene expression is achieved in plants was described previously, but molecular mechanisms generating complex expression patterns remain unclear. We use the gynoecium developmental regulator  Transcription is a universal process in which DNA is transcribed into mRNA that is exported from the nucleus and translated into protein sequence. Already, prokaryotes tightly regulate transcription for the proper timing of cellular development and metabolic processes. However, the prokaryotic way to control expression is different to eukaryotes, as co-functional genes are often grouped in co-regulated polycistronic operons (reviewed in ). In eukFLOWERING LOCUS C (FLC) locus upon vernalization )t manner  and intet manner . Within ets from , co-expr tissues  but CRC does not . The samlate CRC  and woulpression A. This sCRC expression, the Y1H approach identifies only transcription factors acting as monomers or homodimers. To our knowledge, there is no published information on the ratio of TFs forming homo- vs. hetero-dimers in plants. However, a compilation by [While we tried to obtain a comprehensive picture of experimentally verified, direct regulators of ation by  shows thation by  (Figure Our data further show that transcription-activating TFs, such as ETT, bind up to 3 kb upstream of the transcription start site (In summary we can state that the comprehensive analysis of factors regulating complex transcription factors is, at the current state of wet lab and in silico methods, challenging. Directed manipulation of developmental regulators’ expression patterns for yield improvement is thus difficult to achieve and requires extensive research.


Human postmortem specimens are extremely valuable resources for investigating translational hypotheses. Tissue repositories collect clinically assessed specimens from people with and without HIV, including age, viral load, treatments, substance use patterns and cognitive functions. One challenge is the limited number of specimens suitable for transcriptional studies, mainly due to poor RNA quality resulting from long postmortem intervals. We hypothesized that epigenomic signatures would be more stable than RNA for assessing global changes associated with outcomes of interest. We found that H3K27Ac or RNA Polymerase (Pol) were not consistently detected by Chromatin Immunoprecipitation (ChIP), while the enhancer H3K4me3 histone modification was abundant and stable up to the 72 h postmortem. We tested our ability to use H3K4me3 in human prefrontal cortex from HIV+ individuals meeting criteria for methamphetamine use disorder or not (Meth +/−) which exhibited poor RNA quality and were not suitable for transcriptional profiling. Systems strategies that are typically used in transcriptional metadata were applied to H3K4me3 peaks revealing consistent genomic activity differences in regions where addiction and neuronal synapses pathway genes are represented, including genes of the dopaminergic system, as well as inflammatory pathways. The resulting comparisons mirrored previously observed effects of Meth on suppressing gene expression and provided insights on neurological processes affected by Meth. The results suggested that H3K4me3 detection in chromatin may reflect transcriptional patterns, thus providing opportunities for analysis of larger numbers of specimens from cases with substance use and neurological deficits. In conclusion, the detection of H3K4me3 in isolated chromatin can be an alternative to transcriptome strategies to increase the power of association using specimens with long postmortem intervals and low RNA quality. Postmortem human specimens represent an extremely valuable resource with direct translational implications. Tissue repositories collect a great number of brain specimens from HIV-positive (+) and negative individuals, with different viral loads, treatments and ages. Most importantly, specimens may also be from subjects with different substance use disorder patterns and cognitive functions, generating opportunities for identifying important molecular elements tied to recorded clinical and individual variables. Currently, the analysis of the transcriptome can provide in-depth information about pathways, biological processes and even cellular compartments disturbed by variables such as drug abuse. Nevertheless, the use of a large fraction of the archived specimens may be limited for determining global transcriptional changes due to problems with RNA quality. Postmortem time interval prior to tissue harvest is one of the main causes of poor RNA quality and presents a challenge to retrieve transcriptional information from a large number of human specimens, particularly the ones from drug users ,2,3. To Epigenetic marks associated with transcription include enhancer histone modifications such as H3K4me3 and H3K27Ac in proximal promoters, as well as the presence of RNA Polymerase I/II at transcription starting sites (TSS) . These aWe found that both histone modifications were better preserved than RNA Polymerase and that H3K4me3 was more abundant than H3K27Ac. H3K4me3 was consistently found in postmortem human specimens and allowed the identification of promoter and gene activity in inflammatory and viral response genes, as well as in genes involved in addiction. We conclude that the characterization of the epigenetic landscape can enable the retrieval of useful information from precious specimens with otherwise poor RNA quality, offering an opportunity to expand the analysis of specimens in tissue repositories, such as the National NeuroAIDS Tissue Consortium (NNTC). Importantly, this may help increase the number of analyzed specimens, enhancing sample size and statistical power, while improving models that incorporate common confounders and heterogeneities of the human population to genetic and post-translational patterns.The studies were performed with the approval of the Institutional Animal Care and Use Committee (IACUC) of the San Diego Biomedical Research Institute (SDBRI). Eighteen 10 weeks old male C57Bl/6 mice were sacrificed using 5% isoflurane, and the bodies were maintained at room temperature for 0, 6, 24, 48, 72 and 96 h prior to dissection and brain harvesting. The brains were divided into two hemispheres, designated for RNA isolation and quality assessment performed as a service by the Microarray Core Facility at The Scripps Research Institute, La Jolla, as well as for chromatin isolation and cross linking. Mouse ChIP-qPCR was performed in isolated chromatin for the detection of H3K4me3, H3K27Ac and RNA Pol II with ChIP-grade antibodies. The reactions were performed in triplicate using 25 μg of mouse brain tissue chromatin and 3 μL of H3K4me3 antibody , 4 μg of H3K27Ac antibody  or 4 μg of RNA Pol II antibody . We performed qPCR using two positive control primers that worked well in similar assays , as well as a negative control primer pair that amplifies a region in a gene desert on chromosome 6 (Untr6).2 were separated from each frozen tissue specimen. Half of each fragment was used for RNA extraction and RNA quality assessment performed as a service by the Microarray Core Facility at The Scripps Research Institute, La Jolla. The remaining half was used for simultaneous chromatin isolation and cross-linking. ChIP reactions were performed using 30 μg of human HIV+ brain tissue chromatin and 4 μg of antibody against RNA Pol II, , H3K27Ac  and H3K4me3 . Following that, qPCR was performed using two positive control primers (ACTB and GAPDH), as well as a negative control primer pairs that amplify regions in a gene desert on chromosome 12 (Untr12) for human specimens.Frozen human prefrontal cortex brain specimens were provided by the National NeuroAIDS Tissue Consortium (NNTC) upon request and kept at −80 °C until experimentation. All experiments were performed with Institutional IBC and IRB approval (IRB-18-001-MCM). The prefrontal cortex specimens were selected among all male HIV+ cases . Differences were considered statistically significant at Mouse and human tissue were homogenized with Kimble Kontes disposable pestles , and RNA was extracted using RNAeasy Mini kit . RNA quantification was determined by spectrophotometry using a NanoDrop ND-1000 spectrophotometer . The OD260/280 ratio was used to evaluate the purity of the nucleic acid samples, and the quality of the extracted total RNA was determined with the Agilent 2100 Bioanalyzer , at the La Jolla Scripps Research DNA Array Core as a service.g for 5 min to pellet the nuclei. The pellet was resuspended in nuclear lysis buffer  at a ratio 1:1 (v/w), incubated on ice for 10 min and stored at −80 °C until use. The pellets were sonicated, and DNA was sheared to an average length of 300–500 bp. The chromatin extraction was performed using Chromatin IP DNA Purification kit . Genomic DNA (input) was prepared by treating aliquots of chromatin with RNase, proteinase K and heat (65 °C) to reverse cross-linking, followed by phenol and chloroform extractions and ethanol precipitation. Pellets were resuspended in 10 mM Tris, 1 mM EDTA, and the resulting DNA was quantified on a Nanodrop spectrophotometer . Results were used to calculate the total yield.Minced tissue was washed twice in ice cold PBS and treated with 1% formaldehyde (Sigma-Aldrich) for 12 min to crosslink the chromatin, as previously described ,15. The H. sapiens genome annotation (Hg38 assembly) and unique aligns (removed duplicates) were obtained. A signal map showing fragment densities along the genome was visualized in the Integrated Genome Browser (IGB) and MACS peak finding was used to identify peaks. Control data was derived from positive and negative control alignments. The 75-nt sequence reads generated by Illumina sequencing (using NextSeq 500) were mapped to the genome using the BWA algorithm with default settings. Alignment information for each read was stored in the BAM format. Only reads that passed Illumina’s purity filter, aligned with no more than 2 mismatches and mapped uniquely to the genome were used in the subsequent analysis. In addition, duplicate reads were removed. Since the 5′-ends of the aligned reads represent the end of ChIP/IP-fragments, the tags were extended in silico using Active Motif software at their 3’-ends to a length of 150–250 bp, depending on the average fragment length in the selected 200 bp library size. To identify the density of fragments along the genome, the genome was divided into 32-nt bins and the number of fragments in each bin was determined. This information in bigWig was visualized in UCSC genome browser. Intervals with tag enrichment were identified by chromosome number, start and end coordinates using MACS [ChIP was performed using the ChIP-IT High Sensitivity Kit (Active Motif). ChIP-seq reactions were carried out with 30 ug of chromatin and anti-H3K4me3 antibody. The ChIP DNA was processed into an Illumina ChIP-Seq library and sequenced to generate reads, which were aligned to the ing MACS  and SICEing MACS , correspwww.bioconductor.org, 2020) [Genes with H3K4me3 peaks found within 10 kb of start/end were compared in the ChIP samples. Overlap between the samples was high (70–90% in pairwise comparisons). Active region identification settings were −7500 to 2500 from promoter start, with 10,000 upstream and downstream-margin. The comparison of all samples averaged by group and the peak metrics obtained in Bioconductor ChiPpeakAnno   were usep < 0.05 in pairwise comparisons performed in licensed JMP Pro 12 software . Enrichment, processes and pathway annotations were performed using z scores by licensed Ingenuity Pathway Analysis software , confirmed by DAVID Bioinformatics Resources 6.8  and then visualized using local search features in GeneMANIA plugin [www.genemania.org) in Cytoscape 3.7.0 platform [www.cytoscape.org, 2020) with Homo sapiens sources from Reactome [www.reactome.org, 2020) and BioGRID_ORGANISM [https://thebiogrid.org, 2020). Transcription factor-DNA interactions derived from merged peak regions were modeled using licensed TRANSFAC v2021.1 [H. sapiens nervous system.Ratio and log 2-fold-change were calculated using tag densities/signal metrics relative to known gene annotations to group average signal values, with a cut off > and <1.5-fold, and A plugin ,20,21  targeting different segments within the gag-pol region, as described [Paraffin-embedded sections of prefrontal cortex (from the same subjects) were provided by the NNTC upon request. RNAScope 2.5 HD assay  was performed. Briefly, pre-treatment was performed with RNAscope 2.5 HD Detection Kit (RED) Cat# 322360), RNAscope 2.5 Pretreat Reagents-H202 and Protease Plus Cat#322330), RNAscope Target Retrieval (Cat#322000) and RNAscope Wash Buffer Cat#310091), following manual instructions. Probe 0091, fol2360, RNA0, RNAscoPeaks of enrichment in ChIP-seq data may suggest stable and predictive standards associated with transcription, especially the ones in promoter regions . We selex-axis designate the 18 s and 28 s ribosomal RNA, respectively. The y-axis indicates fragment units (FU) derived from band density in relation to standards. Notice that fragment units decrease by about 50% at 24 h compared to 0 and 6 h, although 18s and 28s peaks in histograms may retain aspects of integrity. At 0–6 h, RIN was 8.8 ± 0.5. From 24 h on, RIN was consistently <7.2, reaching 2.5 ± 0.3 at 96 h. RIN < 7.5 is considered unfit for RNA studies, as it signifies a significant fragmentation, indicating poor quality that results in inaccurate transcriptional data.The stability of H3K27Ac, H3K4me3 and RNA Pol was tested in mouse brains, at different postmortem intervals, from 0 to 96 h time points. For that, C57Bl/6 mice were sacrificed and left at room temperature in groups of 3, for 6 h, 24, 48, 72 and 96 h before dissection and brain harvesting. We used these mouse brain specimens to test the interaction between functional genomic marks in mouse brains and time in different postmortem intervals. For that, we used ChIP-qPCR to test H3K4me3, H3K27Ac and RNA Pol II onto promoters of two primer-amplified positive control genomic regions, corresponding to the housekeeping genes beta actin (ACTB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), which are permanent and constitutively transcribed with a fixed number of occupancy units across samples, as well as a negative control primer pair that amplifies a region in a gene desert on chromosome 6 (Untr6), as recommended by standard quality control protocols . Figure We hypothesized that H3K4me3 would be consistently detected in human specimens with poor RNA quality not considered adequate for transcriptional studies. We tested the same marks, H3K4me3, H3K27Ac and RNA Pol, in human postmortem prefrontal cortex specimens, which had a poor RNA quality (RIN < 6.2) and were not considered adequate for transcriptional studies. The specimens were obtained from the NNTC and were from ART-treated male subjects from the UCLA cohort between 2003 and 2011, following inclusion criteria described in methods. Detection of viral RNA in matching paraffin embedded tissue sections by RNAscope was detectable, although not consistently in all the specimens . Viral DGiven the relative stability of H3K4me3 in mouse and human specimens, this genomic unit was then selected for comparing two groups of HIV+ brain specimens represented by 6 cases with HIV-associated neurological diagnosis, divided in 2 subgroups, being 3 cases with no history of substance use and 3 cases that fit Meth-use criteria, based on self-reported recent and life-time use. The results in The quality of genome-wide H3K4me3 signal across samples was tested using different methods . Active y-axis) are used for visualization of read densities or H3K4me3 tag distributions (using bigWIG metrics), which are mapped across target regions. Peaks mapped to gene bodies located between −2 kb and +2 kb, excluding distal downstream and upstream peak signals  and susceptibility to obesity (p = 0.006). Overrepresented functional annotations are within phosphoproteins (p = 5.3 × 10−103), alternative splicing (p = 5.8 × 10−31) and ubiquitin conjugation (p = 4.7 × 10−28). Biological processes (GOTERM) include positive regulation of GTPase activity (p = 6.8 × 10−10), Regulation of transcription (p = 3.2 × 10−9), Wnt signaling pathway (p = 1.8 × 10−8), Cell migration (p = 2.4 × 10−7), MAPK cascade (p = 1.7 × 10−7), neuron projection development (p = 3.0 × 10−6), positive regulation of apoptosis (p = 4.3 × 10−6), oxidative response (p = 6.8 × 10−5) and glial cell differentiation (p =6.6 × 10−4). Pathway annotations (KEGG) for genes with proximal promoter epigenetic activity are Pathways in cancer (p = 2.5 × 10−16), several pathways involved in neurotransmitter signaling and neurological pathogenesis, such as cholinergic synapse (p = 1.5 × 10−7), oxytocin signaling (p = 6.8 × 10−7), glutamatergic synapse (p = 9.4 × 10−7), dopaminergic synapse (p = 3.9 × 10−6), cAMP signaling (p = 5.6 × 10−5), amphetamine addiction (p = 1.2 × 10−5), serotoninergic synapse (p = 9.4 × 10−4), morphine addiction (2.7 × 10−3) and endocannabinoid signaling (p = 2.5 × 10−3). Inflammatory and chemokine pathways were also identified (p = 1.4 × 10−5). This indicates that epigenetic strategies like this can distinguish disruptions in neurotransmitter signaling.The application of a systems biology approach via Genemania and IPA has suggested that this strategy is able to provide important information on functional processes and pathway usage, while mapping effects of infection and co-morbidities on functional units of the genome. Overall, we found 6062 active intervals located upstream the gene sequence, of which 2210 were within proximal promoter regions (−1 kb–0) F, C2. OfWe have identified 2210 genes with active proximal promoter, with both increased (orange shades) and decreased (blue shades) promoter activity associated with Meth use and white nodes corresponding to no change . In Figup < 0.0001). Other IFN pathway components such as IRF1  or transcriptional regulators JUN  and SOX2  were also identified as key components that showed significant changes in the context of Meth  identified among genes with proximal promoter activity and significant ><1.5-fold changes was characterized by dopaminergic synapses signatures C, also iThe unbiased analysis of all genes with HeK4me3 active regions E, regardClusters annotated to synaptic pathways have also been able to distinguish HIV+Meth+ from HIV+Meth-, using the same approach including all merged HeK4me3 active regions E. For inp = 2.29 × 10−26). Cell type attributes characterized by the over-representation patterns included neurons (p = 1.86 × 10−12), followed by T cells (p = 3.93 × 10−8), while innate immune cells (macrophages/microglia) were significantly under-represented (p = 3.77 × 10−6), suggesting potential dysfunction in that compartment regardless of drug use patterns.Interestingly, like with addiction pathways, neurological synapses genes showed a lower number of H3K4me3 active peak regions in the prefrontal cortex from HIV+Meth users compared to non-Meth users, suggesting differences in enhancer chromatin remodeling events, while epigenomic alterations such as the one we are measuring have the capacity to affect the behavior of the identified gene networks. However, epigenomic factors do it so in cooperation with transcription factors and the respective binding motifs made available by such modifications. ChIP data input in BED format into TRANSFAC generated a functional analysis report where prefrontal cortex was the most significantly over-represented attribute . Postmortem specimens from humans are a valuable resource for understanding molecular dynamics taking place in a diversity of conditions. Even more critical is the fact that large association studies benefit from the integration with transcriptional profiles. However, RNA quality has been a limitation, encountered by our group and others ,36,37. IEpigenetic modifications, enhancers and regulators of transcriptional activity are likely protected by protein-based mechanisms that prevent physical damage to the genome and evolutionarily tailored to maintain the stability of gene expression patterns . We testIn our studies in mice with controlled postmortem intervals, we found that H3K4me3 was more abundant than other active chromatin modifications, followed by H3K27Ac, and by RNAPol binding. Although this may reflect a difference of stability  developeA higher activity based on the number of H3K4me3 active sites was found in chromosome 1, which is the largest human chromosome and reported involvement in heritable brain disorders . SimilarThe analysis of H3K4me3 peak distribution was used predict signatures of Meth use disorder in the context of HIV. H3K4me3 promoter and gene activities are associated with transcriptional events in several systems ,52,53. SThe majority of the promoter and gene activity evidenced by H3K4me3 peaks, was suppressed in HIV+ Meth users, compared to HIV+ non-Meth users. These results also parallel previous transcriptional findings in a mouse model of HIV and Meth abuse, where Meth caused a significant suppression of gene transcription ,62, partThe gene networks associated with individual synaptic functions exhibited a common behavior, characterized by an overall decrease in genomic activity in Meth users in the context of HIV. This could result from mechanisms that favor and enhance the inflammatory process, which were identified here, leading to neuronal loss, including in the dopaminergic system, a hallmark of Meth and other substance use disorders ,69. ImpoIn addition to the prediction of gene network behaviors, patterns of H3K4me4 peak activity can be used to predict transcription factor usage patterns, based on the occurrence of binding motif sequences within and near active intervals. We have performed this analysis and found that Sp1 was the most frequent binding motif. It has been shown that Sp1 makes a complex with NFkB to enhance HIV replication in vitro, in a process that is facilitated by Meth . The secThis study has limitations, including small sample size and the lack of HIV-negative specimens. Our epigenetic findings can inform processes occurring in the human brain but must be integrated with transcriptome data in specimens and models with higher RNA quality stability for validation. Nevertheless, since a large fraction of the H3K4me3 genomic activity occurs in proximal promoters followed by introns, it is likely to result into and correspond to transcriptional events.We demonstrate that important insights can be gained from epigenetic approaches and opens to the opportunity of investigations with larger sample sizes. We have shown the potential of using enhancer epigenetic marks for making predictions on the behavior of gene networks and for recovering important information from a significant number of specimens with limited use due to postmortem or storage intervals.


Schizosaccharomyces pombe (S. pombe) and powerful parallel sequencing technologies have provided many insights in this area. This review will give an overview of key findings in S. pombe that relate long RNAs to multiple levels of chromatin regulation: histone modifications, gene neighborhood regulation in cis and higher-order chromosomal ordering. Moreover, we discuss parallels recently found in mammals to help bridge the knowledge gap between the study systems.As part of a complex network of genome control, long regulatory RNAs exert significant influences on chromatin dynamics. Understanding how this occurs could illuminate new avenues for disease treatment and lead to new hypotheses that would advance gene regulatory research. Recent studies using the model fission yeast  Schizosaccharomyces pombe  and human genomes are transcription-competent [From yeast to mammals, eukaryotic genomes can be pervasively transcribed depending on developmental or environmental states. It is estimated that most of the ompetent ,2. AlthoThe transcriptional products of noncoding genes can be broadly classified as small noncoding or long noncoding RNAs (lncRNAs). Small noncoding RNAs are less than 200 nucleotides in length and are comprised of major subclasses, including microRNAs (miRNAs), short interfering RNAs (siRNAs), tRNA-derived small RNAs (tsRNAs) and piwi-interacting RNAs (piRNAs). Their roles in transcriptome and chromatin regulation have been extensively reviewed elsewhere and will not be the focus of this review ,5,6,7,8.bona fide lncRNAs or bifRNAs.Nevertheless, it has been debated that some annotated lncRNAs might be misannotated and can be translated ,14,15. TS. pombe to become an outstanding model for understanding the regulatory functions of nuclear long RNAs. Excitingly, studies in mammalian mouse and human models are also beginning to reveal similar mechanisms of long RNA action at the chromatin level. Here, we review various chromatin-level modes of long RNA-mediated gene regulation in S. pombe and mammals, with special attention to their similarities. In particular, we will focus on the mechanisms by which long RNAs orchestrate control over epigenetic modifications, transcription termination and higher-order chromatin architectures. Altogether, this review will summarize our latest knowledge on how fission yeast and mammals leverage long RNAs to elicit conserved mechanisms of genome control.In recent years, the combination of facile genetics, molecular biology and biochemistry with high-throughput sequencing approaches has enabled the fission yeast Next-generation sequencing  and third-generation sequencing  capabilities have dramatically enabled us to discover long regulatory RNA species. Using strand-specific RNA next-generation sequencing (NGS) to massively produce short sequencing reads is currently the most prominent approach to empirically detect entire RNA repertoires within cells. The short reads are then bioinformatically mapped onto reference transcriptomes, using various open-sourced short-read mapping software  to inferPost-identification, a range of tools are available to assess the regulatory potential of lncRNA transcripts. Aside from a range of computational tools to predict regulatory functions of lncRNAs ,27,28, tLong bifRNAs with dual coding and noncoding functions are more complicated to work with because they require dissecting functions of the RNA from the protein that they encode . GeneticS. pombe through many studies on the Mmi1 RNA-binding protein, as will be our focus below. We will also highlight striking similarities that have only recently been realized between the regulatory functions of Mmi1-targeted transcripts and RNA-targets of YTH Domain Containing 1 (YTHDC1), which is the mammalian homolog of yeast Mmi1.It is often that long RNAs, such as lncRNAs and others, may function by regulating transcript-bound protein partners’ activities . This caS. pombe Mmi1, is essential for directly binding to RNA transcripts [meiRNA [nam [mamRNA [Mmi1 belongs to the family of conserved YT521-B homology (YTH) domain proteins. Its YTH protein domain, located at the C-terminus of nscripts ,55,56. Inscripts . Mmi1 dinscripts . More re [meiRNA , nam [60 [mamRNA . The abi [mamRNA . While t [mamRNA , wherebyA combination of genetic analyses and protein-centric studies has revealed that transcripts bound by Mmi1 are associated with many additional protein factors. These include the Clr4 methyltransferase that is responsible for the post-translational modification of chromatin with di-methylation of histone H3 lysine-9 (H3K9me2), RNA 3′-end processing and transcription termination , RNA 5′ binding and splicing  and RNA degradation , Ccr4-NOT complex, Rrp6) ,69,70,71N6-methyladenosine (m6A) post-translational modification of long RNAs [MALAT1), HOX transcript antisense RNA (HOTAIR) and X-inactive specific transcript (XIST). MALAT1 associates with actively transcribed chromatin regions and positions them near nuclear speckles [6A-modified MALAT1 and YTHDC1 are needed for the chromatin-speckles association and gene activation [HOTAIR interacts with YTHDC1, including at the m6A modification of HOTAIR residue A783 [XIST has many m6A-modifiable sites, with at least 78 residues identified by m6A-mapping using m6A iCLIP [XIST RNA can promote the compartmentalization of specific ribonucleoprotein complexes at chromatin to mediate its silencing functions [S. pombe Mmi1 and mammalian YTHDC1, which bind to DSR motifs and m6A, respectively [In mammals, the Mmi1-homolog YTHDC1 directly binds to the ong RNAs . Some ofspeckles ,74. Thesspeckles . A recendue A783  to local6A iCLIP , and int6A iCLIP ,78,79,80unctions . Althougectively ,55, thesHistones within chromatin are subject to post-translational modifications, including methylation, acetylation, ubiquitination, citrullination and phosphorylation. These modifications can act as molecular platforms to recruit gene regulatory factors, such as histone H3 lysine-9 tri-methylation (H3K9me3) recruiting Heterochromatin Protein 1 (HP1) family proteins ,84,85,86S. pombe, small and long regulatory RNAs have been linked to targeted histone methylations, particularly H3K9me2 and H3K9me3. In this yeast and across higher eukaryotes, these modifications are the hallmarks of chromatin domains called heterochromatin [trans-acting factors. The two broad types of heterochromatin that exist are called constitutive and facultative. In S. pombe, constitutive heterochromatin is marked by the presence of H3K9me2/3 and is predominately nucleated by small interfering RNAs (siRNAs) that derive from repetitive transcripts [In hromatin ,87,89,90hromatin  that sernscripts . Conversnscripts  and RNA-nscripts ,94,95,96nam) was identified [nam transcripts called nam5/6/7 were found to be produced from pericentromeric dh repeats. Mmi1, the nuclear exosome subunit Rrp6 and the CPF subunit Swd22 target these RNAs to promote H3K9me2 at pericentromeres [nam5/6/7 genes [S. pombe is often needed to mediate transcription termination [nam5/6/7 RNA [Recent works in fission yeast have provided mechanistic insights into how long RNAs promote H3K9me2 modifications at constitutive and facultative heterochromatin. At pericentromeric constitutive heterochromatin, the existence of distinct regulatory lncRNAs called noncoding RNA associated with Mmi1  have recently been suggested to regulate transcription of nearby downstream rDNA genes [S. pombe, three example lncRNAs that have been studied in detail are nam1, rse1 and prt [byr2, ste11 and pho1, respectively. Studies on the functions of nam1 and rse1 lncRNAs were recently reviewed by Andric and Rougemaille [prt lncRNA represses the downstream pho1 coding gene.Virtually all organisms need to address how to coordinate the expression of closely-spaced and sometimes overlapping genes . In thesectively , lncRNAsNA genes . In S. p and prt ,63,113, gemaille  and willpho1 promoter [Pho1 is considered a model nutrient-sensing gene because its expression is inversely correlated with phosphate abundance in the cellular environment [pho1 expression controlled in response to available phosphate? From the perspective of cellular phosphates, phosphorylation of RNA polymerase II C-terminal domain (CTD) and phosphate-rich inositol pyrophosphates have been shown to affect pho1 expression [prt lncRNA gene, which is situated immediately upstream of the pho1 coding gene and is transcribed in the same direction as pho1. The prt promoter is within 110 bp upstream of the prt transcribed start site [prt transcription termination region is located proximal to the promoter of the pho1 gene [prt noncoding gene negatively regulates pho1 expression [prt lncRNA gene represses expression of the downstream pho1 gene. Pho1 is a phosphate-responsive secreted acid phosphatase that is involved in phosphate uptake. Its gene expression requires the transcription factor Pho7, which directly binds to the upstream promoter ,117,118.ironment ,69,119. pression ,122,123.art site . The prtho1 gene ,69,116. pression ,125, recprt gene transcription promotes pho1 repression. Ablation of the prt promoter or the prt transcribed region completely prevents prt transcription and elevates pho1 expression [prt transcription and increase pho1 expression [pho1 [pho1 [pho1 by promoting early CPF-mediated transcription termination of the upstream prt gene [prt by CPF and Dhp1 [pho1 gene [prt appear important for the nearby regulation of pho1 gene expression in cis.Evidence indicates that pression ,69,124. pression . Moreoveon [pho1 ,127,128.o1 [pho1 . Subsequand Dhp1 ,69 is liho1 gene ,117. Altprt lncRNA has a direct function in regulating its own expression and that of the nearby pho1 gene. The prt transcript has DSR motifs and is bound by Mmi1 protein. Deleting either the DSR motifs or the mmi1 gene leads to prt transcription that continues unabated throughout the pho1 gene body [prt transcript to control downstream pho1 expression because studies using electrophoretic mobility shift assays have confirmed that Mmi1 can only bind prt RNA and not prt ssDNA or dsDNA [prt gene locus in chromatin immunoprecipitation assays [prt lncRNA directly recruits Mmi1 to the vicinity of the underlying chromatin locus. There, Mmi1 recruits CPF to promote early transcription termination of prt to prevent transcriptional readthrough into the pho1 locus, thereby allowing proper pho1 mRNA expression [prt termination [prt lncRNA and the transcriptional process of making the lncRNA both make distinct contributions for modulating the nearby pho1 gene  complex. Hence, the prt lncRNA contains multiple regulatory features. It remains to be seen precisely how prt DSR and cryptic intron signals are coordinated to measure pho1 expression.In addition to the Mmi1-binding DSR motifs, deep RNA sequencing recently revealed that the c intron . The intXIST) have been characterized to regulate the transcription of gene neighborhoods [CHD2 adjacent suppressive regulatory RNA (CHASERR) lncRNA appears most similar to yeast prt. It is one of the most conserved lncRNAs across vertebrates and is situated in tandem upstream of the nearby CHD2 gene, which encodes for a DNA helicase [prt-pho1 relationship in fission yeast, Rom et al. found that disruption of CHASERR led to derepression of the downstream CHD2 gene [CHASERR-mediated repression of CHD2 likely occurs in cis because exogenous expression of CHASERR in trans was unable to repress CHD2 expression. Still, artificial activation of the lncRNA at its native chromosomal location could repress CHD2. Interestingly, targeting CHASERR using antisense oligonucleotides also led to elevated CDH2 expression, suggesting that CHASERR lncRNA itself can repress its nearby gene  and were enriched for known functional roles in RNA polyadenylation or transcription termination  is a prime model for understanding how lncRNAs similarly regulate chromatin. Within female cells that carry two X chromosomes, one is stably inactivated so that X-linked gene dosage is equivalent to XY male cells, where the sole X chromosome is active [XIST lncRNA that is transcribed from Xi is critical for inactivation by first nucleating repression at the XIST locus and then spreading along the Xi to expand the repression zone [XIST, as reviewed elsewhere [XIST recruits PRC2 to deposit repressive H3K27me3 marks throughout Xi [XIST lncRNA also repels binding of chromosomal remodeling proteins, such as cohesin, to enforce Xi-specific chromosomal structures [XIST, a more recent study from Fang et al. identified the FIRRE lncRNA, transcribed from the active X chromosome (Xa), as a long-range regulator of the homologous Xi [XIST-coating of the Xi, FIRRE does promote repressive H3K27me3 and localization of the Xi near the nuclear periphery or the nucleolus (Xi, with possible implications on Xi chromosomal structure or localization [FIRRE has also been suggested to promote H3K27me3 at autosomal chromatin regions [FIRRE, within the context of these relationships. In mammals, the inactivated X chromosome (s active ,146. Theion zone ,81,147. lsewhere . XIST reghout Xi ,149. XISructures . In addiogous Xi . While iucleolus b. Furthelization ,150. Mor regions . Given t regions ,151,152,S. pombe has been an important model for understanding how long RNAs shape chromatin landscapes, from the individual locus level to much broader chromosomal regions. The striking conservation of these RNA roles in fission yeast and mammals, representing ~1 billion years of evolution, strongly suggests that long RNAs and their interacting protein partners are crucial mediators between nuclear chromatin and the cellular environment. With major advances in sequencing technologies, the identities and functions of transcriptomes are beginning to emerge in unexpected ways. For example, we now understand that nam lncRNAs in yeast are functional transcripts with significant implications for gene repression. The recent studies highlighted here invite numerous additional questions that could be addressed with currently available technologies. What are the sequenced-based and non-sequenced-based features of long RNAs that confer functional specificity? When do cells employ specific RNA-mediated regulatory mechanisms? With cells often expressing multiple copies of an RNA, how are those copies distributed across the genome for regulatory or non-regulatory purposes?The fission yeast With the introduction of whole-genome sequencing over two decades ago, it was believed that all genome biology would be solved within a matter of years. Today, that sentiment could not be further from the truth. Very likely, higher quality, higher depth sequencing within the context of different genomic, developmental or environmental conditions will illuminate new strategies by which nature regulates their genomes. This will undoubtedly expand our knowledge on the sophisticated mechanisms by which transcriptional RNA products loop back to regulate the underlying DNA templates.


Plasmodium sexual gametocyte stages are the only transmissible form of the malaria parasite and are thus responsible for the continued transmission of the disease. Gametocytes undergo extensive functional and morphological changes from commitment to maturity, directed by an equally extensive control program. However, the processes that drive the differentiation and development of the gametocyte post-commitment, remain largely unexplored. A previous study reported enrichment of H3K36 di- and tri-methylated (H3K36me2&3) histones in early-stage gametocytes. Using chromatin immunoprecipitation followed by high-throughput sequencing, we identify a stage-specific association between these repressive histone modifications and transcriptional reprogramming that define a stage II gametocyte transition point.The ap2-g gene repression in post-commitment gametocytes. Lastly, we associate the enhanced potency of the pan-selective Jumonji inhibitor JIB-04 in gametocytes with the inhibition of histone demethylation including H3K36me2&3 and a disruption of normal transcriptional programs.Here, we show that H3K36me2 and H3K36me3 from stage II gametocytes are associated with repression of genes involved in asexual proliferation and sexual commitment, indicating that H3K36me2&3-mediated repression of such genes is essential to the transition from early gametocyte differentiation to intermediate development. Importantly, we show that the gene encoding the transcription factor AP2-G as commitment master regulator is enriched with H3K36me2&3 and actively repressed in stage II gametocytes, providing the first evidence of P. falciparum early sexual development. The stage II gametocyte-specific abundance of H3K36me2&3 manifests predominantly as an independent regulatory mechanism targeted towards genes that are repressed post-commitment. H3K36me2&3-associated repression of genes is therefore involved in key transcriptional shifts that accompany the transition from early gametocyte differentiation to intermediate development.Taken together, our results provide the first description of an association between global gene expression reprogramming and histone post-translational modifications during The online version contains supplementary material available at 10.1186/s13072-021-00393-9. Plasmodium parasites between human hosts, facilitated by the mosquito vector. In the human host, malaria parasites exist either as the asexual proliferative stages, responsible for the symptoms of malaria, or as sexually differentiated, transmissible gametocytes [Plasmodium falciparum. Mature gametocytes are the only stage that can be transmitted by the mosquito vector and as such, the process of gametocytogenesis is an attractive target for the development of transmission-blocking strategies [Malaria remains a serious threat to public health in most of the developing world and is responsible for millions of deaths annually . Neverthetocytes , 3. The rategies –6.P. falciparum parasite life cycle are driven by global transcriptomic reprogramming that is tightly controlled by complex transcriptional and post-transcriptional regulation [P. falciparum parasites is dynamic with each life cycle stage characterised by a unique pattern of hPTMs, suggested to be foundational in establishing the specialised transcriptional program for the stage [The stage transitions within the gulation –9. Additgulation . The hishe stage . Accordihe stage . For exahe stage –17. By che stage  and whilhe stage –21. H3K3he stage –24. H3K3he stage , 25, 26.ap2-g  that is recruited by enrichment of H3K9me3 at these loci , 28. Sexmmitment ) from H3mmitment –32. Howemmitment , 33. Asimmitment , the funThe dynamic nature of the hPTM landscape during gametocyte development and departure from the patterns observed during asexual proliferation suggest that hPTMs may be involved in stage-specific gene expression in gametocytes as has been demonstrated for asexual parasites , 34, 35.P. falciparum stage II gametocytes [To investigate the functional role of H3K36me2&3 in etocytes , we perfP. falciparum [P. falciparum histone H3 with either H3K36me2 or H3K36me3  peaking at > 0.2 and > 0.17, respectively, contrasting with a depletion of these hPTMs in coding regions (CDRs)  and compared these gene sets to their transcription profiles during gametocytogenesis, using a gene expression time-course dataset previously generated in our lab on the same clonal strain of arasites . Corresparasites , 36.Fig.P = 3.35e−5), haemoglobin metabolism (P = 0.003), cell cycle arrest (P = 0.033) and DNA replication (P = 0.041)  are strongly repressed during stage II gametocytes, with the hPTMs present exclusively upstream of the TSSs in 342 of the 417 repressed genes  and host cell modification processes  [hp1 (PF3D7_1220900), histone deacetylase 2 , gametocyte development protein 1  and the ApiAP2 transcription factor, ap2-g  , 56 and mmitment , 32, 57.kahsp40, PF3D7_0201800 and emp3, PF3D7_0201900) and protein export (e.g. hyp). These may therefore be regulated cooperatively by the two mechanisms. A second AP2 transcription factor, AP2-G2 is essential to gametocyte development beyond stage III and in asexual parasites, AP2-G2 represses a subset of genes through an association with H3K36me3 [Previous studies described other gene regulatory mechanisms that function during gametocytogenesis , 58, incH3K36me3  and CCT H3K36me3 , 59. Thiacs2, PF3D7_0301000: acs4, PF3D7_1372400; acs7 PF3D7_1200700 and cept, PF3D7_0628300) and several epigenetic and transcriptional regulators  jmjc1  family [jmjc1 may be present as means of safeguarding the integrity of H3K36me2&3 enrichment and the associated transcriptional repression in the stage II gametocytes. Taken together, these results implicate H3K36me2&3 as a novel mechanism of transcriptional repression in stage II gametocytes that is largely independent of other mechanisms described to date.However, the majority of the H3K36me2&3-enriched and repressed genes in stage II gametocytes are not associated with any other regulatory mechanisms and is likely regulated by a novel repressive mechanism involving the stage II gametocyte-specific enrichment of H3K36me2&3. This includes lysoPC synthesis-associated genes (e.g. ) family . As suchset2 are available [P. falciparum (jmjc1 (PF3D7_0809900), jmjc2 (PF3D7_0602800) and jmj3 (PF3D7_1122200)) are transcribed in stage II gametocyte development, strongly suggesting that at least one of these enzymes demethylates H3K36 , both with activity against gametocytes [P. falciparum Jumonji KDMs demethylate H3K36me2&3 after they peak in abundance in stage II gametocytes.To investigate the functional relevance of enzymes involved in deposition  and removal (KDMs) of H3K36me2&3, we interrogated inhibition of selected enzymes on H3K36 methylation levels on days associated with stage II gametocyte development. In asexual parasites, the HMT SET2 PF3D7_1322100) methylates H3K36 , 60, but100 methyK36 Fig. a. We useetocytes , 61, 62,etocytes , 64. Thijmjc2 and the histone acetyltransferase-encoding genes, gcn5 (PF3D7_0823300) and myst (PF3D7_1118600) , does indeed attest to the influential role of H3K36me2&3 at this transition point. Additionally, the enrichment of genes that are upregulated under conditions of lysoPC depletion links H3K36me2&3 with the parasite’s earliest responses to the environmental cue for sexual commitment [hp1 and ap2-g [Accumulating evidence demonstrates that epigenetic regulators are essential drivers of the transcriptional reprogramming necessary for cellular differentiation , 84–86. mmitment . Specifind ap2-g  and as snd ap2-g . While wnd ap2-g  as well nd ap2-g , 88, 89.P. falciparum gametocyte differentiation [P. falciparum parasites. Importantly, we identify an exclusive association between H3K36me2&3 enrichment and hp1 and ap2-g  and synchronised using 5% D-sorbitol as previously described [P. falciparum gametocytes were induced as previously described [2CO3, 0.024 mg/ml gentamycin, 25 mM HEPES, 0.2 mM hypoxanthine and 0.5% (w/v) Albumax II) was replaced 48 h after initiation. The haematocrit was reduced to 4% 72 h after initiation (hereafter referred to as day 0) and maintained as such throughout gametocyte development with daily replacement of glucose-enriched (20 mM) media. Residual asexual parasites were removed by supplementing glucose-enriched media with 50 mM N-acetylglucosamine (Sigma-Aldrich) from day 1 of development. Gametocytes were sampled on days 2, 4 and 7 post-induction as the pre-stage II, stage II and post-stage II samples, respectively. Two independent cultures were used as biological replicates for each ChIP-seq and ChIP-qPCR experiment. Parasite proliferation and gametocyte differentiation were monitored daily by microscopic evaluation of Giemsa-stained smears.escribed , 91. P. escribed –94 throuP. falciparum 3D7 histone H3 sequence as per ENCODE standards  and anti-H3K36me3  antibodies  ChIP was performed as previously described  with modDNA libraries were prepared for sequencing as previously described . End-repChIP-qPCR was performed on the same material used for ChIP-seq with primers listed in Additional file 2, 0.2% (v/v) Nonidet P-40) and protease inhibitors (Roche)). Histones were isolated from nuclei resuspended in Tris buffer (10 mM Tris (pH 8.0), 0.8 M NaCl, 1 mM EDTA and protease inhibitors) by overnight acid-extraction  and subsequent precipitated with 20% TCA. Histone pellets were washed with acetone, reconstituted in dddH2O and spotted quantitatively (100 ng per sample) on nitrocellulose membranes. Membranes were submerged in blocking buffer for 30 min followed by 1 h incubation with α-H3K36me2  or α-H3K36me3  primary antibody dilutions (1:5000 in TBS-t). Membranes were washed three times in TBS-t and then incubated with goat α-rabbit IgG antibody conjugated to HRP (1:10000) for 1 h. Chemiluminescent signal  was quantified with ImageJ analysis software [Gametocytes were treated with JIB-04 (5 µM) on days 2, 3 and 4 of development and sampled 24 h later. Histones were extracted as described before  with minsoftware .P. falciparum genome as previously described [DNA microarrays  based on the full escribed  were useescribed  for the escribed .P. falciparum 3D7 genome (v39 obtained from PlasmoDB) and duplicate and low-quality reads filtered using BWA-MEM (v0.4.1) [2-transformed ChIP/input ratios averaged for 1 kb bins. Occupancy 1.5 kb upstream of gene TSSs   and SAMt(v0.4.1) , respect(v0.4.1)  was used(v0.4.1) . Genome-l., 2016 ; PlasmoDs. MACS2  was useds. MACS2 , 58, 105s. MACS2 . All resP < 0.01), were normalised with Robust-spline for within-array and G-quantile for between slide normalisation in R  using the limma and marray packages. The fit of a linear model was used to obtain log2-transformed expression values (log2Cy5/Cy3). Genes with log2-transformed FC ≥ 0.5 in either direction were defined as differentially expressed in the JIB-04-treated gametocytes. Visualisation of differentially expressed genes was performed using TIGR MeV with functional classification of genes to significantly associated biological processes  performed using PlasmoDB .Microarray signal intensities that passed Genepix standard background filters (Additional file 1. Figures and corresponding legends for Figures S1–S3 as a supplement to main text and figure.Additional file 2: Table S1. Pearson correlation of genome-wide read counts binned into 1 kb for corresponding biological replicates used for ChIP-seq. Table S2. Log2-transformed ChIP/input values for H3K36me2&3 occupancy upstream of 5602 P. falciparum genes in pre-stage II, stage II and post-stage II gametocytes. Table S3. Sites of H3K36me2&3 enrichment that were present in both biological replicates as determined by peak calling for stage II gametocytes. Table S4. Expression values on days 0 to 10 of gametocyte development for the gene nearest to each significant peak. Table S5. Gene ontology analysis results for biological processes significantly associated with H3K36me2&3-enriched gene sets in stage II gametocytes. Table S6. H3K36me2&3-enriched genes in stage II gametocytes previously identified as commitment- or early differentiation-related or associated with HP1, AP2-G2 or AP2-G2 in other studies [ studies –54, 58.Additional file 3: Table S7. Gene expression values and log2fold changes from microarray experiments for the untreated and JIB-04 treated gametocytes on day 2 and 3 with associated functional categories for each gene.Additional file 4: Table S8. Sequence information of peptides used for antibody validation. Table S9. Primer sequences used in ChIP-qPCR validation.


The transcription of developmental regulatory genes is often controlled by multiple cis-regulatory elements. The identification and functional characterization of distal regulatory elements remains challenging, even in tractable model organisms like sea urchins.cis-regulatory analyses available in sea urchin embryos, with particular attention to common developmental regulatory elements known to have enhancer and silencer functions differentially deployed among embryonic territories. Using the three functional genomic data types, machine learning models are trained and tested to classify and quantitatively predict the enhancer activity of several hundred genomic regions previously validated with reporter constructs in vivo.We evaluate the use of chromatin accessibility, transcription and RNA Polymerase II for their ability to predict enhancer activity of genomic regions in sea urchin embryos. ATAC-seq, PRO-seq, and Pol II ChIP-seq from early and late blastula embryos are manually contrasted with experimental Overall, chromatin accessibility and transcription have substantial power for predicting enhancer activity. For promoter-overlapping cis-regulatory elements in particular, the distribution of Pol II is the best predictor of enhancer activity in blastula embryos. Furthermore, ATAC- and PRO-seq predictive value is stage dependent for the promoter-overlapping subset. This suggests that the sequence of regulatory mechanisms leading to transcriptional activation have distinct relevance at different levels of the developmental gene regulatory hierarchy deployed during embryogenesis.The online version contains supplementary material available at 10.1186/s12864-021-07936-0. Variousderstood  allow gederstood , and, dederstood , 11. Hisderstood , 13, chrderstood  or transderstood  facilitaderstood . Enhancederstood –19. In pderstood , which dderstood .Several experimental advantages have facilitated the exhaustive reconstruction of developmental gene regulatory networks (GRNs) in sea urchin embryos –23. The Evolutionary sequence conservation has been routinely used for the identification of potential TREs in sea urchins , althougChromatin accessibility, transcription and the distribution of RNA Pol II using ChIP-seq were used for the identification of candidate developmental enhancers in 20 h sea urchin embryos Fig. A, and thp-value < 2.2 × 10− 16, Spearman = 0.71, Fig. − 16, Spearman = 0.75, Fig. − 16, Spearman = 0.79, Fig. − 16, Spearman = 0.46, Fig. The dynamic range of the read distribution at peak calls expands several orders of magnitude for the three functional genomic data types Fig. B and D. p-values < 2.2 × 10− 16, Fig. H2A.Z locus [Interestingly, there is less variability among Pol II ChIP-seq biological replicates . However, active CRMs in 12 and 24 h embryos have significatively higher PRO-, ATAC-, and Pol II ChIP-seq signals .We decided to systematically test if machine learning models using chromatin accessibility, Pol II distribution and transcription initiation could predict the previously quantified enhancer activity of 389 CRMs  in 20 h embryos , and outperformed dREG in 20 h embryos   Fig. . The dif16) Fig.  due to tmodule D , or it mSpHox11/13b and onecut , and incubated on ice for 5 min. Dissociated cells were further disrupted with 50 or more strokes in a fine dounce homogenizer. Density gradient nuclear wash and floating layers were prepared by diluting iodixanol 60% (OptiPrep™) in 1 X PBS to 20 and 40%, respectively. About 5 ml of nuclear lysate was deposited on top of 10 ml of nuclear wash and nuclei were collected over 200 μl of floating layer after centrifugation for 30 min at 2 °C and 3000 g in a swing bucket rotor. Nuclei aliquots were flash-frozen in liquid nitrogen. For the 20 h stage, nuclei of one of the two ATAC-seq biological replicates was prepared as previously described [Sea urchin embryos were reared to different stages as previously described . Nuclei escribed . For ChIS. purpuratus genome version 3.1 using default settings, except for the MACS2 peak call p threshold, which was set to 0.05. Two biological and one technical replicate libraries were paired or single end sequenced. A total of 16,005,927 20-h and 73,272,494 12-h embryo reads mapped to the genome after deduplication and mitochondrial chromosome filtration. About 44% of the reads locate in peak calls, see quality control summary for detailed reproducibility and sequence quality metrics . The resuspended nuclei were then sonicated at 4 °C for 15 min on high, cycle of 30 s on and 30 s off, to obtain an extract with fragmented chromatin. Extracts were brought up to 440 μL with FA buffer with protease and phosphatase inhibitors. Between 1 to 2 mg of extract and 4 μg of antibody were used per ChIP. Prior to the addition of antibody, 5% of the extract was taken for input. Mouse monoclonal antibody against RNA polymerase II CTD-repeat YSPTSPS  was used for ChIP. The mixture was incubated rotating at 4 °C overnight. 40 μL of protein G sepharose bead slurry  per ChIP sample was washed three times with 1 mL FA buffer, added 40 μL bead slurry to each ChIP sample and rotated at 4 °C for 2 h. Meanwhile, 200 μL ChIP Elution Buffer  and 2 μL 10 mg/μL RNase A were added to inputs and incubated at room temperature. Beads were washed at room temperature by adding 1 mL of each of the following buffers and collecting beads by spinning for 1 min at 2500 g: two times FA buffer for 5 min, one time FA-1 M NaCl for 5 min, one time FA-500 mM NaCl for 10 min, one time TEL buffer  for 10 min, two times TE for 5 min. Proteinase K was added to both inputs and ChIPs and incubated in a 50 °C heat block for an hour. Inputs and ChIPs were allowed to reverse crosslink overnight in a 65 °C water bath. DNA was ligated to Illumina or homemade multiplexed adapters and amplified by PCR. Using a thin 1.5% agarose gel, DNA fragments between 300 and 600 bp were purified using the Qiagen Gel Extraction kit. Qubit flourometer was used to measure DNA concentration. Single-end sequencing was performed for the ChIP-seq and input DNA at the New York University Center for Genomics and Systems Biology high-throughput sequencing facility. We combined replicates and aligned 50 bp single end reads to the on 2.2.3  with defon 2.2.3  with defSignal at PRO-, ChIP-, and ATAC-seq peak calls was quantified using the R package bigWig . ATAC anUsing bedtools , the intThe package caret was used for the optimization, test and evaluation of classification and regression models . LogistiAdditional file 1.Additional file 2: Supplementary Table 1. Genomic locations, genomic signals and reporter activities of the 389 CRMs.Additional file 3: Fig. S1. Promoter and CRM PRO-, ATAC- and ChIP-seq analysis.Additional file 4: Fig. S2. PRO-, Pol II ChIP- and ATAC-seq at onecut, gcm, gatae and delta. A, onecut, the entire region was scanned for enhancer activity by overlapping reporter constructs averaging 2.23 Kb [ 2.23 Kb . SimilarAdditional file 5: Fig. S3. The enhancer activity of promoter-overlapping CRMs is independent of their orientation. A, the concurrent orientation of the promoter in the GFP reporter construct and the promoter of promoter-overlapping CRMs could in principle result in transcripts reaching to the coding region of the reporter that could confound CRM driven transcription (red dashed line) with enhancer activity (black dotted line). In both cases the reporter enhancer activity of CRMs containing endogenous promoters should be diminished due to enhancer sharing between both promoters. Correlation of Pol II accumulation at endogenous promoters  and reporter expression is expected for CRMs that contain necessary enhancers and endogenous promoters. B, the difference of expression between CRMs in concurrent and divergent reporter construct orientation is not significant (ns) in 12 and 24 h embryos, Wilcox test p-values of 0.67 and 0.14, respectively.Additional file 6: Fig. S4. Evaluation of models for CRMs overlapping and not overlapping promoters. A, 12 h embryo and, B, 20 h embryo models trained and tested with CRMs not overlapping promoters. C, 12 h embryo and, D, 20 h embryo models trained and tested with CRMs overlapping promoters.Additional file 7: Fig. S5. Quantitative prediction of enhancer activity from ATAC and PRO-seq data. Plot of the hold-out predicted and actual reporter expression of linear regression models using ATAC and PRO-seq signal in 12 h and 20 h stages. Violin/Box-plots of R2 values, with the average indicated underneath.Additional file 8: Fig. S6. Summary 12 to 20 h mRNA expression changes of regulatory genes. The fold change in the mRNA expression levels quantified at high resolution for 31 of the 37 genes used in this study [is study .