Identifying DNase I hypersensitive sites as driver distal regulatory elements in breast cancer

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Neutrophil Extracellular Trap Formation and Deoxyribonuclease I Activity in Patients with Otitis Media with Antineutrophil Cytoplasmic Antibody-Associated Vasculitis

INTRODUCTION

No previous studies have evaluated the levels of neutrophil extracellular trap (NET) remnants or the importance of deoxyribonuclease (DNase) I activity based on the disease activity of otitis media with antineutrophil cytoplasmic antibody-associated vasculitis (OMAAV). The aim of this study was to explore the formation of NETs in the middle ear of patients with OMAAV during the onset and remission phases of the disease, with a particular focus on the relationships between the quantifiable levels of NET remnants and DNase I activity.

METHODS

OMAAV patients were eligible for inclusion. Patients with otitis media with effusion (OME) were examined as controls. The levels of cell-free deoxyribonucleic acid (DNA), citrullinated-histone H3 (cit-H3)-DNA complex, and myeloperoxidase (MPO)-DNA complex were quantified using an enzyme-linked immunosorbent assay. DNase I activity was measured using a fluorometric method.

RESULTS

The quantifiable levels of cell-free DNA, cit-H3-DNA complex, and MPO-DNA complex in the middle ear lavage of patients with OMAAV at onset were significantly higher than those in patients with OMAAV at remission and in patients with OME. DNase I activity in the patients with OMAAV at onset was significantly lower than those in patients with OMAAV at remission and OME and was negatively correlated with the level of MPO-DNA complex.

CONCLUSIONS

This study suggests that NET remnants and DNase I activity may be potentially useful biomarkers for the diagnosis and disease activity of OMAAV.

iDHS-FFLG: Identifying DNase I Hypersensitive Sites by Feature Fusion and Local-Global Feature Extraction Network

The DNase I hypersensitive sites (DHSs) are active regions on chromatin that have been found to be highly sensitive to DNase I. These regions contain various cis-regulatory elements, including promoters, enhancers and silencers. Accurate identification of DHSs helps researchers better understand the transcriptional machinery of DNA and deepen the knowledge of functional DNA elements in non-coding sequences. Researchers have developed many methods based on traditional experiments and machine learning to identify DHSs. However, low prediction accuracy and robustness limit their application in genetics research. In this paper, a novel computational approach based on deep learning is proposed by feature fusion and local-global feature extraction network to identify DHSs in mouse, named iDHS-FFLG. First of all, multiple binary features of nucleotides are fused to better express sequence information. Then, a network consisting of the convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and self-attention mechanism is designed to extract local features and global contextual associations. In the end, the prediction module is applied to distinguish between DHSs and non-DHSs. The results of several experiments demonstrate the superior performances of iDHS-FFLG compared to the latest methods.

LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome

DNase I hypersensitive sites (DHSs) are a specific genomic region, which is critical to detect or understand cis-regulatory elements. Although there are many methods developed to detect DHSs, there is a big gap in practice. We presented a deep learning-based language model for predicting DHSs, named LangMoDHS. The LangMoDHS mainly comprised the convolutional neural network (CNN), the bi-directional long short-term memory (Bi-LSTM) and the feed-forward attention. The CNN and the Bi-LSTM were stacked in a parallel manner, which was helpful to accumulate multiple-view representations from primary DNA sequences. We conducted 5-fold cross-validations and independent tests over 14 tissues and 4 developmental stages. The empirical experiments showed that the LangMoDHS is competitive with or slightly better than the iDHS-Deep, which is the latest method for predicting DHSs. The empirical experiments also implied substantial contribution of the CNN, Bi-LSTM, and attention to DHSs prediction. We implemented the LangMoDHS as a user-friendly web server which is accessible at http:/www.biolscience.cn/LangMoDHS/. We used indices related to information entropy to explore the sequence motif of DHSs. The analysis provided a certain insight into the DHSs.

Evolution and antiviral activity of a human protein of retroviral origin

Endogenous retroviruses are abundant components of mammalian genomes descended from ancient germline infections. In several mammals, the envelope proteins encoded by these elements protect against exogenous viruses, but this activity has not been documented with endogenously expressed envelopes in humans. We report that the human genome harbors a large pool of envelope-derived sequences with the potential to restrict retroviral infection. To test this, we characterized an envelope-derived protein, Suppressyn. We found that Suppressyn is expressed in human preimplantation embryos and developing placenta using its ancestral retroviral promoter. Cell culture assays showed that Suppressyn, and its hominoid orthologs, could restrict infection by extant mammalian type D retroviruses. Our data support a generalizable model of retroviral envelope co-option for host immunity and genome defense.

A network-based method for predicting disease-associated enhancers

Background

Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers.

Results

In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases.

Conclusions

Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.

The Expression Patterns and Prognostic Value of the Proteasome Activator Subunit Gene Family in Gastric Cancer Based on Integrated Analysis

Increasing evidence supports that proteasome activator subunit (PSME) genes play an indispensable role in multiple tumors. The diverse expression patterns, prognostic value, underlying mechanism, and the role in the immunotherapy of PSME genes in gastric cancer (GC) have yet to be fully elucidated. We systematically demonstrated the functions of these genes in GC using various large databases, unbiased in silico approaches, and experimental validation. We found that the median expression levels of all PSME genes were significantly higher in GC tissues than in normal tissues. Our findings showed that up-regulated PSME1 and PSME2 expression significantly correlated with favorable overall survival, post-progression survival, and first progression survival in GC patients. The expression of PSME1 and PSME2 was positively correlated with the infiltration of most immune cells and the activation of anti-cancer immunity cycle steps. Moreover, GC patients with high PSME1 and PSME2 expression have higher immunophenoscore and tumor mutational burden. In addition, a receiver operating characteristic analysis suggested that PSME3 and PSME4 had high diagnostic performance for distinguishing GC patients from healthy individuals. Moreover, our further analysis indicated that PSME genes exert an essential role in GC, and the present study indicated that PSME1 and PSME2 may be potential prognostic markers for enhancing survival and prognostic accuracy in GC patients and may even act as potential biomarkers for GC patients indicating a response to immunotherapy. PSME3 may serve as an oncogene in tumorigenesis and may be a promising therapeutic target for GC. PSME4 had excellent diagnostic performance and could serve as a good diagnostic indicator for GC.

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network

DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription factor-binding site, etc. Moreover, the related locus of disease (or trait) are usually enriched in the DHS regions. Therefore, the detection of DHS region is of great significance. In this study, we develop a deep learning-based algorithm to identify whether an unknown sequence region would be potential DHS. The proposed method showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs. Furthermore, for the convenience of related wet-experimental researchers, the user-friendly web-server iDHS-Deep was established at http://lin-group.cn/server/iDHS-Deep/, by which users can easily distinguish DHS and non-DHS and obtain the corresponding developmental stage ofDHS.

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

DNASE1L2, as a Carcinogenic Marker, Affects the Phenotype of Breast Cancer Cells Via Regulating Epithelial-Mesenchymal Transition Process

Purpose: The authors explore the role of DNASE1L2 in breast cancer (BC) and its affect on the cell phenotype. Methods: Breast invasive ductal carcinoma RNA-Seq data set was downloaded from The Cancer Genome Atlas database for analyzing DNASE1L2 levels. Overall survival curve was plotted by Kaplan-Meier methods. The correlations between DNASE1L2 expression and clinical characteristics were analyzed by chi-square tests. Cox regression models were implemented for analyzing the potential prognosticators of BC. Small interference RNA-DNASE1L2 and pcDNA3.1-DNASE1L2 were transfected into BC cells to silence and overexpress DNASE1L2, respectively. Relative mRNA and protein levels were determined by quantitative real-time PCR (qRT-PCR) and Western blot, respectively. Cell counting Kit-8, clone formation, and Transwell assays were employed to measure the proliferative, invasive, and migratory abilities. Results: Bioinformatics analysis showed that the levels of DNASE1L2 were found to be elevated in BC tissues, which was further proved by qRT-PCR tests. Besides, high expression of DNASE1L2 was dramatically led to a poor overall survival. Furthermore, DNASE1L2 expression was remarkably associated with age and pathologic-stage. Silencing DNASE1L2 showed an inhibitory effect on the proliferation, invasion, and migration of MCF7 cells, whereas overexpression of DNASE1L2 in BT549 cells presented the opposite results. Mechanistically, downregulation of DNASE1L2 could significantly enhance the levels of E-cadherin, as well as suppress the levels of Vimentin, N-cadherin and Snail, whereas upregulation of DNASE1L2 showed the reverse outcomes. Conclusion: This study for the first time demonstrated that DNASE1L2 was upregulated in BC cells, and acted as an oncogene to affect the phenotype of BC cells by modulating the epithelial-mesenchymal transition process, which suggested that DNASE1L2 might be considered as a useful biomarker for BC therapeutics.

DNase I improves corneal epithelial and nerve regeneration in diabetic mice

DNase I has been reported to improve diabetic wound healing through the clearance of neutrophils extracellular traps (NETs) caused by neutrophil aggregation. However, the function of DNase I on diabetic corneal wound healing remains unclear. Here, we investigated the effect and mechanism of topical DNase I application on diabetic mouse corneal epithelial and nerve regeneration. Corneal epithelial defects, inflammatory response, regeneration‐related signalling pathways, oxidative stress, corneal innervation and sensation were examined and compared between the diabetic and normal mice. The results confirmed firstly the increased NETs production during the delayed corneal epithelial wound healing of diabetic mice, which was significantly improved through either DNase I or Cl‐amidine administration. Mechanistically, DNase I improved inflammation resolution, reactivated epithelial regeneration‐related signalling pathways and attenuated the accumulation of reactive oxygen species (ROS). Moreover, DNase I application also promoted corneal nerve regeneration and restored the impaired corneal sensitivity in diabetic mice. Therefore, these results indicate that topical DNase I application promotes corneal epithelial wound healing and mechanical sensation restoration in diabetic mice, representing the potential therapeutic approach for diabetic keratopathy.

Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma

Alternative polyadenylation (APA) is a gene regulatory process that dictates mRNA 3'-UTR length, resulting in changes in mRNA stability and localization. APA is frequently disrupted in cancer and promotes tumorigenesis through altered expression of oncogenes and tumor suppressors. Pan-cancer analyses have revealed common APA events across the tumor landscape; however, little is known about tumor type-specific alterations that may uncover novel events and vulnerabilities. Here, we integrate RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) to comprehensively analyze APA events in 148 pancreatic ductal adenocarcinomas (PDACs). We report widespread, recurrent, and functionally relevant 3'-UTR alterations associated with gene expression changes of known and newly identified PDAC growth-promoting genes and experimentally validate the effects of these APA events on protein expression. We find enrichment for APA events in genes associated with known PDAC pathways, loss of tumor-suppressive miRNA binding sites, and increased heterogeneity in 3'-UTR forms of metabolic genes. Survival analyses reveal a subset of 3'-UTR alterations that independently characterize a poor prognostic cohort among PDAC patients. Finally, we identify and validate the casein kinase CSNK1A1 (also known as CK1alpha or CK1a) as an APA-regulated therapeutic target in PDAC. Knockdown or pharmacological inhibition of CSNK1A1 attenuates PDAC cell proliferation and clonogenic growth. Our single-cancer analysis reveals APA as an underappreciated driver of protumorigenic gene expression in PDAC via the loss of miRNA regulation.

Unveiling the gene regulatory landscape in diseases through the identification of DNase I-hypersensitive sites

DNase I-hypersensitive sites (DHSs) serve key roles in the regulation of gene transcription as markers of cis-regulatory elements (CREs). Recent advances in next-generation sequencing have enabled the genome-wide location and annotation of DHSs in a variety of cells. Numerous studies have confirmed that DHSs are involved in several processes in cell fate decision and development. DHSs have also been indicated in cancer and inherited diseases as driver distal regulatory elements. Here, the definition of DHSs is reviewed, in addition to high-throughput methods of DHS identification. Furthermore, the function of DHSs in gene expression is probed. The roles of DHSs in disease occurrence are also reviewed and discussed. Concomitant advances in the identification of essential roles of DHSs will assist in disclosing the underlying molecular mechanisms, supplementing gene transcription and enlarging the molecular basis of DHS-related bioprocesses, phenotypes, distinct traits and diseases.

Insights into the Mutational Burden of Human Induced Pluripotent Stem Cells from an Integrative Multi-Omics Approach

To understand the mutational burden of human induced pluripotent stem cells (iPSCs), we sequenced genomes of 18 fibroblast-derived iPSC lines and identified different classes of somatic mutations based on structure, origin, and frequency. Copy-number alterations affected 295 kb in each sample and strongly impacted gene expression. UV-damage mutations were present in ∼45% of the iPSCs and accounted for most of the observed heterogeneity in mutation rates across lines. Subclonal mutations (not present in all iPSCs within a line) composed 10% of point mutations and, compared with clonal variants, showed an enrichment in active promoters and increased association with altered gene expression. Our study shows that, by combining WGS, transcriptome, and epigenome data, we can understand the mutational burden of each iPSC line on an individual basis and suggests that this information could be used to prioritize iPSC lines for models of specific human diseases and/or transplantation therapy.

Chromatin reprogramming in breast cancer

Reprogramming of the chromatin landscape is a critical component to the transcriptional response in breast cancer. Effects of sex hormones such as estrogens and progesterone have been well described to have a critical impact on breast cancer proliferation. However, the complex network of the chromatin landscape, enhancer regions and mode of function of steroid receptors (SRs) and other transcription factors (TFs), is an intricate web of signaling and functional processes that is still largely misunderstood at the mechanistic level. In this review, we describe what is currently known about the dynamic interplay between TFs with chromatin and the reprogramming of enhancer elements. Emphasis has been placed on characterizing the different modes of action of TFs in regulating enhancer activity, specifically, how different SRs target enhancer regions to reprogram chromatin in breast cancer cells. In addition, we discuss current techniques employed to study enhancer function at a genome-wide level. Further, we have noted recent advances in live cell imaging technology. These single-cell approaches enable the coupling of population-based assays with real-time studies to address many unsolved questions about SRs and chromatin dynamics in breast cancer.