Non-promoter Regions Research Articles

A Comprehensive Pan-Cancer Analysis Identified that TRIB3 was Associated with Immune Cell Infiltration and Poor Prognosis.

Previous studies have demonstrated that TRIB3 plays a carcinogenic role in tumor progression. However, the exploration of TRIB3 at the pan-cancer level has not been reported. This study aimed to conduct a comprehensive pan-cancer analysis of TRIB3. We explored the expression pattern and functional mechanism of TRIB3 on the basis of multiple databases. We first explored the expression level of TRIB3 in the TCGA database. Then, the receiver operation characteristic curve (ROC), Kaplan-Meier plotter, and Cox regression were used to estimate the diagnostic and prognostic value of TRIB3, respectively. We also explored the relationship between TRIB3 and the infiltration of tumor immune cells, as well as the expression of immune checkpoint molecules. Gene enrichment and protein interaction network analysis were carried out to identify possible carcinogenic molecular mechanisms and functional pathways. Finally, we compared the non-promoter region methylation of TRIB3 in normal and tumor tissues and explored potential systems with unique functions in TRIB3-mediated tumorigenesis. The expression level of TRIB3 was elevated in multiple tumor types, and the high expression of TRIB3 was associated with poor prognosis. TRIB3 had a higher frequency of genetic changes in several tumors and showed varying trends in TRIB3 methylation levels. Additionally, high expression of TRIB3 was also associated with infiltration of cancer-related fibroblasts and different types of immune cells and was positively correlated with the expression of immune checkpoint molecules. Furthermore, gene enrichment analysis suggested that TRIB3 may play a role in the malignant progression of cancer by participating in protein post-translational modifications and activating transcription initiation factors. Our pan-cancer analysis provided the potential carcinogenic role of TRIB3 in tumors and verified a promising target for clinical immune treatment.

Molecular Signatures of CB-6644 Inhibition of the RUVBL1/2 Complex in Multiple Myeloma.

Multiple myeloma is the second most hematological cancer. RUVBL1 and RUVBL2 form a subcomplex of many chromatin remodeling complexes implicated in cancer progression. As an inhibitor specific to the RUVBL1/2 complex, CB-6644 exhibits remarkable anti-tumor activity in xenograft models of Burkitt's lymphoma and multiple myeloma (MM). In this work, we defined transcriptional signatures corresponding to CB-6644 treatment in MM cells and determined underlying epigenetic changes in terms of chromatin accessibility. CB-6644 upregulated biological processes related to interferon response and downregulated those linked to cell proliferation in MM cells. Transcriptional regulator inference identified E2Fs as regulators for downregulated genes and MED1 and MYC as regulators for upregulated genes. CB-6644-induced changes in chromatin accessibility occurred mostly in non-promoter regions. Footprinting analysis identified transcription factors implied in modulating chromatin accessibility in response to CB-6644 treatment, including ATF4/CEBP and IRF4. Lastly, integrative analysis of transcription responses to various chemical compounds of the molecular signature genes from public gene expression data identified CB-5083, a p97 inhibitor, as a synergistic candidate with CB-6644 in MM cells, but experimental validation refuted this hypothesis.

Reversal of high-glucose-induced transcriptional and epigenetic memories through NRF2 pathway activation.

Diabetes complications such as nephropathy, retinopathy, or cardiovascular disease arise from vascular dysfunction. In this context, it has been observed that past hyperglycemic events can induce long-lasting alterations, a phenomenon termed "metabolic memory." In this study, we evaluated the genome-wide gene expression and chromatin accessibility alterations caused by transient high-glucose exposure in human endothelial cells (ECs) in vitro. We found that cells exposed to high glucose exhibited substantial gene expression changes in pathways known to be impaired in diabetes, many of which persist after glucose normalization. Chromatin accessibility analysis also revealed that transient hyperglycemia induces persistent alterations, mainly in non-promoter regions identified as enhancers with neighboring genes showing lasting alterations. Notably, activation of the NRF2 pathway through NRF2 overexpression or supplementation with the plant-derived compound sulforaphane, effectively reverses the glucose-induced transcriptional and chromatin accessibility memories in ECs. These findings underscore the enduring impact of transient hyperglycemia on ECs' transcriptomic and chromatin accessibility profiles, emphasizing the potential utility of pharmacological NRF2 pathway activation in mitigating and reversing the high-glucose-induced transcriptional and epigenetic alterations.

IProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking

Background and Objective: Gene promoters play a crucial role in regulating gene transcription by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches, including alignment signal and content-based methods for promoter prediction, accurately identifying promoters remains challenging due to the lack of explicit features in their sequences. Consequently, many machine learning and deep learning models for promoter identification have been presented, but the performance of these tools is not precise. Most recent investigations have concentrated on identifying sigma or plant promoters. While the accurate identification of Saccharomyces cerevisiae promoters remains an underexplored area. In this study, we introduced “iPromyeast”, a method for identifying yeast promoters. Using genome sequences from the eukaryotic yeast Saccharomyces cerevisiae, we investigate vector encoding and promoter classification. Additionally, we developed a more difficult negative set by employing promoter sequences rather than nonpromoter regions of the genome. The newly developed negative reconstruction approach improves classification and minimizes the amount of false positive predictions. Methods: To overcome the problems associated with promoter prediction, we investigate alternate vector encoding and feature extraction methodologies. Following that, these strategies are coupled with several machine learning algorithms and a 1-D convolutional neural network model. Our results show that the pseudo-dinucleotide composition is preferable for feature encoding and that the machine- learning stacking approach is excellent for accurate promoter categorization. Furthermore, we provide a negative reconstruction method that uses promoter sequences rather than non-promoter regions, resulting in higher classification performance and fewer false positive predictions. Results: Based on the results of 5-fold cross-validation, the proposed predictor, iProm-Yeast, has a good potential for detecting Saccharomyces cerevisiae promoters. The accuracy (Acc) was 86.27%, the sensitivity (Sn) was 82.29%, the specificity (Sp) was 89.47%, the Matthews correlation coefficient (MCC) was 0.72, and the area under the receiver operating characteristic curve (AUROC) was 0.98. We also performed a cross-species analysis to determine the generalizability of iProm-Yeast across other species. Conclusion: iProm-Yeast is a robust method for accurately identifying Saccharomyces cerevisiae promoters. With advanced vector encoding techniques and a negative reconstruction approach, it achieves improved classification accuracy and reduces false positive predictions. In addition, it offers researchers a reliable and precise webserver to study gene regulation in diverse organisms.

The Transcriptional Landscape of Ph+B-ALL Is Orchestrated By Long-Range Enhancer-Promoter Interactions and the Coordinated Action of Phosphorylation-Dependent and Phosphorylation-Independent Transcription Factors

Background and Hypothesis: B-cell precursor leukaemia (B-ALL) subtypes can be distinguished by their initiating genetic lesions. Likewise, each subtype exhibits a unique transcriptional program that drives the malignancy and defines the subtype. This often relates to the initiating genetic alteration that affects genes encoding transcription factors (TFs). However, for B-ALLs not driven by TF alterations it is less clear how the transcriptional program is established. Ph+B-ALL is a historically poor prognosis B-ALL driven by the BCR::ABL1 oncogene, a constitutive tyrosine kinase that activates its targets via phosphorylation. We hypothesised that BCR::ABL1 initiates the transcriptional signature of Ph+B-ALL cells by a two-step mechanism involving phosphorylation-activatable TFs, which then induce the expression of additional TFs that consecutively establish the Ph+B-ALL-defining transcriptome. As gene expression is often modulated by cis-regulatory elements such as enhancers, we further speculated that BCR::ABL1 reprograms the enhancer landscape to promote the transcriptional changes that define Ph+B-ALL. Results: Using an in vitro model of BCR::ABL1-induced transformation of murine B-cell precursors, we show that transcriptional deregulation by BCR::ABL1 is a continuous process evolving until full transformation is established. Only a minority of induced transcriptional changes occur immediately upon BCR::ABL1 expression (3 days), while most affected genes require an extended period of BCR::ABL1 to become deregulated. Furthermore, less than half of deregulated genes in murine Ph+B-ALL and less than 10% of human Ph+B-ALL-defining genes were sensitive to 24h BCR::ABL1 inhibition, suggesting an involvement of phosphorylation-dependent and phosphorylation-independent TFs. TF motif enrichment analysis of active chromatin in human and mouse Ph+B-ALL cells indicated the phosphorylation-dependent TF STAT5 and the phosphorylation-independent TF ETV5 as main mediators. Degron-induced rapid degradation of each TF in Ph+B-ALL cells confirmed their unique and overlapping roles and coordinated action. In parallel to gene expression changes, Ph+B-ALL cells further acquire changes in their enhancer signatures defined by H3K27ac+ at non-promoter regions, and these enhancer signatures can be efficiently used to distinguish Ph+B-ALL from other B-ALL subtypes. Analysis of chromatin interactions of these active enhancers with respective active promoters by Promoter-Capture Hi-C (PCHI-C) in combination with H3K27ac-ChIP-Seq showed that most active genes in Ph+B-ALL cells physically interact with an enhancer, including many genes described to be essential for Ph+B-ALL cells, such as CCND2, BCL2, BCL2L1, and ETV5. Using PCHI-C and H3K27ac-HiChIP we further show that, like gene expression and enhancer signatures, physical contacts of promoters and enhancers can be efficiently used to identify Ph+B-ALL cells and distinguish them from other B-ALLs such as KMT2A-rearranged B-ALL. Subtype-specific chromatin interactions that allow this differentiation directly relate to the transcriptional programs that define the respective B-ALL subtype. Conclusion: We describe here the molecular details of how signals from an oncogenic tyrosine kinase become converted into changes in gene expression that define the B-lineage leukaemia subtype Ph+B-ALL. Our work highlights the complexity of these changes by acknowledging the role of gene regulatory elements/enhancers in this process and further describe for the first time that B-ALL subtypes can be distinguished by their enhancer usage and promoter-enhancer interactions. Uncovering the precise enhancer regions for the genes deregulated in Ph+B-ALL and the TFs recruited to them may open new windows for therapeutic intervention.

Targeting SWI/SNF ATPases in H3.3K27M diffuse intrinsic pontine gliomas

Diffuse midline gliomas (DMGs) including diffuse intrinsic pontine gliomas (DIPGs) bearing lysine-to-methionine mutations in histone H3 at lysine 27 (H3K27M) are lethal childhood brain cancers. These tumors harbor a global reduction in the transcriptional repressive mark H3K27me3 accompanied by an increase in the transcriptional activation mark H3K27ac. We postulated that H3K27M mutations, in addition to altering H3K27 modifications, reprogram the master chromatin remodeling switch/sucrose nonfermentable (SWI/SNF) complex. The SWI/SNF complex can exist in two main forms termed BAF and PBAF that play central roles in neurodevelopment and cancer. Moreover, BAF antagonizes PRC2, the main enzyme catalyzing H3K27me3. We demonstrate that H3K27M gliomas show increased protein levels of the SWI/SNF complex ATPase subunits SMARCA4 and SMARCA2, and the PBAF component PBRM1. Additionally, knockdown of mutant H3K27M lowered SMARCA4 protein levels. The proteolysis targeting chimera (PROTAC) AU-15330 that simultaneously targets SMARCA4, SMARCA2, and PBRM1 for degradation exhibits cytotoxicity in H3.3K27M but not H3 wild-type cells. AU-15330 lowered chromatin accessibility measured by ATAC-Seq at nonpromoter regions and reduced global H3K27ac levels. Integrated analysis of gene expression, proteomics, and chromatin accessibility in AU-15330-treated cells demonstrated reduction in the levels of FOXO1, a key member of the forkhead family of transcription factors. Moreover, genetic or pharmacologic targeting of FOXO1 resulted in cell death in H3K27M cells. Overall, our results suggest that H3K27M up-regulates SMARCA4 levels and combined targeting of SWI/SNF ATPases in H3.3K27M can serve as a potent therapeutic strategy for these deadly childhood brain tumors.

β-actin mediated H3K27ac changes demonstrate the link between compartment switching and enhancer-dependent transcriptional regulation

BackgroundRecent work has demonstrated that three-dimensional genome organization is directly affected by changes in the levels of nuclear cytoskeletal proteins such as β-actin. The mechanisms which translate changes in 3D genome structure into changes in transcription, however, are not fully understood. Here, we use a comprehensive genomic analysis of cells lacking nuclear β-actin to investigate the mechanistic links between compartment organization, enhancer activity, and gene expression.ResultsUsing HiC-Seq, ATAC-Seq, and RNA-Seq, we first demonstrate that transcriptional and chromatin accessibility changes observed upon β-actin loss are highly enriched in compartment-switching regions. Accessibility changes within compartment switching genes, however, are mainly observed in non-promoter regions which potentially represent distal regulatory elements. Our results also show that β-actin loss induces widespread accumulation of the enhancer-specific epigenetic mark H3K27ac. Using the ABC model of enhancer annotation, we then establish that these epigenetic changes have a direct impact on enhancer activity and underlie transcriptional changes observed upon compartment switching. A complementary analysis of fibroblasts undergoing reprogramming into pluripotent stem cells further confirms that this relationship between compartment switching and enhancer-dependent transcriptional change is not specific to β-actin knockout cells but represents a general mechanism linking compartment-level genome organization to gene expression.ConclusionsWe demonstrate that enhancer-dependent transcriptional regulation plays a crucial role in driving gene expression changes observed upon compartment-switching. Our results also reveal a novel function of nuclear β-actin in regulating enhancer function by influencing H3K27 acetylation levels.

Promoter prediction in nannochloropsis based on densely connected convolutional neural networks.

Promoter is a key DNA element located near the transcription start site, which regulates gene transcription by binding RNA polymerase. Thus, the identification of promoters is an important research field in synthetic biology. Nannochloropsis is an important unicellular industrial oleaginous microalgae, and at present, some studies have identified some promoters with specific functions by biological methods in Nannochloropsis, whereas few studies used computational methods. Here, we propose a method called DNPPro (DenseNet-Predict-Promoter) based on densely connected convolutional neural networks to predict the promoter of Nannochloropsis. First, we collected promoter sequences from six Nannochloropsis strains and removed 80% similarity using CD-HIT for each strain to yield a reliable set of positive datasets. Then, in order to construct a robust classifier, within-group scrambling method was used to generate negative dataset which overcomes the limitation of randomly selecting a non-promoter region from the same genome as a negative sample. Finally, we constructed a densely connected convolutional neural network, with the sequence one-hot encoding as the input. Compared with commonly used sequence processing methods, DNPPro can extract long sequence features to a greater extent. The cross-strain experiment on independent dataset verifies the generalization of our method. At the same time, T-SNE visualization analysis shows that our method can effectively distinguish promoters from non-promoters.

Mysteries of gene regulation: Promoters are not the sole triggers of gene expression

Cis-regulatory elements of promoters are essential for gene regulation by transcription factors (TFs). However, the regulatory roles of nonpromoter regions, TFs, and epigenetic marks remain poorly understood in plants. In this study, we characterized the cis-regulatory regions of 53 TFs and 19 histone marks in 328 chromatin immunoprecipitation (ChIP-seq) datasets from Arabidopsis. The genome-wide maps indicated that both promoters and regions around the transcription termination sites of protein-coding genes recruit the most TFs. The maps also revealed a diverse of histone combinations. The analysis suggested that exons play critical roles in the regulation of non-coding genes. Additionally, comparative analysis between heat-stress-responsive and nonresponsive genes indicated that the genes with distinct functions also exhibited substantial differences in cis-regulatory regions, histone regulation, and topologically associating domain (TAD) boundary organization. By integrating multiple high-throughput sequencing datasets, this study generated regulatory models for protein-coding genes, non-coding genes, and TAD boundaries to explain the complexity of transcriptional regulation.

Abstract 2427: Identification of transcription factors activated in glioblastoma multiforme

Abstract Introduction Glioblastoma multiforme (GBM) is the most common intrinsic brain tumor in adults and is universally fatal. Our strategy to study the molecular mechanisms leading to GBM is to investigate the role of transcription factors (TFs) that are differentially expressed between normal and GBM. Previous studies have shown that ZFX is a key TF that shows increased expression in GBM. However, the role of ZFX, and the identity of TFs that may work with ZFX, in driving GBM carcinogenesis is not yet understood. Materials and methods: ChIP-seq was performed to determine ZFX binding sites in the U87 GBM cancer cell line. RNA-seq datasets generated from GBM tissues by The Cancer Genome Atlas (TCGA) were analyzed to identify TFs showing increased expression in GBM. Publicly available H3K27ac chromatin immunoprecipitation (ChIP-seq) and Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) datasets generated from GBM tissues were analyzed to identify active enhancers and TFs that bind to GBM enhancers. Results: We found that a majority of ZFX binding sites in GBM are located at promoters, with ~1000 binding sites being at non-promoter regions. An overlap analysis of ZFX binding sites in multiple cancer cell types showed that a high percentage of ZFX-binding sites at promoter regions are shared across cell types whereas hundreds of GBM-specific binding sites are located at non-promoter regions. Using TCGA GBM RNA-seq datasets, we next identified differentially expressed TFs. For example, we found that MTY1L and NEUROD2, which have been reported to be tumor suppressors, are downregulated while HOX family TFs are upregulated in GBM tissues, as compared to normal tissues. We also identified thousands of enhancers specifically active in GBM, as compared to normal cells, using H3K27ac ChIP-seq and ATAC-seq datasets. TF motif analysis of the identified enhancers revealed that motifs of Basic Leucine Zipper Domain (bZIP), CAAT box-binding transcription factor (CTF), homeobox (HOX), signal transducer and activator of transcription (STAT), and helix-turn-helix (HTH) family were enriched. Conclusion: By integrating transcriptomic and epigenomic datasets, we identified and characterized TFs activated in GBM. We are currently investigating how ZFX cooperates with the TFs we identified as bound to the GBM-specific enhancers. These studies will provide insight into the molecular mechanisms underlying GBM. Citation Format: Zexun Wu, Shannon Schreiner, Peggy J. Farnham, Suhn K. Rhie. Identification of transcription factors activated in glioblastoma multiforme [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2427.

Loosening chromatin and dysregulated transcription: a perspective on cryptic transcription during mammalian aging.

Cryptic transcription, the initiation of transcription from non-promoter regions within a gene body, is a type of transcriptional dysregulation that occurs throughout eukaryotes. In mammals, cryptic transcription is normally repressed at the level of chromatin, and this process is increased upon perturbation of complexes that increase intragenic histone H3 lysine 4 methylation or decrease intragenic H3 lysine 36 methylation, DNA methylation, or nucleosome occupancy. Significantly, similar changes to chromatin structure occur during aging, and, indeed, recent work indicates that cryptic transcription is elevated during aging in mammalian stem cells. Although increased cryptic transcription is known to promote aging in yeast, whether elevated cryptic transcription also contributes to mammalian aging is unclear. There is ample evidence that perturbations known to increase cryptic transcription are deleterious in embryonic and adult stem cells, and in some cases phenocopy certain aging phenotypes. Furthermore, an increase in cryptic transcription requires or impedes pathways that are known to have reduced function during aging, potentially exacerbating other aging phenotypes. Thus, we propose that increased cryptic transcription contributes to mammalian stem cell aging.

Exploring Common Therapeutic Targets for Neurodegenerative Disorders Using Transcriptome Study.

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are well-known neuronal degenerative disorders that share common pathological events. Approved medications alleviate symptoms but do not address the root cause of the disease. Energy dysfunction in the neuronal population leads to various pathological events and ultimately results in neuronal death. Identifying common therapeutic targets for these disorders may help in the drug discovery process. The Brodmann area 9 (BA9) region is affected in both the disease conditions and plays an essential role in cognitive, motor, and memory-related functions. Analyzing transcriptome data of BA9 provides deep insights related to common pathological pathways involved in AD and PD. In this work, we map the preprocessed BA9 fastq files generated by RNA-seq for disease and control samples with reference hg38 genomic assembly and identify common variants and differentially expressed genes (DEG). These variants are predominantly located in the 3′ UTR (non-promoter) region, affecting the conserved transcription factor (TF) binding motifs involved in the methylation and acetylation process. We have constructed BA9-specific functional interaction networks, which show the relationship between TFs and DEGs. Based on expression signature analysis, we propose that MAPK1, VEGFR1/FLT1, and FGFR1 are promising drug targets to restore blood-brain barrier functionality by reducing neuroinflammation and may save neurons.

Application of Random Forest and data integration identifies three dysregulated genes and enrichment of Central Carbon Metabolism pathway in Oral Cancer

BackgroundStudies of epigenomic alterations associated with diseases primarily focus on methylation profiles of promoter regions of genes, but not of other genomic regions. In our past work (Das et al. 2019) on patients suffering from gingivo-buccal oral cancer – the most prevalent form of cancer among males in India – we have also focused on promoter methylation changes and resultant impact on transcription profiles. Here, we have investigated alterations in non-promoter (gene-body) methylation profiles and have carried out an integrative analysis of gene-body methylation and transcriptomic data of oral cancer patients.MethodsTumor and adjacent normal tissue samples were collected from 40 patients. Data on methylation in the non-promoter (gene-body) regions of genes and transcriptome profiles were generated and analyzed. Because of high dimensionality and highly correlated nature of these data, we have used Random Forest (RF) and other data-analytical methods.ResultsIntegrative analysis of non-promoter methylation and transcriptome data revealed significant methylation-driven alterations in some genes that also significantly impact on their transcription levels. These changes result in enrichment of the Central Carbon Metabolism (CCM) pathway, primarily by dysregulation of (a) NTRK3, which plays a dual role as an oncogene and a tumor suppressor; (b) SLC7A5 (LAT1) which is a transporter dedicated to essential amino acids, and is overexpressed in cancer cells to meet the increased demand for nutrients that include glucose and essential amino acids; and, (c) EGFR which has been earlier implicated in progression, recurrence, and stemness of oral cancer, but we provide evidence of epigenetic impact on overexpression of this gene for the first time.ConclusionsIn rapidly dividing cancer cells, metabolic reprogramming from normal cells takes place to enable enhanced proliferation. Here, we have identified that among oral cancer patients, genes in the CCM pathway – that plays a fundamental role in metabolic reprogramming – are significantly dysregulated because of perturbation of methylation in non-promoter regions of the genome. This result compliments our previous result that perturbation of promoter methylation results in significant changes in key genes that regulate the feedback process of DNA methylation for the maintenance of normal cell division.

Effect of sonication on results of ChIP-seq experiments involving PRC2 related proteins

Sonication is one of the essential strategies of chromatin fragmentation in Chromatin immunoprecipitation (ChIP) assay. The impact of proteins with different molecular weights generated under different duration of sonication on the results of Chromatin immunoprecipitation sequencing (ChIP-seq) experiments involving the Polycomb Repressive Complex 2 (PRC2) related protein EZH2, which is a histone methyltransferase, and its product H3K27me3 was investigated. The results indicate that in the promoter region or nonpromoter region, there were hardly any differences among the H3K27me3 peaks annotated genes from different duration of sonication, which suggesting that the duration of sonication had little effect on histone ChIP-seq results. In contrast, in the promoter region newly gained EZH2 peaks annonated genes at 20 min sonication time were significantly clustered in the gene ontology (GO) pathways related to actin filament bundle compared with 10 min. In the nonpromoter region, compared with 10 min, the GO pathways of newly gained EZH2 peaks annonated genes at 20 min sonication time is much more than that of lost EZH2 peaks annonated genes. And in the nonpromoter region, compared with 20 min, the GO pathways of lost EZH2 peaks annonated genes at 30 min sonication time is much more than that of newly gained EZH2 peaks annonated genes. Most of these pathways are associated with RNA polymerase II (RNAPII), organ development and cell morphogenesis. These suggest that the genomic information of EZH2 will be lost if the duration of sonication is not enough or too long. Different duration of sonication mainly affect the EZH2 peaks in PRC2 unoccupied region and the bivalent promoter in the promoter region, as well as the PRC2 occupied region, PRC2 unoccupied region and the activated enhancer in the nonpromoter region. Therefore, the sonication for the chromatin related proteins with high molecular weights need to be optimized to make chromatin fragments size vary from 100 bp to 500 bp, which will yield relatively comprehensive genomic information of the target protein. For histones, which are of small molecular weights, duration of sonication has little effect on them.

P13.15 DNA methylation abnormalities in non-promoter regulatory regions are associated with invasive behavior in pituitary tumors

Abstract BACKGROUND Despite histologically benign, pituitary tumors (PT) may invade important adjacent neurovascular structures which can incur in significant comorbidities preventing a complete surgical resection and contributing to resistance to medical treatment. DNA methylation clearly stratified PT based on their functional status i.e. nonfunctioning PTs (NFPTs) from functioning PT (FPTs). However associations of methylation aberrations with invasive behavior is less clear. MATERIAL AND METHODS In order to evaluate whether DNA methylation alterations in regulatory regions other than promoter and coding regions are associated with invasive behavior we performed a meta-analysis of the genome-wide methylome of three public available PT cohorts plus our own (Illumina HumanMethylation platforms- 450K/EPIC). Pituitary specimens comprised of 43 invasive pituitary tumors (InvPT) and 37 noninvasive (NInvPT); 12 FPT and 68 NFPTs, in addition to 20 non-tumor pituitaries. RNA-seq data were available for one cohort (n=23, 12 InvPT,11NInvPT) and integrated with DNA methylation. Invasiveness criteria was based on Knosp grade >= 2 and/or sphenoid or dural invasion. RESULTS Wilcoxon Rank-sum test; Δβ=0.15; p-value <0.001 identified 58 differentially methylated CpG sites in InvPT that were mainly hypomethylated (95%) in relation to NInvPT. NInvPT methylation profile was similar to non-tumor specimens, despite its heterogeneity. Thirty-four percent (n=20) of the differentially methylated CpG sites were located within predicted enhancer regions distributed in intronic (40%), intergenic (40%) and promoter (20%) regions. Predicted enhancer-target genes were enriched for actin filament cell movement, response to starvation, growth factor stimulus and protein autophosporilation pathways. Among them, ZNF625 and INO80E were found mostly negative correlated among methylation and expression data (-0.50 and -0.48, respectively), besides DOC2A found to be one potentially differentially expressed gene under enhancer control (log2FC > 0.2, pvalue <0.05). CONCLUSION Our results suggest that methylation alterations in predicted regulatory regions, such as enhancers, annotated in non-promoter regions (introns and intergenic) may contribute to the invasive behavior of PT.

DeePromoter: Robust Promoter Predictor Using Deep Learning.

The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm.

DNA Methylation Identifies Genetically and Prognostically Distinct Subtypes of MDS

Background: Recurrent somatic mutations in patients with myelodysplastic syndromes (MDS) implicate several epigenetic regulators in the molecular pathobiology of these disorders. We hypothesized that identification of MDS subtypes defined by DNA methylation patterns could improve prognostication and enhance our understanding of MDS disease biology. Methylation assays that investigate regions of the genome beyond promoters may better inform which regulatory regions possess biologic importance in the development and progression of MDS. Additionally, computational methods that agnostically approach large methylation datasets are needed when attempting to classify clinically and genetically heterogeneous disease populations such as MDS.Methods: To measure DNA methylation, we used bisulfite padlock probes (BSPP), a targeted enrichment technique that captures approximately 500,000 unique CpGs in regions known to contain differentially methylated regulatory regions. BSPP was performed on bone marrow mononuclear DNA from 141 treatment-naïve patients with MDS that were genetically and clinically annotated in prior studies (Bejar et al. N Engl J Med 2011 and J Clin Oncol 2012). We used Onco-GPS, a computational method that identifies shared underlying cellular states among heterogeneous sample populations in an unsupervised manner, to identify DNA methylation subtypes of MDS (Kim et al. Cell Systems 2017). This method uses non-negative matrix factorization (NMF) followed by consensus clustering of the resulting NMF components. The optimal number of NMF components and consensus clusters were chosen to maximize the cophenetic correlation coefficient. A Fisher's Exact test was used to test for enrichment of genetic lesions in specific clusters. Univariate and multivariable models of overall survival (OS) were constructed using cox proportional hazards regression. Variables with univariate p-values <0.2 were evaluated for inclusion in the final multivariable model and the final model was constructed using a forward and backward stepwise procedure based on the Aikake Information Criterion.Results: We found that 5 NMF components and 5 consensus clusters maximized the cophenetic correlation coefficient, indicating the most stable partitioning of our dataset (Figures 1A-1B). The samples making up each cluster were enriched largely in a single NMF component, indicating that the underlying DNA methylation loci contributing to each cluster are distinct. Specific genetic and cytogenetic abnormalities were significantly enriched in different clusters, including enrichment of mutations in TP53 and U2AF1 in cluster 2; EZH2, ASXL1, RUNX1, SRSF2 in cluster 3; TET2 in cluster 4; SF3B1 in cluster 5; and abnormal and complex karyotypes enriched in clusters 1 and 2 (Figure 1C-1D). Kaplan-Meier curves revealed two major patterns of survival with clusters 2 and 3 displaying inferior OS when compared with clusters 1, 4, and 5. We combined clusters with similar median OS into “High Risk” and “Low Risk” cluster groups. In univariate Cox proportional hazards regression, High Risk and Low Risk cluster groups had significant differences in OS (HR, 1.95; 95% CI,1.24-3.08; p=0.003). In a multivariable model including elements of the IPSS-R, cluster risk group remained a significant predictor of OS (HR, 2.02; 95% CI, 1.25-3.27, p=0.004). We determined cluster-specific differentially methylated regions (DMRs) and found divergent patterns of methylation among clusters with the majority of DMRs located at non-promoter regulatory regions with high enrichment at both active and bivalent enhancers (as defined in a reference epigenome from mobilized CD34+ cells).Conclusion: Using BSPP and novel computational methods, we found that DNA methylation of MDS patients identifies clinically relevant subtypes not otherwise distinguished by current prognostic models. Our analysis also shows that while genetic abnormalities can be associated with specific DNA methylation changes, patients with diverse genetic lesions can converge on common DNA methylation states, reflecting shared pathogenic mechanisms and clinical outcomes. Ongoing work to annotate the biological differences between the DNA methylation subgroups identified will enhance our understanding of the role of DNAm in prognosis and the underlying biology of MDS. [Display omitted] DisclosuresZhang:Singlera Genomics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Bejar:AbbVie/Genentech: Consultancy, Honoraria; Modus Outcomes: Consultancy; Takeda: Research Funding; Celgene: Consultancy, Honoraria; Astex/Otsuka: Consultancy, Honoraria; Genoptix: Consultancy; Foundation Medicine: Consultancy.

Integrated Analysis of Global DNA Methylation and Transcription Reveals a Leukemia Subtype with Extreme Hypermethylation Associated with Silencing of Regulators of Differentiation

Acute myeloid leukemia (AML) represents a highly heterogeneous myeloid stem cell disorder classified based on various genetic defects. Besides genetic alterations, epigenetic changes are recognized as an additional mechanism contributing to leukemogenesis, but insight into the latter process remains minor. Using a combination of Methyl-CpG-Immunoprecipitation (MCIp-chip) and MALDI-TOF analysis of bisulfite-treated DNA in a cohort of 196 AML patients we previously demonstrated that (cyto)genetically defined AML subtypes, including CBFB-MYH11, AML-ETO, NPM1-mut, CEBPA-mut or IDH1/2-mut subtypes, express specific DNA-methylation profiles (Gebhard et al, Leukemia, 2018). A fraction of AML patients (5/196) displayed a unique abnormal hypermethylation profile that was completely distinct from any other AML subtype. These patients present immature leukemia (FAB M0, M1) with various chromosomal aberrations but very few mutations (e.g. no IDH1/2, KRAS, DNMT3A) that might explain the CpG island methylator phenotype (CIMP) phenotype. The CIMP patients showed high resemblance with a recently reported CEBPA methylated subgroup (Wouters et al, 2007 and Figueroa et al, 2009), which we confirmed by MCIp-chip and MALDI-TOF analysis.To explore the whole range of epigenetic alterations in the CIMP-AML patients we performed in-depth global DNA methylation and gene expression analyses (MCIp-seq and RNA-seq) in 45 AML and 12 CIMP patients from both studies. Principle component analysis and t-distributed stochastic neighbor embedding (t-SNE) revealed that CIMP patients express a unique DNA-methylation and gene-expression signature that separated them from all other AMLs. We could discriminate promoter methylation from non-promoter methylation by selecting MCIp-seq peaks within 3kb around TSS. Promoter hypermethylation was highly associated with repression of genes (PCC = -0.053, p-value = 0.00075). Hypermethylation of non-promoter regions was more strongly associated with upregulation of genes (PCC = 0.046, p-value = 4.613e-06). Interestingly, differentially methylated regions also showed a positive association with myeloid lineage CTCF binding sites (27% vs 18% expected, p-value < 2.2e-16 in a chi-square test of independence). Methylation of CTCF sites causes loss of CTCF binding, which has been reported to disrupt boundaries between so-called topologically associated domains (TADs), allowing enhancers located in a particular TAD to become accessible to genes in adjacent TADs and affect their transcription. Whether this is the case is under investigation. In this study we particularly focused on the role of hypermethylation of promoters in CIMP-AMLs.Promoters of many transcriptional regulators that are involved in the differentiation of myeloid lineages of which several are frequently mutated in AML were hypermethylated and repressed, including CEBPA, CEBPD, IRF8, GATA2, KLF4, MITF or MAFB. Notably, HMGA2, a critical regulator of myeloid progenitor expansion, exhibited the largest degree of CIMP promoter hypermethylation compared to the other AMLs, accompanied by a reduction in gene expression. Moreover, multiple members of the HOXB family and KLF1 (erythroid differentiation) were methylated and repressed as well.In addition, these patients frequently showed hypermethylation of many chromatin factors (e.g. LMNA, CHD7 or TET2). Hypermethylation of the TET2 promoter could result in a loss of maintenance DNA demethylation and therefore successive hypermethylation at CpG islands. We carried out regulome-capture-bisulfite sequencing on CIMP-AMLs compared to other AML samples and normal blood cell controls and confirmed methylation of the same transcription and chromatin factor promoters.We conclude that these leukemias represent very primitive HSCPs which are blocked in differentiation into multiple hematopoietic lineages, due to the absence of regulators of these lineages. Although the underlying cause for the extreme hypermethylation signature is still subject to ongoing studies, the consequence of promoter hypermethylation is silencing of key lineage regulators causing the differentiation arrest in these cells. We argue that these patients may particularly benefit from therapies that revert DNA methylation. DisclosuresEhninger:Cellex Gesellschaft fuer Zellgewinnung mbH: Employment, Equity Ownership; GEMoaB Monoclonals GmbH: Employment, Equity Ownership; Bayer: Research Funding. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding.

Integrative genomic analysis reveals novel regulatory mechanisms of eyeless during Drosophila eye development.

Eyeless (ey) is one of the most critical transcription factors for initiating the entire eye development in Drosophila. However, the molecular mechanisms through which Ey regulates target genes and pathways have not been characterized at the genomic level. Using ChIP-Seq, we generated an endogenous Ey-binding profile in Drosophila developing eyes. We found that Ey binding occurred more frequently at promoter compared to non-promoter regions. Ey promoter binding was correlated with the active transcription of genes involved in development and transcription regulation. An integrative analysis revealed that Ey directly regulated a broad and highly connected genetic network, including many essential patterning pathways, and known and novel eye genes. Interestingly, we observed that Ey could target multiple components of the same pathway, which might enhance its control of these pathways during eye development. In addition to protein-coding genes, we discovered Ey also targeted non-coding RNAs, which represents a new regulatory mechanism employed by Ey. These findings suggest that Ey could use multiple molecular mechanisms to regulate target gene expression and pathway function, which might enable Ey to exhibit a greater flexibility in controlling different processes during eye development.

Tri-methylation of histone H3 lysine 4 facilitates gene expression in ageing cells.

Transcription of protein coding genes is accompanied by recruitment of COMPASS to promoter-proximal chromatin, which methylates histone H3 lysine 4 (H3K4) to form H3K4me1, H3K4me2 and H3K4me3. Here, we determine the importance of COMPASS in maintaining gene expression across lifespan in budding yeast. We find that COMPASS mutations reduce replicative lifespan and cause expression defects in almost 500 genes. Although H3K4 methylation is reported to act primarily in gene repression, particularly in yeast, repressive functions are progressively lost with age while hundreds of genes become dependent on H3K4me3 for full expression. Basal and inducible expression of these genes is also impaired in young cells lacking COMPASS components Swd1 or Spp1. Gene induction during ageing is associated with increasing promoter H3K4me3, but H3K4me3 also accumulates in non-promoter regions and the ribosomal DNA. Our results provide clear evidence that H3K4me3 is required to maintain normal expression of many genes across organismal lifespan.