Astrocytes are the most abundant type of glial cell in the central nervous system and perform a myriad of vital functions, however, the nature of their diversity remains a longstanding question in neuroscience. Using transcription factor motif discovery analysis on region-specific gene signatures from astrocytes we uncovered universal and region-specific transcription factor expression profiles. This analysis revealed that motifs for Nuclear Factor-I (NFI) are present in genes enriched in astrocytes from all regions, with NFIB and NFIX exhibiting pan-astrocyte expression in the olfactory bulb, hippocampus, cortex, and brainstem. Further analysis into region-specific motif patterns, identified Nkx3-1, Stat4, Pgr, and Nkx6-1 as prospective region-specific transcription factors. Validation studies revealed that Nkx6-1 is exclusively expressed in astrocytes in the brainstem and associates with the promoters of several brainstem specific target genes. These studies illustrate the presence of multiple transcriptional layers in astrocytes across diverse brain regions and provide a new entry point for examining how astrocyte diversity is specified and maintained. The brain is composed of an incredible array of diverse cell types, of which, glial cells account for approximately half of this mosaic . AstrocyAstrocytes are electrically silent cells, making it difficult to characterize their functional diversity based on electrophysiological activities. Methods for understanding neuronal cell diversity, such as whole cell electrophysiology , morpholOne important question remaining is what controls the unique expression profiles observed in astrocyte populations from distinct brain regions? In the spinal cord, positionally distinct subpopulations of astrocytes arise from the differential expression of transcription factors during development and this combinatorial transcription factor code results in three distinct astrocyte subpopulations in the developing spinal cord . ApplyinAll research and animal care procedures were approved by the Baylor College of Medicine Institutional Animal Care and Use Committee and housed in the Association for Assessment and Accreditation of Laboratory Animal Care-approved animal facility at Baylor College of Medicine. Both male and female BAC Aldh1l1–eGFP mice were used. All strains were maintained on C57BL6 background.The olfactory bulb, hippocampus, cortex, and brainstem from 16-week old Aldh1l1–eGFP mice was dissected and dissociated using the protocol in Total RNA was extracted from Aldh1l1–eGFP+ FAC-sorted cells using the RNeasy Micro Kit  and quality controlled using the High Sensitivity RNA Analysis Kit  on a 12-Capillary Fragment Analyzer. cDNA synthesis, library construction and rRNA depletion was performed on 5 ng total of RNA using the Trio RNA-Seq System . The resulting single index libraries were validated using the Standard Sensitivity NGS Fragment Analysis Kit  for size confirmation and quantified using the Quant-iT dsDNA Assay Kit, high sensitivity . Samples were diluted to equimolar concentrations (2 nM), pooled, and denatured according to the manufacturer’s protocol. The final library dilution of 1.3 pM was sequenced on a NextSeq500 using the High Output v2 kit  for paired-end (2 × 75) sequencing of approximately 40 million reads per sample.Demultiplexed sequencing files were downloaded from BaseSpace and quality control was assessed using fastQC (v0.10.1) and MultiQC (v0.9) . Reads wp < 0.01. Gene Ontologies associated with region-specific DEGs were determined using Enrichr and visualized using GOplot (v1.0.2) and ggplot2.To identify unique gene signatures, we compared global gene expression from one region to all three other regions using DESeq2. This process was repeated to determine region-specific gene expression patterns. We defined differentially expressed genes (DEGs) as those with normalized reads per million (RPM) >5 in at least two of the replicates and expression fold-change >1.5 at To identify any transcription factor motifs that are enriched across region-specific gene signatures, the DEGs from each region were pooled to comprise one list of 3555 genes. These genes were analyzed using Hypergeometric Optimization of Motif EnRichment (HOMER) (v4.10) to identify transcription factor motifs enriched within 2 kb of the gene’s promoter sequence. To be considered enriched across all regions the transcription factor motif had to be present in at least 50% of DEGs from each region. Transcription factors with enriched motifs were further analyzed to determine their expression patterns across regions. Those with an RPM >5 in at least two of the replicates were used for downstream analysis.p < 0.01 in the region of interest.Applying the same parameters outlined above, DEGs from each region were individually subjected to motif analysis using HOMER to discover transcription factor motifs enriched within 2 kb of the gene’s promoter sequence. The resulting list of enriched motifs was filtered based on expression data to identify regionally specific transcription factors. We considered a transcription factor regionally enriched only with a fold change >2 at Perfusion and tissue collection were performed as described previously in Potential transcription factor targets were predicted using HOMER’s annotatePeaks.pl with the -m option. Each set of regional DEGs was interrogated for the presence of the associated transcription factor motif allowing zero mismatch. Only genes that had the appropriate motif sequence within 2 kb of the transcriptional start site were considered possible targets. The resulting list was then subset to include only genes with a fold change >2.5 for each region. Predicted target enrichment was visualized using ComplexHeatmap (v2.0.0) and circlize (v0.4.6). Gene Ontologies of predicted targets were determined using Enrichr .Brainstems were collected from 16-week old mice for ChIP-PCR validation of Nkx6-1 targets. Tissue was coarsely chopped and washed with cold PBS before dissociation with a pellet homogenizer. The homogenate from 6 dissociated brainstems were pooled for subsequent sample preparation. Crosslinking was performed using freshly prepared 1.1% formaldehyde solution  while rocking for 10 min and neutralized by adding glycine (125 mM). Samples were centrifuged at 3500 rpm for 5 min at 4°C, washed with PBS (containing 1 mM PMSF), and pellets were stored at −80°C or lysed immediately. All remaining buffers contain protease inhibitors (Roche Cat. 04693132001). To release nuclei cell pellets were resuspended in PBS/PMSF containing 0.5% Igepal and washed with cold ChIP-Buffer 1  and rotated for 10 min at 4°C followed by centrifugation at 1200 rpm for 5 min at 4°C and washing with ChIP-Buffer 2  while rotating at room temperature. Cells were collected via centrifugation and incubated in ChIP lysis buffer  for 15–20 min at room temperature to lyse nuclei. Lysates were sonicated to approximately 200–500 bp length fragments using a Bioruptor . Fragment lengths of chromatin were confirmed using the Standard Sensitivity NGS Fragment Analysis Kit  on a 12-Capillary Fragment Analyzer and quantified using Quant-it dsDNA assay kit (Cat. Q33120). An input of 100 μg of sonicated chromatin was used for each experiment, and 1 ug was saved as input chromatin. Samples were diluted 5-fold with ChIP-dilution buffer  and immunoprecipitation was performed overnight at 4°C with 10 μg of Nkx6-1 antibody  and mouse IgG  while rotating. Samples were then incubated with Dynabeads (Invitrogen) for 6 h and purified through a series of washes with TSE1 buffer , TSE2 buffer , LiCl buffer  and TE buffer . Samples with beads were then incubated at 65°C for 20 min in ChIP Elution buffer . ChIP samples and input control were incubated at 55°C with proteinase K (0.2 mg/ml) and NaCl (125 mM) for 3 h followed by overnight incubation at 65°C to reverse crosslinking. Immunoprecipitated DNA was purified using the Qiagen PCR purification kit and analyzed using primers specific to the Hoxc4 and Hoxb3 promoters .Aldh1l1 is a validated marker of astrocytes  and AldhTo evaluate the regional diversity of these astrocyte populations, we probed the transcriptome of the four brain regions using various bioinformatics approaches. First, we used principal component analysis (PCA) as an unbiased approach to analyze global gene expression patterns in each region. The PCA revealed distinct gene expression patterns that were unique for each region . The olfp < 0.01. We found 1360 DEGs in the OB, 398 DEGs in the HC, 505 DEGs in the CX, and 1292 DEGs in the BS . The identified DEGs are unique to the region of interest, such that a DEG is significantly up or down regulated only in the respective region when compared to all other regions and exhibits a fold change >1.5 at n the BS . VisualiFinally, to gain insight into the cellular pathways regulated by these DEGs, we performed gene ontology (GO) analysis on the DEGs from each brain region . We founTo investigate how transcriptional regulation maintains conserved gene ontologies across the brain, we pooled all region-specific DEGs, hypothesizing that since these distinct DEGs are associated with some functionally redundant gene ontologies across the brain, they are likely to be regulated by universally conserved transcription factors. We analyzed the pool of 3555 DEGs from all regions for transcription factor motif enrichment, querying transcription factor binding sites within 2 kb of the 5′ promoter regions of these genes. We found seven transcription factor motifs (RPM >5) that were significantly enriched in DEGs from all four brain regions , and thein silico analysis suggests that NFI transcription factors operate in mature astrocytes to regulate the expression of key genes associated with synaptic physiology. Moreover, NFI transcription factors exhibit a significantly higher motif frequency in all regional DEGs; therefore, we set out to validate expression of the NFI family members in adult astrocytes from all four regions. Since NFIA is known to be expressed in adult astrocytes (Our trocytes , we sougtrocytes . We findtrocytes . Furthertrocytes , we alsop < 0.01 we were able to identify uniquely enriched transcription factors in the OB, CX, and BS (Thus far, our observations indicate the existence of transcription factors that are universally expressed in nearly all adult astrocytes, across a host of diverse brain regions. Given that transcription factor patterning has been shown to define distinct regional expression profiles in the spinal cord  and beca, and BS . The HC , and BS . In the , and BS  and Nkx6, and BS , both of, and BS . These rNext, we sought to validate our bioinformatics analyses by performing immunolabeling with antibodies to candidate transcription factors on brains from 16-week old Aldh1l1-eGFP mice. We found that the CX-specific transcription factor, Pgr, was expressed in neurons throughout the brain. However, Pgr co-localized with 51%  of Aldh1p < 0.01. Using this approach, we identified 66 predicted targets of Nkx3-1 in the OB, 45 for Nkx6-1 in the BS, and only 5 for Pgr in the CX. As there were no significantly enriched transcription factors identified in the HC it was not considered in subsequent analysis. The log2 transformed RPM of each transcription factor’s targets are visualized in a heatmap (It has been suggested that astrocytes maintain regional heterogeneity to afford them specialized functions for interacting with neurons in their specific regional circuitry units . Region- heatmap .Next, to determine what biological pathways these potential targets were associated with we performed GO analysis . TargetsThe molecular heterogeneity of astrocytes across diverse brain regions has been profiled extensively in recent years, but a clear understanding of the mechanisms that give rise to their vast diversity has long eluded us. In the present study we hypothesized that differential expression of transcription factors controls the region-specific molecular signatures observed in astrocytes across the brain. To test this, we analyzed the astrocyte transcriptomes across four brain regions to first establish DEGs in each region of interest that constitute region-specific gene signatures. Further analysis of region-specific DEGs revealed functionally redundant gene ontologies are associated with the unique gene profiles from each region. Together, these observations suggest that core astrocyte functions are achieved through distinct molecular mechanisms. This prompted us to search these signatures for transcription factors whose expression is conserved across all region-specific DEGs, as these transcription factors may regulate functionally redundant gene ontology pathways. Critically, we find the motif sequence of the NFI family of transcription factors enriched in DEGs and ubiquitous expression of NFIB/NFIX in astrocytes from all four regions . FinallyTranscriptomic analyses have indeed revealed unique region-specific astrocyte signatures that translate to spatially distinct functional differences . HoweverAnother possibility is that these diverse features of astrocytes are developmentally pre-ordained, where molecularly distinct subpopulations of astrocytes are specified, and each subtype migrates to different locations where they maintain region-specific heterogeneity into adulthood. In support of this mechanism, it has been shown that astrocyte spatial identity  and hetein vitro by regulating Notch signaling (Here, we ask if a transcription factor code can be defined for maintaining regionally distinct astrocyte populations. By interrogating region-specific DEGs our analysis revealed Nkx2-2, Maz and NFI family members as transcription factors that are universally conserved across the brain and regulate functionally redundant gene ontologies. Nkx2-2 is known to repress neurogenesis to promote oligodendrocyte precursor cell differentiation . Nkx2-2 ignaling . Here, wignaling . Less isignaling . Despitein vivo and found that Pgr is expressed in neurons throughout the brain, but colocalizes with astrocytes only in the cortex while Nkx6-1 expression is exclusive to the BS where it labels most astrocytes. These data supported our hypothesis that astrocyte regional heterogeneity is maintained by region-specific transcription factors. Furthermore, the region-specific expression of a developmental patterning factor suggests astrocyte diversity may be intrinsically specified during development and it merits investigation in future studies.Additionally, our data suggests that Nkx3-1, Pgr, and Nkx6-1 may act as region-specific transcription factors to regulate the unique molecular profiles observed in astrocytes across regions. The HC had the fewest DEGs compared to the other regions, making it more difficult to identify enriched transcription factors. However, the identification of region-specific transcription factors in the OB, CX, and BS suggests that the unique molecular signatures identified in each region may be maintained by Nkx3-1, Pgr, and Nkx6-1, respectively. Nkx3-1 expression was reported in the OB, but it has not been studied in astrocyte function or maintenance . PreviouWhile understanding how astrocyte diversity is specified and maintained is important it is also critical to determine why they display such broad diversity. One likely explanation is that astrocyte diversity enables specialized interactions between astrocytes and the neuronal circuits of their spatial domain . Indeed,To investigate the regulatory networks that may be controlled by these region-specific transcription factors we determined which genes they are regulating by predicted targets of Nkx3-1, Pgr, and Nkx6-1 in their respective brain regions. Nkx3-1 and Nkx6-1 had the largest number of potential targets and because Nkx6-1 showed the most exclusive region-specific expression in the BS, we confirmed direct regulation of one of its targets, Hoxc4 and Hoxb3, using ChIP-PCR. It is well documented that Hox genes follow distinct regional expression patterns and have been implicated in dorso-ventral patterning of the spinal cord and hindbrain . The rolOur discovery of Nkx6-1 as a region-specific transcription factor in the adult brain, coupled with its established role in developmental patterning of astrocytes in the spinal cord, suggests that astrocytes in the brain are also subject to transcriptionally regulated patterning. These prospective patterning mechanisms could contribute to molecularly and functionally diverse populations of astrocytes throughout the brain. In summary, our study not only provides evidence of a region-specific transcription factor code through the identification of Nkx6-1, but also opens the door to identifying and characterizing astrocytic patterning transcription factors in the brain. In addition, our study defines a new approach to study astrocyte diversity by interrogating transcription factor profiles to provide insights into region-specific gene regulatory networks across the brain.GSE143282.The datasets generated for this study can be found in the GEO repository, accession The animal study was reviewed and approved by the Baylor College of Medicine IACUC.BL and BD conceived the project. T-WH performed all the immunostaining. DS assisted with the writing and ChIP assay. AH bred the mice and assisted with FACS. BL performed all bioinformatics analysis.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Saccharomyces cerevisiae offers several advantages for genetic engineering for this purpose due to its widespread use as a model system studied by many researchers. The focus of this review is to present a primer on understanding genomic considerations prior to genetic modification and manipulation of S. cerevisiae. The choice of a site for genetic manipulation can have broad implications on transcription throughout a region and this review will present the current understanding of position effects on transcription.The growing global population and developing world has put a strain on non-renewable natural resources, such as fuels. The shift to renewable sources will, thus, help meet demands, often through the modification of existing biosynthetic pathways or the introduction of novel pathways into non-native species. There are several useful biosynthetic pathways endogenous to organisms that are not conducive for the scale-up necessary for industrial use. The use of genetic and synthetic biological approaches to engineer these pathways in non-native organisms can help ameliorate these challenges. The budding yeast  As the global population grows and countries continue to develop, the demand for fuel continues to rise. Traditional fuel sources are finite and contribute to climate change, stressing the importance of renewable sources of energy. This shift offers many benefits, including reductions in greenhouse gasses and improved energy security. Many countries use field crops such as corn, wheat, barley, sugar cane, and cassava to produce ethanol; however, their use as a biomass source competes with their use as food crops, increasing demands for land and water resources ,2. AlthoThe native metabolic pathways in diverse microbes, including wild yeasts and bacteria, are particularly attractive for use in the production of biofuels via fermentation, which can generate bioethanol from a variety of non-food crop substrates, such as glycerol ,4. The mEscherichia coli, Clostridium acetobutylicum, Streptomyces venezuelae, and Saccharomyces cerevisiae have proven successful in the introduction of non-native pathways in each organism . Th. ThKANR RRB or Ribi), consuming significant amounts of intracellular energy stores and under tight regulation, predominantly at the level of transcription [The production of mature, translationally competent ribosomes requires the coordinated gene expression of two distinct families of protein coding genes: the 129 ribosomal proteins (RPs) and the more than 200 rRNA and ribosome biosynthesis genes  and the polymerase A and C (PAC) element, both of which are enriched within the RRB family as a whole [MRX12 contains no readily identifiable transcription factor-binding site; rather, the genome region immediately upstream is a nucleosome devoid autonomously replicating sequence  counterparts [A systematic survey in budding yeast revealed that there are many functionally related gene families found in a non-random genomic distribution—this phenomenon is not limited to genes involved in the synthesis of the ribosome. Approximately, one-quarter of functionally related gene families in terparts . It is lterparts . SRG1-SER3 genomic locus found on chromosome V. SRG1 is a non-coding RNA that prevents the transcription of SER3—which can only be actively transcribed when the SRG1 transcript is repressed—as the transcript extends over the transcription start site for SER3, altering the underlying chromatin [Proper gene regulation via genomic clustering can also involve transcriptional interference, which results in an anti-correlated expression of neighboring genes due to mutually exclusive expression. The canonical example of this type of transcriptional regulation is seen at the hromatin ,76. GAL genes cluster on chromosome II—there are three genes necessary for galactose catabolism , and the production of beta-carotene [S. cerevisiae; they likely extend throughout the fungal kingdom and to related eukaryotes [Due to its versatility and potential, carotene ,81,82,83karyotes ,84.


Eukaryotic gene expression is extensively regulated by cellular stress and pathogen infections. We have previously shown that herpes simplex virus 1 (HSV-1) and several cellular stresses cause widespread disruption of transcription termination (DoTT) of RNA polymerase II (RNAPII) in host genes and that the viral immediate early factor ICP27 plays an important role in HSV-1-induced DoTT. Here, we show that HSV-1 infection also leads to widespread changes in alternative polyadenylation (APA) of host mRNAs. In the majority of cases, polyadenylation shifts to upstream poly(A) sites (PAS), including many intronic PAS. Mechanistically, ICP27 contributes to HSV-1-mediated APA regulation. HSV-1- and ICP27-induced activation of intronic PAS is sequence-dependent and does not involve general inhibition of U1 snRNP. HSV1-induced intronic polyadenylation is accompanied by early termination of RNAPII. HSV-1-induced mRNAs polyadenylated at intronic PAS (IPA) are exported into the cytoplasm while APA isoforms with extended 3’ UTRs are sequestered in the nuclei, both preventing the expression of the full-length gene products. Finally we provide evidence that HSV-induced IPA isoforms are translated. Together with other recent studies, our results suggest that viral infection and cellular stresses induce a multi-faceted host response that includes DoTT and changes in APA profiles. Viral infections profoundly alter host cell gene expression. It is important to understand both how viruses hijack the host cell machineries to express their own genes and how host cells respond to viral infection for defense. We have previously shown that herpes simplex virus-1 (HSV-1) blocks host cell transcription termination, at least in part, through the viral immediate early protein ICP27. Here we show that HSV-1 infection also alters mRNA 3’ end formation and promotes the formation of truncated mRNAs. Some of these aberrant mRNAs are exported into the cytoplasm and translated. This viral activity requires ICP27 and other viral factors. Our study, together with other recent reports, suggests that viral infections and cellular stress elicit similar responses in mammalian cells. The 3’ ends of the vast majority of eukaryotic mRNAs are formed through cleavage and polyadenylation –3. In maRNA 3’ processing also plays an important role in gene regulation. The transcripts of over 70% of human genes can be cleaved and polyadenylated at multiple alternative PAS, a process called alternative polyadenylation (APA) . DiffereBoth RNA 3’ processing and transcription termination are highly regulated. For example, we have previously shown that HSV-1 infection leads to a widespread disruption of transcription termination (DoTT) ,14. InflTo determine if and how the global APA profile of host genes is altered during HSV-1 infection, we performed PAS-seq analysis of HeLa cells at 0, 2, 6, and 12 hours post-infection (hpi). PAS-seq is a method developed in our laboratory for quantitatively mapping RNA poly(A) junctions . BrieflyEXOSC4 transcripts shifted from a PAS in the 3’ UTR to a proximal intronic PAS  of the 1,050 genes. The greatest shifts in APA profile occurred between 6 and 12 hpi . Howeveronic PAS . The majequently . Finallyd 12 hpi . TogetheTOB2  data, which provides information on transcription activities . We focuTOB2 . Here, aream PAS . As a coream PAS , the 4sUream PAS , indicatJB6 gene . A majorJB6 gene . In HSV-JB6 gene . TogetheEXOSC4 transcripts, and a similar activation was not observed in ΔICP27-infected cells . On the global level, we compared the PAS-seq reads in the 5 kilobase (kb) region downstream of the annotated TES for all PtoD genes and found that, indeed, there are significantly higher PAS-seq reads within this region in HSV-1-infected cells compared to mock treated cells ; 2) Mapping Ribo-seq reads to nucleotide resolved ribosome positions NNNNAGATCGGAAGAGCGTCGTGTTCGGATCCATTAGGATCCGAGACGTGTGCTCTTCCGATCTTTTTTTTTTTTTTTTTTTT[V-Q] and Superscript III. cDNA was recovered by ethanol precipitation and centrifugation. 120–200 nucleotides of cDNA was gel-purified and eluted from 8% Urea-PAGE. Recovered cDNA was circularized with CircligaseFrom the raw PAS-seq reads, first those with no poly(A) tail (less than 15 consecutive “A”s) were filtered out. The rest were trimmed and mapped to hg19 genome using STAR. If 6 consecutive “A”s or more than 7 “A”s were observed in the 10 nucleotide downstream of PAS for a reported alignment, it was marked as a possible internal priming event and removed. The bigwig files were then generated for the remaining reads using deepTools (v2.4) with “normalizeUsingRPKM” and “ignoreDuplicates” parameters .Next, the locations of 3’ ends of the aligned reads were extracted and those in 40nt of each other were merged into one to provide a list of potential PAS for human. This list was then annotated based on the canonical transcripts for known genes. The final read count table was created using the reads with their 3’ ends in -40nt to 40nt of these potential PAS.Alternatively polyadenylated PAS in different experimental conditions were identified using diffSpliceDGE and topSpliceDGE from edgeR package(v3.8.5) . This piFor the genes with alternatively polyadenylated sites (target genes), the log2 of ratio of read counts in the distal site to the read counts in the proximal site was calculated and illustrated as a heatmap in Figs We applied Bowtie 1.0 (REF) to map reads to rRNA, genomic and transcriptomic sequences from the Ensembl database (version 75). rRNA reads and reads mapping to the mitochondrial genome were discarded. All alignments were mapped to genomic coordinates. Fractional counts were used for ambiguous alignments (with regard to genomic coordinates). We then used the probabilistic model implemented in Price (version 1.0.3b)  to map rMeta-analyses of read distribution were performed using deeptools . 4SU-seqRNA-seq data on the subcellular RNA fractions, 4sU-seq, and Ribo-seq data were previously published ,17. PAS-S1 Fig(A) Normalized PAS-seq read counts at proximal and distal PAS of APA genes. (B) A heat map of log(APA index) scaled by column.(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Fig(A) A heat map showing the log(APA index) for genes with significant APA changes between mock and HSV-1-infected cells . (B) A heatmap showing the APA index  for all genes that displayed significant APA changes in either KOS or dICP27-infected cells (combination of genes in (TIFF)Click here for additional data file.S4 FigP values were calculated using the hypergeometric test.(TIFF)Click here for additional data file.S5 Fig(A) Distribution of AWTAAA motif at the PAS-seq peaks downstream of normal TES. (B) Nucleotide composition of cryptic PAS downstream of normal PAS. (C) RNA-seq tracks of cytoplasmic RNAs in mock- and HSV-1-infected cells (8 hpi). The ratio of RNA-seq read counts in the 500 nt upstream and 500 nt downstream of the intronic PAS for DtoP (D) and PtoD (E) genes for the cytoplasmic fractions of mock- and HSV-1-infected cells.(TIFF)Click here for additional data file.S6 FigLactimidomycin is a translation inhibitor.(TIFF)Click here for additional data file.


Histone deacetylases (HDACs) play important roles in transcriptional regulation in eukaryotic cells. Class I deacetylase HDAC1/2 often associates with repressor complexes, such as Sin3 (Switch Independent 3), NuRD (Nucleosome remodeling and deacetylase) and CoREST (Corepressor of RE1 silencing transcription factor) complexes. It has been shown that HDAC1 interacts with and modulates all essential transcription factors for erythropoiesis. During erythropoiesis, histone deacetylase activity is dramatically reduced. Consistently, inhibition of HDAC activity promotes erythroid differentiation. The reduction of HDAC activity not only results in the activation of transcription activators such as GATA-1 (GATA-binding factor 1), TAL1 (TAL BHLH Transcription Factor 1) and KLF1 (Krüpple-like factor 1), but also represses transcription repressors such as PU.1 (Putative oncogene Spi-1). The reduction of histone deacetylase activity is mainly through HDAC1 acetylation that attenuates HDAC1 activity and trans-repress HDAC2 activity through dimerization with HDAC1. Therefore, the acetylation of HDAC1 can convert the corepressor complex to an activator complex for gene activation. HDAC1 also can deacetylate non-histone proteins that play a role on erythropoiesis, therefore adds another layer of gene regulation through HDAC1. Clinically, it has been shown HDACi can reactivate fetal globin in adult erythroid cells. This review will cover the up to date research on the role of HDAC1 in modulating key transcription factors for erythropoiesis and its clinical relevance. Histone deacetylases (HDACs) catalyze the removal of acetyl groups from the ε-amino groups of lysine residues. The reversible acetylation of histones and non-histone proteins by histone acetyltransferases (HATs) and HDACs play critical roles in transcriptional regulation in eukaryotic cells. Histone acetylation is commonly associated with transcriptional activation of genes, and is thought to be responsible for the formation of a local “open chromatin” structure required for the binding of multiple transcription factors (Reviewed in ). In conMammalian HDACs are classified into four classes  based on the sequence homology of the yeast histone deacetylases Rpd3 (reduced potassium dependency), Hda1 (histone deacetylase 1), and Sir2 (silent information regulator 2), respectively. Class I HDACs include HDAC1, 2, 3 and 8, class II HDACs contain HDACs 4, 5, 6, 7, 9 and 10. Class III enzymes, however, require the coenzyme NAD+ (Nicotinamide adenine dinucleotide) as a cofactor. HDAC11 belongs to the class IV family. Class I HDACs are ubiquitously expressed nuclear proteins. Although these enzymes share a high level of sequence homology and common substrates, and HDAC2 can partially compensate for HDAC1 loss, each of the enzymes has a unique role in cell function, as deletion of each member of the class I deacetylase leads to lethality (reviewed in ), indicaHDAC1 is found in at least three evolutionally conserved, distinct protein complexes: the Sin3, the CoREST and the Mi-2/NuRD complexes (Reviewed in ). HDAC1 The CoREST complex is a multi-subunit complex containing the lysine demethylase LSD1 (Lysine-specific demethylase 1), corepressor CtBP , BHC80 (BRAF35-HDAC complex protein BHC80), CoREST, HDAC1 and HDAC2 ,30,31,32The NuRD complex includes the ATPase/helicase Mi-2, HDAC1/2, MTA (Metastasis-associated) proteins, MBD2 (methyl CpG-binding domain-2), and RbAp46/48 ,37. Mi-2In addition to these three complexes, HDAC1 also associates with other sub-complexes during development , includiHDAC1 activity is modulated through various post translational modifications including phosphorylation, acetylation, ubiquitination, sumoylation, and nitrosylation . HDAC1 iHDAC1 can also undergo acetylation modification. Qiu et al. 2006) initially reported HDAC1 acetylation at six lysine residues  initiall. AcetylaUnlike constitutive phosphorylation, acetylation on HDAC1 is an inducible event. Glucocorticoid receptor (GR) associated HDAC1 becomes acetylated by p300 when GR mediated transcription activation is down regulated on the GR responsive promoter . CellulaHDAC1 and HDAC2 often coexist in multi-component protein complexes and are highly related enzymes ,15,16. IBlood cells consist of cells from multiple lineages, which are specialized for oxygen delivery, homeostasis, and defense from infection. Diverse lineages of blood cells originate from hematopoietic stem cells (HSC), which reside within the bone marrow. HSC give rise to multipotent progenitors (MPPs) that continue to differentiate into common myeloid progenitors (CMPs) and common lymphocyte progenitors (CLPs), which further differentiate toward the specific cell lineages. CMPs differentiate to generate megakaryocytes, red blood cells, neutrophils and macrophages, while CLPs differentiate into B cells and T cells ,63. Hdac1 in mice results in embryonic lethality before E10.5 due to severe proliferation defects and retardation in development [Hdac1/2 double knockout in hematopoietic compartment was established in mice harboring the interferon-inducible MxCre (Mx dynamin-like GTPase/Cyclic recombinase) transgene and conditional knockout alleles for Hdac1 and/or Hdac2. The Hdac1/2 double knockout mice die approximately nine days after pI/pC (Polyinosinic/polycytidylic acid) injections, displaying significant hematopoietic defects that reduced bone-marrow cell numbers and affected differentiation of all major hematopoietic lineages. The reduction of bone marrow cell numbers was associated with an increase in apoptotic bone marrow cells. The most significant defect was in erythroid/megakaryocyte compartment, including reduction of mature differentiated cells and increased apoptosis [Hdac1 and Hdac2 show compensatory and overlapping functions in hematopoiesis. However, Hdac1 knockout mice expressing mono-allelic Hdac2 showed severe erythropoiesis defect, with mice dying 12 days after pI/pC induction. The defect included reduction of proerythroblast and mature erythroblast in bone marrow and compensatory extramedullary hematopoiesis. Hdac2 knockout mice expressing mono-allelic Hdac1 developed normally, indicating the essential role of HDAC1 in erythropoiesis [Hematopoietic deficiencies such as anemia, thrombocytopenia, neutropenia, and lymphopenia have been reported in clinical trials with various HDACi , indicatelopment . The Hdapoptosis ,64. IndiExperiments conducted in hematopoietic progenitor cells show that HDAC1 deacetylase activity plays diverse roles in erythropoiesis, including promoting cell survival and proliferation ,66,67. IErythropoiesis is regulated through lineage specific transcription factors for the proliferation and differentiation of multipotent progenitors into the erythroid lineage. Among them, GATA-1, GATA-2 (GATA-binding factor 2), FOG-1 (Friend of GATA Protein 1), TAL1/SCL/LMO2/Ldb1/E2A (TAL BHLH Transcription Factor 1/Stem cell leukemia/ LIM Domain Only 2/ LIM-domain-binding protein 1/ Early 2A protein), KLF1, Gfi-1b (Growth factor independent protein 1b), BCL11A (B-cell lymphoma/leukemia 11A), NF-E2 , and PU.1 are hematopoietic-specific transcription factors required for erythropoiesis . ImportaHematopoietic lineage specific transcription factor GATA-1 is the founding member of the GATA factor family. GATA-1 is primarily expressed in erythroid lineage. It is also expressed in megakaryocytes, eosinophils, mast cells, and sertoli cells of the testis . GATThe transcriptional activity of GATA-1 is regulated through interactions with a variety of transcription factors and cofactors. GATA-1 interacts with TAL1, KLF1, PU.1, and SP1 (Sp1 transcription factor) . GATA-1 GATA-1 can be acetylated at both N- and C-terminal zinc-fingers at conserved lysine residues. Acetylation of GATA-1 may or may not affect DNA binding depending on the GATA-1 binding sites ,87. LysiE2A or HEB, and recognizes a hexanucleotide sequence CANNTG termed “E box” to act as regulators of transcription. Knockout of Tal1 in mice leads to embryonic lethality at E8.5 due to complete loss of yolk sac hematopoietic cells [Tal1 null embryonic stem cells are unable to generate both primitive and definitive erythroid cells in vitro and do not contribute to hematopoiesis in vivo in chimeric mice [TAL1 is a member of the basic helix-loop-helix (bHLH) family of transcription factors and is required for the development of all hematopoietic cell lineages . TAL1 foic cells ,95. Consric mice ,96. The ric mice . All of ric mice ,100,101.During hematopoiesis, TAL1 can function as a repressor or an activator of transcription depending on the DNA sequence and/or cellular contexts . TAL1 rePU.1 belongs to ETS (Erythroblast Transformation Specific) family of transcription factor that plays an essential role in hematopoietic stem cell self-renewal and in directing multipotent hematopoietic stem cells towards lineage commitment ,110,111.PU.1 gene expression can be regulated epigenetically ,123,124.In addition, HDAC1 can directly modulate PU.1 activity. It was found that the C-terminal domain of PU.1 formed a complex with mSin3A and HDAC1 in vivo, which mediates PU.1 induced transcriptional repression . FurtherKLF1 (EKLF) is another transcription factor essential for erythropoiesis. KLF1 expression is restricted to hematopoietic compartment and expressed in both primitive and definitive erythroid populations . Gene abKLF1 regulates erythropoiesis mainly through activation of gene transcription ,136,137.Although KLF1 has been primarily characterized as a transcriptional activator, it can also exert transcriptional repression. p300/CBP (E1A binding protein p300/CREB-binding protein) can acetylate KLF1 at lysine 288. Acetylation leads to increased association of KLF1 with the chromatin remodeling SWI/SNF-related complex, resulting in an open chromatin domain and transcription of the adult β-globin promoter ,139,140.Interestingly, KLF1 also physically interact with TAF9, a component of TFIID complex. This interaction is required for a subset of KLF1 activated genes, including β-globin promoter, and is necessary for its activation. Since the interaction is not required at all KLF1 activation target promoters, it is conceivable that the requirement of TAF9 by KLF1 may depend on specific promoter sequences DPE, which is present at the β-globin promoter ,145. FurThe family of Gfi-1 zinc finger oncoproteins consists of Gfi-1 and Gfi-1b. Studies from gene targeting and mutational screening in humans have revealed an essential role for Gfi-1 and Gfi-1b in hematopoiesis . In miceGfi-1b p32 is an alternative spliced form that lacks the first two zinc finger domains of the protein. Gfi-1b p32 isoform binds to Gfi-1b target gene promoters and associates with the LSD1-CoREST repressor complex more efficiently than the major Gfi-1b p37 isoform. Selective knock down of Gfi-1b p32 compromises erythroid differentiation, further demonstrates that association of CoREST complex is essential for the repressive effect of Gfi-1b . Bcl11a in mice prevents silencing of endogenous mouse fetal globin and transgenic human γ-globin [Bcl11a in adult mice results in derepression of fetal globin, indicating that BCL11A actively repress fetal globin expression in the adult stage and that repression is reversible. Importantly, knock down of BCL11A in transgenic sickle cell disease mice stimulated therapeutic levels of fetal globin, suggesting the importance of BCL11A in hemoglobinopathy treatments [BCL11A is a zinc finger transcription repressor. Genome-wide association studies (GWAS) have linked BCL11A to fetal globin level, which directly associates with the clinical severity of sickle cell disease and β-thalassemia ,152. Genγ-globin . Inducibeatments .BCL11A binds to the globin locus control region (LCR) and multiple other regions at the β-globin gene locus to regulate globin gene expression ,156,157.Erythropoietin (EPO) is the primary hormone that regulates erythroid cell maturation. EPO binds to its cognate receptor EpoR and subsequently activates the Janus Kinase 2 (JAK2), which activates signaling pathways, including mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3 kinase), signal transducer and activator of transcription 5 (Stat5) ,160. EPOSickle cell disease and β-thalassemia represent the most common hemoglobinopathies. The expression of normally silenced fetal globin in adult erythroid cells can ameliorate the pathophysiological consequences. Therefore, mechanisms of fetal globin reactivation have been intensively studied in the past few decades. In addition to factors such as BCL11A that mediate silencing of the γ-globin gene in adult stage erythroid cells, epigenetic factors, such as chromatin remodelers and histone modification enzymes have been intensively studied for their role in fetal globin gene expression ,166. Theclinicaltrials.gov). The Vorinostat treatment shows effective elevation of γ-globin level and increased γ to β globin ratios. However, the trial was terminated due to blood related adverse events [Two clinical trials have been conducted with two pan-HDAC inhibitors for sickle cell disease therapy. One trial used Vorinostat and another trial used Panobinostat as a single drug for therapy . HDAC1 The aberrant recruitment of HDAC1 is linked to various types of leukemia ,178,179.


Helicobacter pylori (H. pylori) infects nearly half of the world’s population with a variation in incidence among different geographic regions. Genetic variants in the promoter regions of the IL1B gene can affect cytokine expression and creates a condition of hypoacidity which favors the survival and colonization of H. pylori. Therefore, the aim of this study was to characterize the polymorphic sites in the 5′- region [−687_ + 297] of IL1B in H. pylori infection using in silico tools.IL1B which led to the addition or alteration of transcription factor binding sites (TFBSs) or composite regulatory elements (CEs). Genotyping of IL1B − 31 C > T revealed a significant association between -31 T and susceptibility to H. pylori infection in the studied population (P = 0.0363). Comparative analysis showed conservation rates of IL1B upstream [−368_ + 10] region above 70% in chimpanzee, rhesus monkey, a domesticated dog, cow and rat.A total of five nucleotide variations were detected in the 5′-regulatory region [−687_ + 297] of H. pylori-infected patients, three detected SNPs  located in the IL1B promoter were predicted to alter TFBSs and CE, which might affect the gene expression. These in silico predictions provide insight for further experimental in vitro and in vivo studies of the regulation of IL1B expression and its relationship to H. pylori infection. However, the recognition of regulatory motifs by computer algorithms is fundamental for understanding gene expression patterns.In  Helicobacter pylori (H. pylori) is a Gram-negative, spiral-shaped and microaerophilic bacterium that infects nearly half of the world’s population with a variation in incidence among different geographic regions  were deposited in the GenBank database under the following accession numbers: from MT767762 to MT767775.In Sudanese the IL1B 5′- region [−687_ + 297] and the results are presented in Table IL1B translational start codon (transcript NM_000576.3), whose prediction scores were 0.97, 0.60 and 0.96, respectively. While Fprom, TSSG and TSSW programs predicted one promoter region, + 1 bp, which is the only region predicted by all used prediction programs.Five types of promoter prediction programs were employed to predict the promoter regions of  −687_ + 97 and thENCODE data showed a high level of DNase I hypersensitivity, promoter associated histone modifications and transcription factor occupancy patterns at − 124 and + 1 bp promoter regions. While no Nuclease hypersensitivity around − 328 bp region. However, no CpG islands were detected in the predicted promoter regions  (99.2%), rhesus monkey (Macaca mulatta - rheMac2) (93.4%), domesticated dog (Canis lupus familiaris - canFam2) (73%), cow (Bos Taurus - bosTau3) (71.1%), rat (Rattus norvegicus - rn4) (70.5%) and mouse (Mus musculus - mm9) (69.3%). But the region was not conserved in opossum (Monodelphis domestica), chicken , frog (Xenopus laevis), zebrafish (Danio rerio), fugu pufferfish (Takifugu rubripes), and spotted green pufferfish (Tetraodon nigrovoridis), see Fig. The ECR Browser revealed mammalian conservation for the [− 368_ + 10] region in chimpanzee  which were verified experimentally. Also, it found novel regulatory elements by computational comparison but without experimental verification on functionality. These elements were found by using similarity to known ones in a library of CE models . The sumIL1B-31 C/T polymorphism . The allelic and genotype distributions of IL1B-31 C/T polymorphism followed those expected in Hardy-Weinberg equilibrium (HWE) for control population (P = 0.1366)  and interestingly, two of them (− 338 and − 155) were located at in silico-predicted promoter regions. Thus, these mutations might play a role in regulating the expression of IL1B. In this study, the computational analysis predicted three promoter regions at − 328, − 124 and + 1, but two of them (− 328 and − 124) were only predicted by the NNPP algorithm that uses neural networks (NNs). NNs have been applied for promoter prediction since 1991  were applied in two steps: 1) in silico prediction of the promoter region and 2) in silico analysis of the predicted promoter region [−368_ + 10]. Furthermore, genotyping of IL1B-31 C > T polymorphism was performed using PCR-CTPP in 122 participants to study its association with the susceptibility to H. pylori infection in the Sudanese population. The methodology followed in this study is described in Fig. In this study, genomic DNA Sanger sequencing was used to detect SNPs in the region −687_ + 297] of 87_ + 297This study was carried out at public and private hospitals in Khartoum state. The hospitals included Ibin Sina specialized hospital, Soba teaching hospital, Modern Medical Centre and Al Faisal Specialized Hospital. Sample size was calculated using Epi Info software version 7 , 66. TheThe 122 participants had been referred for endoscopy. Out of that, 15 had gastric cancer, 27 had peptic ulceration, 61 had gastroduodenal inflammation, 10 had esophageal diseases, while nine showed normal upper gastroduodenoscopic features. The diagnosis of gastroduodenal diseases had been made by an experienced gastroenterologist during the upper gastrointestinal (GI) endoscopy procedure. While gastric cancer was diagnosed based on histology. Participants’ demographic and clinical data were obtained by a structured questionnaire, personal interviews, and a review of case records. The selection criteria included the Sudanese population from both sexes, no antibiotic or non-steroidal anti-inflammatory drugs (NSAIDs) uses. All the participants were informed with the objectives and purposes of the study and the written informed consents were taken. The demographic characteristics of participants is presented in Table Gastric biopsies were collected in 400 μl phosphate buffer saline (PBS). For histological examination, the biopsies were transported in formalin. DNA extraction was carried out by using innuPREP DNA Mini Kit  according to the protocol given by the manufacturer, as previously described in .16S rRNA gene of H. pylori was amplified by using the following primers (primers: F:5′-GCGCAATCAGCGTCAGGTAATG-3′) (R:5′-GCTAAGAGAGCAGCCTATGTCC-3′) [The specific GTCC-3′) . The PCRGTCC-3′) .IL-1B-511 and − 31, promoter polymorphisms, were amplified using the following primers: F:5′- CATCCATGAGATTGGCTAG-3′ and R:5′- AGCACCTAGTTGTAAGGAAG-3′ [IL-1B gene.The GGAAG-3′ . The cycH. pylori-infected subjects, which have the clearest bands, were sent for DNA purification and Sanger dideoxy sequencing. Both DNA strands were sequenced commercially by Macrogen Inc., Korea.Out of 14 PCR products of https://blast.ncbi.nlm.nih.gov/) was used to assess nucleotide sequence similarities [The sequencing results, two chromatograms for each patient (forward and reverse), were visualized, checked for quality, and analyzed using the Finch TV program version 1.4.0 . The nuclarities .IL-1B promoter region, multiple sequence alignment (MSA) for tested sequences with a reference sequence (NG_008851) were performed by using BioEdit software [To determine the SNPs in the software .http://www.cbs.dtu.dk/) which takes advantage of a combination of elements similar to neural networks and genetic algorithms to recognize a set of discrete sub-patterns with variable separation as one pattern: a promoter [http://www.fruitfly.org/) which applying multiple hidden layers and time-delay neural networks (TDNNs) for promoter annotation [http://softberry.com/) that uses functional motifs from the Wingender et al. database [http://softberry.com/) program that uses the same approach of TSSW but the TFD database of functional motifs [http://softberry.com/) which is TSSG variant with different learning set of promoter sequences [The crucial element for initiating and regulating messenger RNA transcription is the promoter sequence which is generally located in the 5′ upstream region of a structural gene . Promotepromoter ; (2) Neunotation ; (3) TSSdatabase  and linedatabase ; (4) TSSl motifs ; (5) Fprequences .https://epd.epfl.ch/cgi-bin/get_doc?db=hgEpdNew&format=genome&entry=IL1B_1) [In silico predicted promoter region was additionally assessed for the presence of promoter associated features, including promoter-associated histone marks, broad chromatin state segmentation, transcription factor ChIP-seq, and DNase I hypersensitivity clusters, using the ENCODE data (=IL1B_1) –47.http://www.softberry.com/berry.phtml?topic=cpgfinder&group=programs&subgroup=promoter) were employed to predict CpG islands in the promoter. CpG finder is intended to search for CpG islands in sequences, while MethPrimer is developed to design PCR primers for methylation mapping and primers are picked around the predicted CpG islands. CpG islands are predicted by using a simple sliding window algorithm to examine the GC content and the ratio observed/expected (Obs/Exp) across the sequence. The search parameter values for the software were CpG island length > 200 bp, CG% > 50%, and Obs/Exp > 0.6.A CpG island is often regarded as a marker for the initiation of gene expression. It is a segment of DNA with high GC and CpG dinucleotide contents which is located in the 5′ UTR (untranslated regions) of genes. In this study, MethPrimer , 80 and http://alggen.lsi.upc.es/cgi-bin/promo_v3/) in which positional weight matrices (PWM) are constructed from known binding sites extracted from TRANSFAC [http://www.gene-regulation.com/) which works based on the assumption that each binding site has an unknown context that determines its sequence and this leads to a construct of specific matrices for each sequence we are analyzing. And to do so a context-specific process starting at a dataset of known binding sites and ending with the identification of a potential new binding site [http://GPMiner.mbc.nctu.edu.tw/) which is an integrated system that identifies promoter regions, regulatory elements and DNA stability by incorporating the support vector machine (SVM) with nucleotide composition features, over-represented hexamer nucleotides, and DNA stability. For predicting TFBSs, MATCH tool [http://tfbind.hgc.jp/) which uses positional weight matrices (PWMs) and Bucher’s calculating method [http://www.ifti.org) which is an object-oriented transcription factors database (ooTFD)-retrieval tool that is used for transcription factors sites analyses. It constructs an image-map in association with sequence analysis results which is linked to individual sites entries [One of the important steps in the chain of promoter analytical events is the prediction of the potentially functional TFBSs. Protein binding sites in a promoter represent the most important elements and the corresponding proteins are called transcription factors (TFs). In this step, the promoter region was analyzed for possible TFBSs using five prediction software. (1) Alggen Promo ing site . (3) GenTCH tool  was utilTCH tool  and JASPTCH tool , 87. (4)g method  to calcug method . (5) Tfs entries .http://gnaweb.helmholtz-hzi.de/cgi-bin/MCatch/MatrixCatch.pl). The basic idea of MatrixCatch is to recruit data collected for respective binding sites separately from each other in order to complement the lack of knowledge on sequence variation of each DNA BS in CEs, and such information is compiled in position weight matrices (PWMs). The CE model consists of two PWMs, as well as their minimal scores, relative orientation and distance. Moreover, MatrixCatch is supplied with a library of 265 matrix models used for recognition which represents the widest scope of known CEs available to date [CE is the minimal functional unit, which can provide combinatorial transcriptional regulation of gene expression. Structurally, a CE consists of two closely located DNA binding sites (BSs) for distinct transcription factors. But its regulatory function is qualitatively different from regulation effects of either individual DNA binding sites. In this study, we identified the composite regulatory elements in our region by using MatrixCatch algorithm ( to date .http://ecrbrowser.dcode.org) [http://blast.ncbi.nlm.nih.gov/Blast.cgi) and ClustalW . Conservation was assessed in 11 species: chimpanzee (Pan troglodytes), rhesus monkey (Macacamulatta), mouse (Mus musculus), rat (Rattusnorvegicus), dog (Canisfamiliaris), cow (Bostaurus), opossum (Monodelphisdomestica), chicken , frog (Xenopuslaevis), zebrafish (Danio rerio), fugu pufferfish (Takifugurubripes), and spotted green pufferfish (Tetraodon nigrovoridis).Promoter region was analyzed for possible conservation using the ECR Browser (ode.org) , NCBI BLhttps://mulan.dcode.org/) [Also, conservation of SNPs was evaluated and the possible conservation of TFBSs at these SNP locations was screened with Multiple-sequence local alignment and visualization (Mulan) search engine (de.org/) .IL-1B-31 polymorphism, PCR-CTPP was applied. The primers for the C allele were (F:5′-ACT TCT GCT TTT GAA GGC C-3′) and (R:5′-TAG CAC CTA GTT GTA AGG A-3′); and those for the T allele were (F:5′-AGA AGC TTC CAC CAA TAC T-3′) and (R:5′-CTC CCT CGC TGT TTT TAT A-3′) [i-Taq) , 23 μl of de-ionized sterile water, 0.25 μl of each primer. PCR conditions were as follow: 5 min of initial denaturation at 94 °C, followed by 25 cycles of 1 min at 94 °C, 1 min at 54 °C, and 1 min at 72 °C, and a 5 min final incubation at 72 °C. The PCR products were visualized by electrophoresis on a 2% agarose gel stained with ethidium bromide. Genotyping was performed as follows; 240, 155 bp for CC genotype, 240, 155, 122 bp for CT genotype, and 240, 122 bp for TT genotype [For detection of the AT A-3′) . One μl genotype .χ2 test. According to prevalence of H. pylori infection, differences in distribution by age were assessed by Mann-Whitney test, while differences in distribution by categorical variables were examined by χ2 test or Fisher’s test. Odds ratios (ORs) were calculated and reported within the 95% confidence intervals (CIs). P < 0.05 was considered to be statistically significant. The statistical analyses were performed using the GraphPad Prism 5.Deviations from Hardy-Weinberg equilibrium in control were examined by Additional file 1. The structured questionnaire.Additional file 2. Original, full-length gel and blot images.


Enhancer RNAs (eRNA) are unstable non-coding RNAs, transcribed bidirectionally from active regulatory sequences, whose expression levels correlate with enhancer activity. We use capped-nascent-RNA sequencing to efficiently capture bidirectional transcription initiation across several human lymphoblastoid cell lines (Yoruba population) and detect ~75,000 eRNA transcription sites with high sensitivity and specificity. The use of nascent-RNA sequencing sidesteps the confounding effect of eRNA instability. We identify quantitative trait loci (QTLs) associated with the level and directionality of eRNA expression. High-resolution analyses of these two types of QTLs reveal distinct positions of enrichment at the central transcription factor (TF) binding regions and at the flanking eRNA initiation regions, both of which are associated with mRNA expression QTLs. These two regions—the central TF-binding footprint and the eRNA initiation cores—define a bipartite architecture of enhancers, inform enhancer function, and can be used as an indicator of the significance of non-coding regulatory variants. Enhancer RNAs are transcribed bidirectionally from core transcription initiation regions. Here, by employing nascent RNA sequencing, the authors identify quantitative trait loci (QTLs) associated with enhancer RNA level and directionality, revealing the bipartite architecture of enhancers. Therefore, considerable efforts have been made to connect genetic variations to molecular phenotypes at TREs, and to understand how those might affect gene expression11.Regulation of transcription is achieved mainly through the binding of transcription factors (TFs) at transcription regulatory elements (TREs), such as promoters and enhancers. Genes are expressed from promoters, which integrate regulatory signals from proximal and distal enhancers to determine the amount of RNA product. Such regulatory networks are key to most cellular processes, including development, cell-type differentiation, and stress response. Their misregulation can often cause diseases, and disease-associated genetic variation is enriched in TREs16, and its levels track with enhancer activity17. A pair of enhancer RNAs (eRNAs) are typically transcribed in opposite directions from core transcription-initiation regions flanking the central transcription-factor-binding site (TFBS) of the enhancer18. While the production of eRNAs has been used to identify active enhancers across numerous cell types and tissues15, the precise roles of eRNAs in gene regulation have not yet been elucidated19. Complicating eRNA detection and quantification is the fact that they are rapidly degraded15. This issue makes the use of methods that rely on steady-state RNA, such as RNA-seq or Cap Analysis of Gene Expression (CAGE), less suitable. Nascent-RNA-sequencing methods, such as Precision nuclear Run-On sequencing with 5′-capped (m7G) RNA enrichment21 (PRO-cap), overcome this challenge by capturing capped RNA at the synthesis stage.Enhancer transcription arises in addition to the target promoter activation in diverse species, including flies and humans25. Similarly, identifying associations between genotypes and enhancer transcription will help connect transcriptional changes at enhancers to changes in gene expression, revealing potential mechanisms for gene regulation. Recent studies have mapped genetic variation to transcription at promoters and enhancers using CAGE, revealing quantitative trait loci (QTLs) associated with alternative promoter usage, promoter shape, and expression11. However, the statistical power to comprehensively profile enhancer-associated QTLs using CAGE was relatively limited compared to promoter-associated QTLs due to the rapid degradation of eRNAs. Given the properties of PRO-cap that allow it to detect and quantify the transcription of unstable RNAs, we anticipate that a much more comprehensive list of enhancer-associated QTLs can be identified.The mapping of genetic variation to molecular phenotypes at different stages of gene expression has provided important insights into the DNA sequences underlying gene regulationThis study leverages the variation in transcription initiation at enhancers, measured by PRO-cap, in lymphoblastoid cell lines (LCLs) from 67 individuals. We find thousands of genetic variants that affect either transcription initiation levels (tiQTLs) or the directionality of initiation (diQTLs) at enhancers. We find that these two types of QTLs are enriched at distinct positions within the enhancer architecture. Importantly, both variant types show significant association with mRNA expression, illustrating their potential functionality in gene regulation. Overall, our genetic analyses, investigating the pattern of enhancer transcription, suggest a bipartite architecture of enhancers.26. We combined all PRO-cap datasets and identified transcribed transcriptional regulatory elements (tTREs), including both enhancers and promoters, with bidirectional divergent nascent transcription within 300 base pairs (bp) of one another   and their proximity to annotated (Refseq) gene transcription start sites (TSS). Based on the CAGE data, 12,878 tTREs were identified as promoters, and the remaining 74,948 tTREs were identified as enhancers; similar numbers were obtained using Refseq TSSs , chromatin accessibility QTLs5 , and disease associated variants (GWAS SNPs)32 32 Fig. . Interes)32 Fig. . TogetheTo investigate how underlying sequences establish the transcriptional signature at tTREs, we tested the association between genetic variation across individuals and the pattern of transcription at tTREs. To avoid confounding effects caused by differences in the read mappability of different alleles, we devised a variant sensitive alignment method that masks out allele-mappability biased regions . Around 90% of reads overlapping tiQTLs were correctly remapped to the genome and 87% of tiQTLs on chromosome 22 were reidentified by WASP, verifying that most of our tiQTLs are not the result of allele-biased mapping in our method , resulting in a reduction to 22,949 tiQTLs. We then examined the tiQTL density around tTRE midpoints . Similarly, tiQTL tTREs are also enriched to have diQTLs .SNPs identified as diQTLs and those identified as tiQTLs show significant overlap in terms of the tTREs they are located within. 52.1% of tTREs associated with a diQTL also contain an associated tiQTL, comprising 6.4% of tTREs with tiQTLs. Focusing only on primary diQTL SNPs, defined as described for tiQTLs above, 4,479 primary diQTLs remain within tTREs. Of these filtered diQTL-associated tTREs, 43.6% are also associated with a tiQTL; this constitutes a 3.76-fold enrichment of tiQTLs compared to a background set of tTREs , coinciding with decreased transcription from the affected strand  and the TATA-like elements. For example, the non-reference allele of diQTL and Fig. . More gl 38 Fig. .22 to define expression QTLs (eQTLs) using only SNPs located within 2 kb of a tTRE, to match the set of SNPs used to identify ti- and diQTLs. We found 744 genes with associated eQTLs within 200 kb, with a total of 5,997 eQTLs (FDR < 0.05). Overall, these eQTLs are enriched within 200 bp from the enhancer midpoints defined by PRO-cap  given the smaller number of eQTLs   Fig. . EnhanceLs) Fig. . HoweverP < 0.05, Fig. P < 10−16). These support our hypothesis that the core eRNA initiation regions are indeed important for target gene expression, in addition to the central transcription factor binding regions.To further explore the model that both the center and TSS cores are important for enhancer function, we hypothesized that eQTLs would be enriched in those regions relative to the regions outside them. We separated enhancer regions into: the center, the core promoter-like region from which transcription arises (core), the space between them , the space outside of the core but within the enhancer region (out), and those beyond the enhancer region (far out) Fig. . Using tWe explored the activity and architecture of transcribed transcriptional regulatory elements (tTREs) by studying the variation in transcription initiation across human LCLs. We identified genetic variants associated with enhancer and promoter transcriptional activity and directionality. The pattern of enrichment for these genetic variants and the types of motifs they affect suggest that overall transcriptional activity at enhancers is regulated from the central transcription-factor binding region (TFBS) and directionality is regulated from the surrounding core initiation regions. Both variant types are associated with gene expression at a higher rate than expected by chance, indicating that both the central TFBS and the flanking core initiation regions affect transcription at enhancers and their role in gene expression. This conclusion is supported by regional enrichment of eQTLs within enhancers.15. Other transcription-based approaches, such as CAGE and nuclear short RNA analysis, are impeded by this instability. As we showed, CAGE performs well in identifying promoters but detects enhancers less efficiently than PRO-cap. Additionally, focusing on the bidirectional nascent transcription start sites (nTSSs) using PRO-cap enabled us to filter out spurious transcription from only one strand, which increases the specificity. Although it is possible that enhancer transcription can be unidirectional, our nascent transcription analysis detected mostly bidirectional and divergently paired PRO-cap peaks, and observed very few unpaired peaks.Identification of enhancers based on capped nascent RNA sequencing provides a direct measure of transcriptional activity and, therefore, shows higher sensitivity than previous methods. A direct measure of transcriptional activity is critical, as non-productive transcripts such as eRNAs are rapidly degraded in the nucleusThe fact that genetic variants affecting enhancer directionality are associated with changes in gene expression brings up an interesting question of its mechanism. While the change in directionality towards or away from the sense strand of the gene will obviously affect its expression levels at promoters, how directional initiation at enhancers affects gene expression is less clear. In some cases, modulating the polymerase initiation at only one of the two initiating sites could impact the overall enhancer activity, though in other cases overall transcriptional activity is not affected by the diQTLs. In the latter case, it is possible that, despite the prevailing model that enhancers are orientation-independent, there may be distance and orientation-specific effects on their target genes, potentially involving eRNAs. While our current list of diQTLs is limited due to the sample size, increasing power with a larger scale analysis would allow testing of these possibilities. Additionally, dissecting the putatively directional enhancers identified in this paper, either in reporter assays or through genome editing with CRISPR, and measuring effects on the expression of associated genes could provide important insights into the role of directionality and eRNAs in enhancer function.Overall, our data show the power of combining capped-nascent-RNA sequencing with human population genetics to explore the architecture of human enhancers. These results suggest a bipartite model for enhancers, where sequences at both the central TFBS and the core promoter regions surrounding the TSSs are important for enhancer function. Our model also supports the hypothesis that eRNA transcription itself can be functional. In practice, this will have important implications for assessing the significance of disease-associated non-coding genetic variants by adding eRNA start sites to the regions of interest..5. . One individual was dropped out due to unsuccessful PRO-cap library generation and two because they lacked phased genotype data  and centrifugation at 800 × g for 3 min, followed by resuspension in 50 µl of storage buffer . Cells were instantly frozen in liquid nitrogen, and stored at −80 °C. Upon thawing, cells were incubated in polymerase run-on reactions with ribonucleotide triphosphate (NTP) substrates at a final concentration of 19 µM biotin-11-CTP, 19 µM biotin-11-UTP, 0.19 mM ATP, and 0.19 mM GTP at 37 °C for 3 min. This was followed by nascent-RNA sequencing library preparation39. Briefly, biotin-labeled nascent RNA is extracted by Trizol, fragmented through base hydrolysis under 0.1 N sodium hydroxide in 4 °C for 12 min, neutralized by adding Tris–Cl pH 8.0, and passed through P-30 size exclusion column (BioRad). The RNA is bound to streptavidin-magnetic beads (ThermoFisher), washed 3-5 times, and extracted using Trizol. The purified biotin-labeled RNA is processed through 3′ RNA adaptor (pUGGAAUUCUCGGGUGCCAAGG-/inverted dT/) ligation, 2′nd streptavidin purification, 5′ RNA decapping, 5′ RNA adaptor (GUUCAGAGUUCUACAGUCCGACGAUCNNNNNNNN) ligation, 3’rd streptavidin purification, and reverse transcription (primer sequence: GCCTTGGCACCCGAGAATTCCA). The adaptor sequences are compatible with the Illumina small RNA sequencing platform. cDNA is amplified through 15–20 cycles of PCR amplification using Illumina TRU-seq single barcode multiplexing primers. The PCR product is PAGE purified and subject to high throughput sequencing with the Illumina NextSeq 500.Human lymphoblastoid cell lines (LCLs) were acquired from the Coriell Biorepository. Unrelated individuals were selected from the Yoruba (YRI) population matching those in a study by Degner et al39 and combined alignments for each method  unique read molecules mapped to the hg19 genome. We then used a bidirectional read-count filtering approach comparable to capped-RNA analysis described elsewhere to identify bidirectional transcribed tTREs22 , and ENCODE FACTORBOOK defined EP300 binding sites in GM12878 cell line as the enhancer standards . We used the randomly selected 1 million genomic regions in 500 bp windows as described in the previous section as a background distribution. We calculated the true positive rate as a function of different RPKM thresholds using TSS or EP300 standards, and calculated the false positive rate as the fraction of background regions above the thresholds. We generated receiver operating characteristic (ROC) curves for PRO-cap detecting TSS and EP300 sites, and compared the PRO-cap ROC with an available cap analysis of gene expression (CAGE) data in GM12878 (RIKEN poly-A-selected nuclear and cytoplasmic CAGE) and available NET-CAGE data in GM12878 . We then defined tTREs greater than 2 kb away from any refseq TSSs as distal enhancers . For the CAGE based promoter and enhancer classifications, PRO-cap and CAGE counts at the nTSSs are collected for the plus and the minus strands separately, and RPM normalized. nTSSs with CAGE counts significantly above the background were called promoter TSSs. To estimate the background CAGE read counts, we first selected nTSSs that are at least 1 kb away from any annotated refseq TSS , and calculated the CAGE read counts for both the plus and the minus strands at these background regions. We calculated the p-values of the nTSS CAGE read counts based on the empirical background distribution, and found 13,833 nTSSs that have CAGE readcounts that are significantly higher than the background using FDR < 0.1 . Of the 13,833 nTSSs, 995 were bidirectional pairs, yielding 12,878 tTREs as promoters, 995 of which are bidirectional. We defined the remaining 74,948 tTREs as enhancers.To classify the tTREs into gene promoters and enhancers/regulatory elements, we used two criteria: (1) distance to annotated refseq TSSs, (2) overlap with CAGE sites. For the refseq based promoter annotations, we defined the tTREs with at least one nTSSs within 500 bp of refseq TSSs as the promoters , which includes LCLs, and calculated the p-value of differential LCL expression compared to the remaining 53 cell types for each gene. We compared the -log10 p-value distributions of all CISBP TFs (background) against TRE enriched CISBP TFs.We used the RTFBSdb suite22 and DNase I hypersensitivity QTLs5 in Yoruba LCLs and lifted the coordinates over to the hg19 genome . For the disease associated GWAS SNPs, we used the NIH GWAS Catalog32 entries as of March, 2015 in hg19 , using all GWAS SNPS as well as SNPs filtered for B-cell related effects  in all LCL cell lines. . See supplement for details.In summary, we reconstructed the individual phased haplotype genomes, masked out any tTRE regions with ambiguous mappability, then re-aligned the PRO-cap reads to the individual haplotype genomes , and variant sites with a minor allele frequency greater than 5% in our haplotype-phased individuals . A total of 994,993 pairs were tested. We tested up to 20 principal components (PCs) as co-variates in 2 kb cis-tiQTL tests and found that 16 PCs gave the largest number of significant tiQTLs (FDR > 0.1). In total 16,193 tTREs are associated with at least one tiQTL within a 2 kb cis region.We tested the association between the nascent transcription initiation phenotypes at each tTRE region and the genotype of SNPs and short indels within a cis range of 2 kb from the midpoint of the nTSS regions. We took the variant sensitive normalized PRO-cap readcounts, and identified tiQTLs using the MatrixEQTL R package2 transformed the ratio and quantile normalized the resulting index using the same method we used for diQTLs. The association of these directionality phenotypes with genotype was assessed, as for tiQTLs, using MatrixEQTL. We determined that using 8 principal components as covariates in association testing gave the largest number of significant associations.We tested the association between the relative direction of the divergent bidirectional nascent transcription initiation pairs at the two nTSSs in each tTRE, and the genotypes of the genetics variants. To compute a metric for the directionality of tTREs, we calculated the directionality index as ratio of plus-strand (1–250) and minus-strand (−250 - 0) read counts, logP-value, independent of which tTRE or gene it affects. If there were two or more SNPs with the lowest P-value, none were kept.We split the genome into 5 kb windows, staggered by 1 kb so that each SNP is in 5 different 5 kb windows. In each window we keep only the SNP with the lowest 5. We limited our analysis to the tiQTLs at the center-region (midpoint ±40 bp) of tTREs and imposed a stricter filtering requirement by keeping only those QTLs where the most significantly associated SNP has a P-value an order of magnitude lower than the next most significant . Reference and alternative alleles were categorized into enhancing and repressive alleles based on the relative PRO-cap readcounts around the tTREs. We used CIS-BP human transcription factor frequency matrices to generate position weight matrices (PWMs), and queried a 40 bp region surrounding each tiQTL for strong matches to the PWMs (motif score > 13). We repeated this analysis for both alleles and compared the resulting motif scores for enhancing and repressive alleles. We then took these motif score differences in these strong-matched TF motifs to obtain the overall effect of each variation on TF binding potential. For comparison, we also performed this analysis on dsQTLs5 that fall within the most highly affected window (100 bp windows) (n = 967) and dsQTLs outside the affected windows .We tested the effect of tiQTLs on TF binding likelihood as described by Degner et al.45 to calculate the motif disruption score for each tiQTL for the 400 motifs selected from the CIS-BP motif database with RTFBSdb. A motif is considered disrupted by the tiQTL if there is a significant match to a motif in at least one allele (p < 0.01) and there is at least a 0.5 bit difference in motif matching scores between the alleles. We then count the number of times each motif is disrupted by a tiQTL and compare it to the number of times that same motif is disrupted by randomly selected SNPs within the same region (±200 bp from tTRE midpoint).We used motifbreakR37 by taking the natural exponent of the PWM scores. First, we selected diQTL SNPs within 5 base pairs (bp) from the defined nascent transcription start sites (nTSSs) of tTREs (n = 209). We calculated the strand specific Inr likelihood difference between the two alleles and included diQTLs that generate differences in Inr likelihood score (>5). We oriented the diQTL directionality effect sizes towards the strand in which the Inr scores are calculated, and assigned the Inr likelihood difference as . For comparison, we selected tiQTL SNPs within 5 bp from the center of tTREs (n = 520) and dsQTLs at the nTSS (n = 85) and at center (n = 71), calculated Inr likelihood differences in between relatively higher directional allele to the lower directional allele towards the direction of the Inr element, and plotted the distribution of ΔInr likelihood similar to diQTLs.Initiator (Inr) element likelihood is calculated using published human Inr frequency matrixn = 162) as well as tiQTLs at center (n = 520) as described above as well as dsQTLs in this area (n = 108) and at center (n = 71). We oriented the diQTLs as explained for Inr and calculated a delta TATA score . A perfect TATA got a score of 4, a single inversion  such as TTTA or TAAA got a score of 2, and two inversions got a score of 1. Anything else scored 0.We focused on the area 40–20 bp upstream of tTRE TSSs and looked for instances of TATA, or inversions thereof, at diQTLs , we tested the association between SNPs and gene expression. By limiting our analysis only to the region we are interested in, we increase our power to detect significant associations. We used RNA-seq data from Pickrell et al.22, merged replicates by taking an average across replicates for each gene, and performed the same quantile normalization as above. We used matrixEQTL to identify eQTLs within a cis-distance of 200 kb (FDR < 0.05). We determined that using 13 PCs as covariates gave the largest number of significant associations.Using only SNPs that fall within 2 kb of tTREs , DNase I hypersensitivity QTLs , and disease associated GWAS SNPs for all traits and B-cell related traits . For the transcription factor binding QTLs, we used published QTLs26 for JunD, NF-κB, Pou2f1, PU.1, and Stat1 . We overlapped these QTL lists with filtered diQTL and tiQTL lists , as well as background lists . Background SNPs were all SNPs within 2 kb of TRE centers. We then computed the percentage of diQTL, tiQTL, or background SNPs that overlapped and found the enrichment of diQTL and tiQTL overlap over background. We used two-sided Fisher’s exact tests on the overlap-count data to compute p values. diQTLs at the TSS (n = 206) were ±20 bp from the TSS of the TRE, and tiQTLs at the TRE center (n = 1230) were ±25 bp from the TRE center. Background SNPs for center and TSS were filtered similarly .We used previously mentioned lists of expression QTLs  with those for all SNPs within 2 kb of tTREs using QQ plots. We also split diQTLs into groups of diQTLs which are not tiQTLs (n = 4326), diQTLs±25 bp from the TRE center (n = 284), and diQTLs±25 bp from the nTSS of TREs (n = 326) and compared those to all SNPs. We then found the percentage of SNPs which pass an FDR threshold of 0.1 with a Benjamini–Hochberg correction and compared each group to all SNPs.We computed a Enhancers were split into regions according to Fig. Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2