SEdb 2.0: a comprehensive super-enhancer database of human and mouse

Nuclear ANLN regulates transcription initiation related Pol II clustering and target gene expression

Anillin (ANLN), a mitotic protein that regulates contractile ring assembly, has been reported as an oncoprotein. However, the function of ANLN in cancer cells, especially in the nucleus, has not been fully understood. Here, we report a role of nuclear ANLN in gene transcriptional regulation. We find that nuclear ANLN directly interacts with the RNA polymerase II (Pol II) large subunit to form transcriptional condensates. ANLN enhances initiated Pol II clustering and promotes Pol II CTD phase separation. Short-term depletion of ANLN alters the chromatin binding and enhancer-mediated transcriptional activity of Pol II. The target genes of ANLN-Pol II axis are involved in oxidoreductase activity, Wnt signaling and cell differentiation. THZ1, a super-enhancer inhibitor, specifically inhibits ANLN-Pol II clustering, target gene expression and esophageal squamous cell carcinoma (ESCC) cell proliferation. Our results reveal the function of nuclear ANLN in transcriptional regulation, providing a theoretical basis for ESCC treatment.

Randomizing the human genome by engineering recombination between repeat elements

We lack tools to edit DNA sequences at scales necessary to study 99% of the human genome that is noncoding. To address this gap, we applied CRISPR prime editing to insert recombination handles into repetitive sequences, up to 1697 per cell line, which enables generating large-scale deletions, inversions, translocations, and circular DNA. Recombinase induction produced more than 100 stochastic megabase-sized rearrangements in each cell. We tracked these rearrangements over time to measure selection pressures, finding a preference for shorter variants that avoided essential genes. We characterized 29 clones with multiple rearrangements, finding an impact of deletions on expression of genes in the variant but not on nearby genes. This genome-scrambling strategy enables large deletions, sequence relocations, and the insertion of regulatory elements to explore genome dispensability and organization.

Long G4-rich enhancers target promoters via a G4 DNA-based mechanism

Several studies have now described instances where G-rich sequences in promoters and enhancers regulate gene expression through forming G-quadruplex (G4) structures. Relatedly, our group recently identified 301 long genomic stretches significantly enriched for minimal G4 motifs (LG4s) in humans and found the majority of these overlap annotated enhancers, and furthermore, that the promoters regulated by these LG4 enhancers are similarly enriched with G4-capable sequences. While the generally accepted model for enhancer:promoter specificity maintains that interactions are dictated by enhancer- and promoter-bound transcriptional activator proteins, the current study tested an alternative hypothesis: that LG4 enhancers interact with cognate promoters via a direct G4:G4 DNA-based mechanism. This work establishes the nuclear proximity of LG4 enhancer:promoter pairs, biochemically demonstrates the ability of individual LG4 single-stranded DNAs (ssDNAs) to directly interact target promoter ssDNAs, and confirms that these interactions, as well as the ability of LG4 enhancers to activate target promoters in culture, are mediated by G4 DNA.

sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human

Identifying cell populations associated with risk variants is essential for uncovering cell-specific mechanisms that drive disease development and progression. Integrating genome-wide association studies (GWAS) with single-cell RNA sequencing (scRNA-seq) has become an effective strategy for detecting trait-cell relationships. The accumulation of trait-related single cell data has led to an urgent need for its comprehensively processing. To address this, we developed sc2GWAS (https://bio.liclab.net/sc2GWAS/), which aims to document large-scale GWAS trait-cell regulatory pairs at single-cell resolution and provide comprehensive annotations and enrichment analyses for these related pairs. The current version of sc2GWAS curates a total of 15 078 310 candidate trait-cell pairs from > 6 300 000 individual cells, offering a valuable resource for exploring complex regulatory relationships between traits and cells. We applied strict quality control measures on both scRNA-seq data and GWAS data, ensuring the reliability and accuracy of the datasets for the identification of trait-relevant cells and genes. In addition, sc2GWAS provides ranked lists of trait-relevant genes and extensive (epi) genetic annotations, making it a valuable resource for downstream analyses. We demonstrate the utility of the platform by investigating Alzheimer's disease, where we identified significant associations between the disease and microglial cells, with the APOE gene emerging as particularly significant. This platform facilitates detailed research into complex trait-cell and trait-gene interactions, we anticipate that sc2GWAS will become a comprehensive and valuable platform for exploring GWAS trait-cell regulatory mechanisms.

Bioinformatic analysis predicts the regulatory function of noncoding SNPs associated with Long COVID-19 syndrome

Long or Post COVID-19 is a condition of collected symptoms persisted after recovery from COVID-19. Host genetic factors play a crucial role in developing Long COVID-19, and GWAS studies identified several SNPs/genes in various ethnic populations. In African-American population two SNPS, rs10999901 (C>T, p = 3.6E-08, OR = 1.39, MAF-0,27, GRCH38, chr10:71584799 bp) and rs1868001 (G>A, p = 6.7E-09, OR = 1.40, MAF-0.46, GRCH38, chr10:71587815 bp) and in Hispanic population, rs3759084 (A>C, p = 9.7E-09, OR = 1.56, MAF-0.17, chr12: 81,110,156 bp) are strongly associated with Long COVID-19. All these three SNPs reside in noncoding regions implying their regulatory function in the genome. In silico dissection suggests that rs10999901 and rs1868001 physically interact with the CDH23 and C10orf105 genes. Both SNPs act as distant enhancers and bind with several transcription factors (TFs). Further, rs10999901 SNP is a CpG that is methylated in CD4++ T cells and monocytes and loses its methylation due to transition from C>T. rs3759084 is located in the promoter (- 687 bp) of MYF5, acts as a distant enhancer, and physically interacts with PTPRQ. These results offer plausible explanations for their association and provide the basis for experiments to dissect the development of symptoms of Long COVID-19.

STAT3 drives the expression of HIF1alpha in cancer cells through a novel super-enhancer

Aerobic glycolysis is one of the major hallmarks of malignant tumors. This metabolic reprogramming benefits the rapid proliferation of cancer cells, facilitates the formation of tumor microenvironment to support their growth and survival, and impairs the efficacy of various tumor therapies. Therefore, the elucidation of the mechanisms driving aerobic glycolysis in tumors represents a pivotal breakthrough in developing therapeutic strategies for solid tumors. HIF1α serves as a central regulator of aerobic glycolysis with elevated mRNA and protein expression across multiple tumor types. However, the mechanisms contributing to this upregulation remain elusive. This study reports the identification of a novel HIF1α super enhancer (HSE) in multiple cancer cells using bioinformatics analysis, chromosome conformation capture (3C), chromatin immunoprecipitation (ChIP), and CRISPR/Cas9 genome editing techniques. Deletion of HSE in cancer cells significantly reduces the expression of HIF1α, glycolysis, cell proliferation, colony and tumor formation ability, confirming the role of HSE as the enhancer of HIF1α in cancer cells. Particularly, we demonstrated that STAT3 promotes the expression of HIF1α by binding to HSE. The discovery of HSE will help elucidate the pathways driving tumor aerobic glycolysis, offering new therapeutic targets and potentially resolving the bottleneck in solid tumor treatment.

Targeted RNA Sequencing of Head and Neck Adenoid Cystic Carcinoma Reveals SEC16A::NOTCH1 Fusion and MET Exon 14 Skipping as Potentially Actionable Alterations

PURPOSE

Adenoid cystic carcinoma (AdCC) of the head and neck harbors MYB/MYBL1::NFIB fusions in around 60% of cases, with unfavorable long-term survival due to frequent recurrences and metastases, currently lacking effective targeted therapy. The study aims to identify actionable alterations and to elucidate the molecular underpinnings of MYB/MYBL1::NFIB-negative AdCC using a large targeted RNA sequencing panel.

METHODS AND RESULTS

We retrospectively searched our MSK-Solid Fusion clinical sequencing database for head and neck AdCC sequenced between 2016 and 2023. Of a total of 55 cases, 28 showed MYB::NFIB, 7 showed MYBL1::NFIB, and one case each harbored MYB::MPDZ (case 1) and FUS::MYB (case 2). One base of tongue tumor expressed both MYB::NFIB fusion and MET exon 14 skipping transcripts due to concurrent MET splice site mutation, D1010N (case 3). One parotid tumor lacked MYB/MYBL1 rearrangement but instead showed an in-frame SEC16A::NOTCH1 fusion that preserved the secretase cleavage site (case 4). Clinical records on 4 cases with non-canonical sequencing findings were reviewed. Distant metastases were present at the initial diagnosis (case 2) or at recurrence (cases 1, 3, and 4). Disease-related mortality occurred in cases 2 and 4 despite radiotherapy and immunotherapy.

CONCLUSIONS

The study improved the understanding of AdCC providing the first documentation of tumor clinical behavior associated with MYB::MPDZ and FUS::MYB fusions and reporting potentially actionable SEC16A::NOTCH1 fusion and MET exon 14 skipping mutation. Further research is needed to explore the therapeutic utility of MET inhibition and the efficacy of γ-secretase inhibitors against rare NOTCH1 fusions in AdCC.

eRNA-IDO: A One-stop Platform for Identification, Interactome Discovery, and Functional Annotation of Enhancer RNAs

Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novo assembled transcriptomes. eRNA-ID includes eight kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNAs and protein-coding genes. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/.

CDK9 inhibition inhibits multiple oncogenic transcriptional and epigenetic pathways in prostate cancer

BACKGROUND

Cyclin-dependent kinase 9 (CDK9) stimulates oncogenic transcriptional pathways in cancer and CDK9 inhibitors have emerged as promising therapeutic candidates.

METHODS

The activity of an orally bioavailable CDK9 inhibitor, CDKI-73, was evaluated in prostate cancer cell lines, a xenograft mouse model, and patient-derived tumor explants and organoids. Expression of CDK9 was evaluated in clinical specimens by mining public datasets and immunohistochemistry. Effects of CDKI-73 on prostate cancer cells were determined by cell-based assays, molecular profiling and transcriptomic/epigenomic approaches.

RESULTS

CDKI-73 inhibited proliferation and enhanced cell death in diverse in vitro and in vivo models of androgen receptor (AR)-driven and AR-independent models. Mechanistically, CDKI-73-mediated inhibition of RNA polymerase II serine 2 phosphorylation resulted in reduced expression of BCL-2 anti-apoptotic factors and transcriptional defects. Transcriptomic and epigenomic approaches revealed that CDKI-73 suppressed signaling pathways regulated by AR, MYC, and BRD4, key drivers of dysregulated transcription in prostate cancer, and reprogrammed cancer-associated super-enhancers. These latter findings prompted the evaluation of CDKI-73 with the BRD4 inhibitor AZD5153, a combination that was synergistic in patient-derived organoids and in vivo.

CONCLUSION

Our work demonstrates that CDK9 inhibition disrupts multiple oncogenic pathways and positions CDKI-73 as a promising therapeutic agent for prostate cancer, particularly aggressive, therapy-resistant subtypes.

Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

Genomic analyses identify 15 susceptibility loci and reveal HDAC2, SOX2-OT, and IGF2BP2 in a naturally-occurring canine model of gastric cancer

Gastric cancer (GC) is the fifth most common human cancer worldwide, but the genetic etiology is largely unknown. We performed a Bayesian genome-wide association study and selection analyses in a naturally-occurring canine model of GC, the Belgian Tervuren and Sheepdog breeds, to elucidate underlying genetic risk factors. We identified 15 loci with over 90% predictive accuracy for the GC phenotype. Variant filtering revealed germline putative regulatory variants for the EPAS1 (HIF2A) and PTEN genes and a coding variant in CD101. Although closely related to Tervuren and Sheepdogs, Belgian Malinois rarely develop GC. Across-breed analyses uncovered protective haplotypes under selection in Malinois at SOX2-OT and IGF2BP2. Among Tervuren and Sheepdogs, HDAC2 putative regulatory variants were present at comparatively high frequency and were associated with GC. Here, we describe a complex genetic architecture governing GC in a dog model, including genes such as PDZRN3, that have not been associated with human GC.

Decoding the genomic landscape of chromatin-associated biomolecular condensates

Biomolecular condensates play a significant role in chromatin activities, primarily by concentrating and compartmentalizing proteins and/or nucleic acids. However, their genomic landscapes and compositions remain largely unexplored due to a lack of dedicated computational tools for systematic identification in vivo. To address this, we develop CondSigDetector, a computational framework designed to detect condensate-like chromatin-associated protein co-occupancy signatures (CondSigs), to predict genomic loci and component proteins of distinct chromatin-associated biomolecular condensates. Applying this framework to mouse embryonic stem cells (mESC) and human K562 cells enable us to depict the high-resolution genomic landscape of chromatin-associated biomolecular condensates, and uncover both known and potentially unknown biomolecular condensates. Multi-omics analysis and experimental validation further verify the condensation properties of CondSigs. Additionally, our investigation sheds light on the impact of chromatin-associated biomolecular condensates on chromatin activities. Collectively, CondSigDetector provides an approach to decode the genomic landscape of chromatin-associated condensates, facilitating a deeper understanding of their biological functions and underlying mechanisms in cells.

HDAC4 influences the DNA damage response and counteracts senescence by assembling with HDAC1/HDAC2 to control H2BK120 acetylation and homology-directed repair

Access to DNA is the first level of control in regulating gene transcription, a control that is also critical for maintaining DNA integrity. Cellular senescence is characterized by profound transcriptional rearrangements and accumulation of DNA lesions. Here, we discovered an epigenetic complex between HDAC4 and HDAC1/HDAC2 that is involved in the erase of H2BK120 acetylation. The HDAC4/HDAC1/HDAC2 complex modulates the efficiency of DNA repair by homologous recombination, through dynamic deacetylation of H2BK120. Deficiency of HDAC4 leads to accumulation of H2BK120ac, impaired recruitment of BRCA1 and CtIP to the site of lesions, accumulation of damaged DNA and senescence. In senescent cells this complex is disassembled because of increased proteasomal degradation of HDAC4. Forced expression of HDAC4 during RAS-induced senescence reduces the genomic spread of γH2AX. It also affects H2BK120ac levels, which are increased in DNA-damaged regions that accumulate during RAS-induced senescence. In summary, degradation of HDAC4 during senescence causes the accumulation of damaged DNA and contributes to the activation of the transcriptional program controlled by super-enhancers that maintains senescence.

Histone variant H2BE enhances chromatin accessibility in neurons to promote synaptic gene expression and long-term memory

Histone proteins affect gene expression through multiple mechanisms, including through exchange with histone variants. Recent findings link histone variants to neurological disorders, yet few are well studied in the brain. Most notably, widely expressed variants of H2B remain elusive. We applied recently developed antibodies, biochemical assays, and sequencing approaches to reveal broad expression of the H2B variant H2BE and defined its role in regulating chromatin structure, neuronal transcription, and mouse behavior. We find that H2BE is enriched at promoters, and a single unique amino acid allows it to dramatically enhance chromatin accessibility. Further, we show that H2BE is critical for synaptic gene expression and long-term memory. Together, these data reveal a mechanism linking histone variants to chromatin accessibility, transcriptional regulation, neuronal function, and memory. This work further identifies a widely expressed H2B variant and uncovers a single histone amino acid with profound effects on genomic structure.

Epigenome-wide impact of MAT2A sustains the androgen-indifferent state and confers synthetic vulnerability in ERG fusion-positive prostate cancer

Castration-resistant prostate cancer (CRPC) is a frequently occurring disease with adverse clinical outcomes and limited therapeutic options. Here, we identify methionine adenosyltransferase 2a (MAT2A) as a critical driver of the androgen-indifferent state in ERG fusion-positive CRPC. MAT2A is upregulated in CRPC and cooperates with ERG in promoting cell plasticity, stemness and tumorigenesis. RNA, ATAC and ChIP-sequencing coupled with histone post-translational modification analysis by mass spectrometry show that MAT2A broadly impacts the transcriptional and epigenetic landscape. MAT2A enhances H3K4me2 at multiple genomic sites, promoting the expression of pro-tumorigenic non-canonical AR target genes. Genetic and pharmacological inhibition of MAT2A reverses the transcriptional and epigenetic remodeling in CRPC models and improves the response to AR and EZH2 inhibitors. These data reveal a role of MAT2A in epigenetic reprogramming and provide a proof of concept for testing MAT2A inhibitors in CRPC patients to improve clinical responses and prevent treatment resistance.

Super-enhancer omics in stem cell

The hallmarks of stem cells, such as proliferation, self-renewal, development, differentiation, and regeneration, are critical to maintain stem cell identity which is sustained by genetic and epigenetic factors. Super-enhancers (SEs), which consist of clusters of active enhancers, play a central role in maintaining stemness hallmarks by specifically transcriptional model. The SE-navigated transcriptional complex, including SEs, non-coding RNAs, master transcriptional factors, Mediators and other co-activators, forms phase-separated condensates, which offers a toggle for directing diverse stem cell fate. With the burgeoning technologies of multiple-omics applied to examine different aspects of SE, we firstly raise the concept of "super-enhancer omics", inextricably linking to Pan-omics. In the review, we discuss the spatiotemporal organization and concepts of SEs, and describe links between SE-navigated transcriptional complex and stem cell features, such as stem cell identity, self-renewal, pluripotency, differentiation and development. We also elucidate the mechanism of stemness and oncogenic SEs modulating cancer stem cells via genomic and epigenetic alterations hijack in cancer stem cell. Additionally, we discuss the potential of targeting components of the SE complex using small molecule compounds, genome editing, and antisense oligonucleotides to treat SE-associated organ dysfunction and diseases, including cancer. This review also provides insights into the future of stem cell research through the paradigm of SEs.

Histone variant H2BE enhances chromatin accessibility in neurons to promote synaptic gene expression and long-term memory

Regulation of histone proteins affects gene expression through multiple mechanisms including exchange with histone variants. However, widely expressed variants of H2B remain elusive. Recent findings link histone variants to neurological disorders, yet few are well studied in the brain. We applied new tools including novel antibodies, biochemical assays, and sequencing approaches to reveal broad expression of the H2B variant H2BE, and defined its role in regulating chromatin structure, neuronal transcription, and mouse behavior. We find that H2BE is enriched at promoters and a single unique amino acid allows it to dramatically enhance chromatin accessibility. Lastly, we show that H2BE is critical for synaptic gene expression and long-term memory. Together, these data reveal a novel mechanism linking histone variants to chromatin regulation, neuronal function, and memory. This work further identifies the first widely expressed H2B variant and uncovers a single histone amino acid with profound effects on genomic structure.

The discovery of GGT1 as a novel gene for ischemic stroke conferring protection against disease risk in non-smokers and non-abusers of alcohol

OBJECTIVES

Increased plasma gamma-glutamyl transferase (GGT1) has been identified as a robust and independent risk factor for ischemic stroke (IS), but the molecular mechanisms of the enzyme-disease association are unclear. The present study investigated whether polymorphisms in the GGT1 gene contribute to IS susceptibility.

MATERIALS AND METHODS

DNA samples obtained from 1288 unrelated individuals (600 IS patients and 688 controls) were genotyped for common single nucleotide polymorphisms of GGT1 using the MassArray-4 platform.

RESULTS

The rs5751909 polymorphism was significantly associated with decreased risk of ischemic stroke regardless sex and age (Pperm ≤ 0.01, dominant genetic model). The haplotype rs4820599A-rs5760489A-rs5751909A showed strong protection against ischemic stroke (OR 0.53, 95 %CI 0.36 - 0.77, Pperm ≤ 0.0001). The protective effect of SNP rs5751909 in the stroke phenotype was successfully replicated in the UK Biobank, SiGN, and ISGC cohorts (P ≤ 0.01). GGT1 polymorphisms showed joint (epistatic) effects on the risk of ischemic stroke, with some known IS-associated GWAS loci (e.g., rs4322086 and rs12646447) investigated in our population. In addition, SNP rs5751909 was found to be strongly associated with a decreased risk of ischemic stroke in non-smokers (OR 0.54 95 %CI 0.39-0.75, Pperm = 0.0002) and non-alcohol abusers (OR 0.43 95 %CI 0.30-0.61, Pperm = 2.0 × 10-6), whereas no protective effects of this SNP against disease risk were observed in smokers and alcohol abusers (Pperm < 0.05).

CONCLUSIONS

We propose mechanisms underlying the observed associations between GGT1 polymorphisms and ischemic stroke risk. This pilot study is the first to demonstrate that GGT1 is a novel susceptibility gene for ischemic stroke and provides additional evidence of the genetic contribution to impaired redox homeostasis underlying disease pathogenesis.

Validated Negative Regions (VNRs) in the VISTA Database might be Truncated Forms of Bona Fide Enhancers

The VISTA enhancer database is a valuable resource for evaluating predicted enhancers in humans and mice. In addition to thousands of validated positive regions (VPRs) in the human and mouse genomes, the database also contains similar numbers of validated negative regions (VNRs). It is previously shown that the VPRs are on average half as long as predicted overlapping enhancers that are highly conserved and hypothesize that the VPRs may be truncated forms of long bona fide enhancers. Here, it is shown that like the VPRs, the VNRs also are under strong evolutionary constraints and overlap predicted enhancers in the genomes. The VNRs are also on average half as long as predicted overlapping enhancers that are highly conserved. Moreover, the VNRs and the VPRs display similar cell/tissue-specific modification patterns of key epigenetic marks of active enhancers. Furthermore, the VNRs and the VPRs show similar impact score spectra of in silico mutagenesis. These highly similar properties between the VPRs and the VNRs suggest that like the VPRs, the VNRs may also be truncated forms of long bona fide enhancers.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Enhancer selectivity across cell types delineates three functionally distinct enhancer-promoter regulation patterns

BACKGROUND

Multiple enhancers co-regulating the same gene is prevalent and plays a crucial role during development and disease. However, how multiple enhancers coordinate the same gene expression across various cell types remains largely unexplored at genome scale.

RESULTS

We develop a computational approach that enables the quantitative assessment of enhancer specificity and selectivity across diverse cell types, leveraging enhancer-promoter (E-P) interactions data. We observe two well-known gene regulation patterns controlled by enhancer clusters, which regulate the same gene either in a limited number of cell types (Specific pattern, Spe) or in the majority of cell types (Conserved pattern, Con), both of which are enriched for super-enhancers (SEs). We identify a previously overlooked pattern (Variable pattern, Var) that multiple enhancers link to the same gene, but rarely coexist in the same cell type. These three patterns control the genes associating with distinct biological function and exhibit unique epigenetic features. Specifically, we discover a subset of Var patterns contains Shared enhancers with stable enhancer-promoter interactions in the majority of cell types, which might contribute to maintaining gene expression by recruiting abundant CTCF.

CONCLUSIONS

Together, our findings reveal three distinct E-P regulation patterns across different cell types, providing insights into deciphering the complexity of gene transcriptional regulation.

Super-enhancer mediated upregulation of MYEOV suppresses ferroptosis in lung adenocarcinoma

Super-enhancers (SEs) exerted a crucial role in regulating the transcription of oncogenes across various malignancies while the roles of SEs driven genes and the core regulatory elements remain elusive in LUAD. In this study, cancer-specific-SE-genes of lung adenocarcinoma (LUAD) were profiled through H3K27ac ChIP-seq data of cancer cell lines and normal lung tissues, which enriched in in biological processes and pathways integral to the pathophysiology of LUAD. Based on this study, LUAD cells were susceptible to SEs inhibitors, with a reduction of cell proliferation as well as an elevation of apoptosis upon JQ1 or THZ1 intervention. Moreover, the integration of SEs landscapes, CRISPRi, ChIP-PCR, Hi-C data analysis and dual-luciferase reporter assays revealed that myeloma overexpressed gene (MYEOV) was aberrantly overexpressed in LUAD via transcriptional activation by the core SE elements. Functionally, the knockdown of MYEOV undermined cell proliferation in vitro and tumor growth in vivo. In addition, the knockdown of MYEOV generated a prominent ferroptotic phenotype, characterized by elevation of intracellular ferrous iron, reactive oxygen species and lipid peroxidation, together with alteration in marker proteins (SLC7A11, GPX4, FTH1, and ACSL4). Instead, the overexpression of MYEOV accelerated cell proliferation and abrogated ferroptosis. Clinically, the overexpression of MYEOV was observed in LUAD tissues indicating a poor prognosis in patients with LUAD. Mechanistically, SMPD1-induced autophagic degradation of GPX4 assumed a crucial role in the process of ferroptosis triggered by MYEOV knockdown. Serving as an oncogene repressing ferroptosis, promoting proliferation as well as shortening survival in LUAD, SEs-mediated activation of MYEOV might distinguish as a promising therapeutic target.

Pathogenic role of super-enhancers as potential therapeutic targets in lung cancer

Lung cancer is still one of the deadliest malignancies today, and most patients with advanced lung cancer pass away from disease progression that is uncontrollable by medications. Super-enhancers (SEs) are large clusters of enhancers in the genome's non-coding sequences that actively trigger transcription. Although SEs have just been identified over the past 10 years, their intricate structure and crucial role in determining cell identity and promoting tumorigenesis and progression are increasingly coming to light. Here, we review the structural composition of SEs, the auto-regulatory circuits, the control mechanisms of downstream genes and pathways, and the characterization of subgroups classified according to SEs in lung cancer. Additionally, we discuss the therapeutic targets, several small-molecule inhibitors, and available treatment options for SEs in lung cancer. Combination therapies have demonstrated considerable advantages in preclinical models, and we anticipate that these drugs will soon enter clinical studies and benefit patients.

Employing bimodal representations to predict DNA bendability within a self-supervised pre-trained framework

The bendability of genomic DNA, which measures the DNA looping rate, is crucial for numerous biological processes of DNA. Recently, an advanced high-throughput technique known as 'loop-seq' has made it possible to measure the inherent cyclizability of DNA fragments. However, quantifying the bendability of large-scale DNA is costly, laborious, and time-consuming. To close the gap between rapidly evolving large language models and expanding genomic sequence information, and to elucidate the DNA bendability's impact on critical regulatory sequence motifs such as super-enhancers in the human genome, we introduce an innovative computational model, named MIXBend, to forecast the DNA bendability utilizing both nucleotide sequences and physicochemical properties. In MIXBend, a pre-trained language model DNABERT and convolutional neural network with attention mechanism are utilized to construct both sequence- and physicochemical-based extractors for the sophisticated refinement of DNA sequence representations. These bimodal DNA representations are then fed to a k-mer sequence-physicochemistry matching module to minimize the semantic gap between each modality. Lastly, a self-attention fusion layer is employed for the prediction of DNA bendability. In conclusion, the experimental results validate MIXBend's superior performance relative to other state-of-the-art methods. Additionally, MIXBend reveals both novel and known motifs from the yeast. Moreover, MIXBend discovers significant bendability fluctuations within super-enhancer regions and transcription factors binding sites in the human genome.

Histone H4 asymmetrically dimethylated at arginine 3 (H4R3me2a), a mark of super-enhancers

Histone H4 asymmetrically dimethylated at arginine 3 (H4R3me2a) is an active histone mark catalyzed by protein arginine methyltransferase 1 (PRMT1), a major arginine methyltransferase in vertebrates catalyzing asymmetric dimethylation of arginine. H4R3me2a stimulates the activity of lysine acetyltransferases such as CBP/p300, which catalyze the acetylation of H3K27, a mark of active enhancers, super-enhancers, and promoters. There are a few studies on the genomic location of H4R3me2a. In chicken polychromatic erythrocytes, H4R3me2a is found in introns and intergenic regions and binds to the globin locus control region (a super-enhancer) and globin regulatory regions. In this report, we analyzed chromatin immunoprecipitation sequencing data for the genomic location of H4R3me2a in the breast cancer cell line MCF7. As in avian cells, MCF7 H4R3me2a is present in intronic and intergenic regions. Nucleosomes with H4R3me2a and H3K27ac next to nucleosome-free regions are found at super-enhancers, enhancers, and promoter regions of expressed genes. Genes with critical roles in breast cancer cells have broad domains of nucleosomes with H4R3me2a, H3K27ac, and H3K4me3. Our results are consistent with PRMT1-mediated H4R3me2a playing a key role in the chromatin organization of regulatory regions of vertebrate genomes.

Construction of a gene model related to the prognosis of patients with gastric cancer receiving immunotherapy and exploration of COX7A1 gene function

BACKGROUND

GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning.

METHODS

Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy.

RESULTS

First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS).

CONCLUSIONS

The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients.

Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling

BACKGROUND

The Illumina family of Infinium Methylation BeadChip microarrays has been widely used over the last 15 years for genome-wide DNA methylation profiling, including large-scale and population-based studies, due to their ease of use and cost effectiveness. Succeeding the popular HumanMethylationEPIC BeadChip (EPICv1), the recently released Infinium MethylationEPIC v2.0 BeadChip (EPICv2) claims to extend genomic coverage to more than 935,000 CpG sites. Here, we comprehensively characterise the reproducibility, reliability and annotation of the EPICv2 array, based on bioinformatic analysis of both manifest data and new EPICv2 data from diverse biological samples.

RESULTS

We find a high degree of reproducibility with EPICv1, evidenced by comparable sensitivity and precision from empirical cross-platform comparison incorporating whole genome bisulphite sequencing (WGBS), and high correlation between technical sample replicates, including between samples with DNA input levels below the manufacturer's recommendation. We provide a full assessment of probe content, evaluating genomic distribution and changes from previous array versions. We characterise EPICv2's new feature of replicated probes and provide recommendations as to the superior probes. In silico analysis of probe sequences demonstrates that probe cross-hybridisation remains a significant problem in EPICv2. By mapping the off-target sites at single nucleotide resolution and comparing with WGBS we show empirical evidence for preferential off-target binding.

CONCLUSIONS

Overall, we find EPICv2 a worthy successor to the previous Infinium methylation microarrays, however some technical issues remain. To support optimal EPICv2 data analysis we provide an expanded version of the EPICv2 manifest to aid researchers in understanding probe design, data processing, choosing appropriate probes for analysis and for integration with methylation datasets from previous versions of the Infinium Methylation BeadChip.

TSCRE: a comprehensive database for tumor-specific cis-regulatory elements

Cis-regulatory elements (CREs) and super cis-regulatory elements (SCREs) are non-coding DNA regions which influence the transcription of nearby genes and play critical roles in development. Dysregulated CRE and SCRE activities have been reported to alter the expression of oncogenes and tumor suppressors, thereby regulating cancer hallmarks. To address the strong need for a comprehensive catalogue of dysregulated CREs and SCREs in human cancers, we present TSCRE (http://tscre.zsqylab.com/), an open resource providing tumor-specific and cell type-specific CREs and SCREs derived from the re-analysis of publicly available histone modification profiles. Currently, TSCRE contains 1 864 941 dysregulated CREs and 68 253 dysregulated SCREs identified from 1366 human patient samples spanning 17 different cancer types and 9 histone marks. Over 95% of these elements have been validated in public resources. TSCRE offers comprehensive annotations for each element, including associated genes, expression patterns, clinical prognosis, somatic mutations, transcript factor binding sites, cancer-type specificity, and drug response. Additionally, TSCRE integrates pathway and transcript factor enrichment analyses for each study, enabling in-depth functional and mechanistic investigations. Furthermore, TSCRE provides an interactive interface for users to explore any CRE and SCRE of interest. We believe TSCRE will be a highly valuable platform for the community to discover candidate cancer biomarkers.

NCAD v1.0: a database for non-coding variant annotation and interpretation

The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.

Transgenerational epigenetic effects imposed by neonicotinoid thiacloprid exposure

Neonicotinoids are a widely used class of insecticides that are being applied in agricultural fields. We examined the capacity of a neonicotinoid, thiacloprid (thia), to induce transgenerational effects in male mice. Pregnant outbred Swiss female mice were exposed to thia at embryonic days E6.5-E15.5 using different doses. Testis sections were used for morphology analysis, ELISAs for testosterone level analysis, RT-qPCR and RNA-seq for gene expression analysis, MEDIP-seq and MEDIP-qPCR techniques for DNA methylation analysis, and Western blot for a protein analysis. The number of meiotic double-strand breaks and the number of incomplete synapsed chromosomes were higher in the thia 6-treated group of F3 males. Genome-wide analysis of DNA methylation in spermatozoa revealed that differentially methylated regions were found in all three generations at the promoters of germ cell reprogramming responsive genes and many superenhancers that are normally active in embryonic stem cells, testis, and brain. DNA methylation changes induced by thia exposure during embryonic period are preserved through several generations at important master regulator regions.

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thaliana and Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sites and chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysis and Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse

Enhancer RNAs (eRNAs) transcribed from distal active enhancers serve as key regulators in gene transcriptional regulation. The accumulation of eRNAs from multiple sequencing assays has led to an urgent need to comprehensively collect and process these data to illustrate the regulatory landscape of eRNAs. To address this need, we developed the eRNAbase (http://bio.liclab.net/eRNAbase/index.php) to store the massive available resources of human and mouse eRNAs and provide comprehensive annotation and analyses for eRNAs. The current version of eRNAbase cataloged 10 399 928 eRNAs from 1012 samples, including 858 human samples and 154 mouse samples. These eRNAs were first identified and uniformly processed from 14 eRNA-related experiment types manually collected from GEO/SRA and ENCODE. Importantly, the eRNAbase provides detailed and abundant (epi)genetic annotations in eRNA regions, such as super enhancers, enhancers, common single nucleotide polymorphisms, expression quantitative trait loci, transcription factor binding sites, CRISPR/Cas9 target sites, DNase I hypersensitivity sites, chromatin accessibility regions, methylation sites, chromatin interactions regions, topologically associating domains and RNA spatial interactions. Furthermore, the eRNAbase provides users with three novel analyses including eRNA-mediated pathway regulatory analysis, eRNA-based variation interpretation analysis and eRNA-mediated TF-target gene analysis. Hence, eRNAbase is a powerful platform to query, browse and visualize regulatory cues associated with eRNAs.

A Combinatorial Regulatory Platform Determines Expression of RNA Polymerase III Subunit RPC7α (POLR3G) in Cancer

RNA polymerase III (Pol III) subunit RPC7α, which is encoded by POLR3G in humans, has been linked to both tumor growth and metastasis. Accordantly, high POLR3G expression is a negative prognostic factor in multiple cancer subtypes. To date, the mechanisms underlying POLR3G upregulation have remained poorly defined. We performed a large-scale genomic survey of mRNA and chromatin signatures to predict drivers of POLR3G expression in cancer. Our survey uncovers positive determinants of POLR3G expression, including a gene-internal super-enhancer bound with multiple transcription factors (TFs) that promote POLR3G expression, as well as negative determinants that include gene-internal DNA methylation, retinoic-acid induced differentiation, and MXD4-mediated disruption of POLR3G expression. We show that novel TFs identified in our survey, including ZNF131 and ZNF207, functionally enhance POLR3G expression, whereas MXD4 likely obstructs MYC-driven expression of POLR3G and other growth-related genes. Integration of chromatin architecture and gene regulatory signatures identifies additional factors, including histone demethylase KDM5B, as likely influencers of POLR3G gene activity. Taken together, our findings support a model in which POLR3G expression is determined with multiple factors and dynamic regulatory programs, expanding our understanding of the circuitry underlying POLR3G upregulation and downstream consequences in cancer.

A body map of super-enhancers and their function in pig

Introduction

Super-enhancers (SEs) are clusters of enhancers that act synergistically to drive the high-level expression of genes involved in cell identity and function. Although SEs have been extensively investigated in humans and mice, they have not been well characterized in pigs.

Methods

Here, we identified 42,380 SEs in 14 pig tissues using chromatin immunoprecipitation sequencing, and statistics of its overall situation, studied the composition and characteristics of SE, and explored the influence of SEs characteristics on gene expression.

Results

We observed that approximately 40% of normal enhancers (NEs) form SEs. Compared to NEs, we found that SEs were more likely to be enriched with an activated enhancer and show activated functions. Interestingly, SEs showed X chromosome depletion and short interspersed nuclear element enrichment, implying that SEs play an important role in sex traits and repeat evolution. Additionally, SE-associated genes exhibited higher expression levels and stronger conservation than NE-associated genes. However, genes with the largest SEs had higher expression levels than those with the smallest SEs, indicating that SE size may influence gene expression. Moreover, we observed a negative correlation between SE gene distance and gene expression, indicating that the proximity of SEs can affect gene activity. Gene ontology enrichment and motif analysis revealed that SEs have strong tissue-specific activity. For example, the CORO2B gene with a brain-specific SE shows strong brain-specific expression, and the phenylalanine hydroxylase gene with liver-specific SEs shows strong liver-specific expression.

Discussion

In this study, we illustrated a body map of SEs and explored their functions in pigs, providing information on the composition and tissue-specific patterns of SEs. This study can serve as a valuable resource of gene regulatory and comparative analyses to the scientific community and provides a theoretical reference for genetic control mechanisms of important traits in pigs.

GC-biased gene conversion drives accelerated evolution of ultraconserved elements in mammalian and avian genomes

Ultraconserved elements (UCEs) are the most conserved regions among the genomes of evolutionarily distant species and are thought to play critical biological functions. However, some UCEs rapidly evolved in specific lineages, and whether they contributed to adaptive evolution is still controversial. Here, using an increased number of sequenced genomes with high taxonomic coverage, we identified 2191 mammalian UCEs and 5938 avian UCEs from 95 mammal and 94 bird genomes, respectively. Our results show that these UCEs are functionally constrained and that their adjacent genes are prone to widespread expression with low expression diversity across tissues. Functional enrichment of mammalian and avian UCEs shows different trends indicating that UCEs may contribute to adaptive evolution of taxa. Focusing on lineage-specific accelerated evolution, we discover that the proportion of fast-evolving UCEs in nine mammalian and 10 avian test lineages range from 0.19% to 13.2%. Notably, up to 62.1% of fast-evolving UCEs in test lineages are much more likely to result from GC-biased gene conversion (gBGC). A single cervid-specific gBGC region embracing the uc.359 allele significantly alters the expression of Nova1 and other neural-related genes in the rat brain. Combined with the altered regulatory activity of ancient gBGC-induced fast-evolving UCEs in eutherians, our results provide evidence that synergy between gBGC and selection shaped lineage-specific substitution patterns, even in the most constrained regulatory elements. In summary, our results show that gBGC played an important role in facilitating lineage-specific accelerated evolution of UCEs, and further support the idea that a combination of multiple evolutionary forces shapes adaptive evolution.

HiBrowser: an interactive and dynamic browser for synchronous Hi-C data visualization

With the development of chromosome conformation capture technology, the genome-wide investigation of higher-order chromatin structure by using high-throughput chromatin conformation capture (Hi-C) technology is emerging as an important component for understanding the mechanism of gene regulation. Considering genetic and epigenetic differences are typically used to explore the pathological reasons on the chromosome and gene level, visualizing multi-omics data and performing an intuitive analysis by using an interactive browser become a powerful and welcomed way. In this paper, we develop an effective sequence and chromatin interaction data display browser called HiBrowser for visualizing and analyzing Hi-C data and their associated genetic and epigenetic annotations. The advantages of HiBrowser are flexible multi-omics navigation, novel multidimensional synchronization comparisons and dynamic interaction system. In particular, HiBrowser first provides an out of the box web service and allows flexible and dynamic reconstruction of custom annotation tracks on demand during running. In order to conveniently and intuitively analyze the similarities and differences among multiple samples, such as visual comparisons of normal and tumor tissue samples, and pan genomes of multiple (consanguineous) species, HiBrowser develops a clone mode to synchronously display the genome coordinate positions or the same regions of multiple samples on the same page of visualization. HiBrowser also supports a pluralistic and precise search on correlation data of distal cis-regulatory elements and navigation to any region on Hi-C heatmap of interest according to the searching results. HiBrowser is a no-build tool, and could be easily deployed in local server. The source code is available at https://github.com/lyotvincent/HiBrowser.

Cis-Cardio: A comprehensive analysis platform for cardiovascular-relavant cis-regulation in human and mouse

Cis-regulatory elements are important molecular switches in controlling gene expression and are regarded as determinant hubs in the transcriptional regulatory network. Collection and processing of large-scale cis-regulatory data are urgent to decipher the potential mechanisms of cardiovascular diseases from a cis-regulatory element aspect. Here, we developed a novel web server, Cis-Cardio, which aims to document a large number of available cardiovascular-related cis-regulatory data and to provide analysis for unveiling the comprehensive mechanisms at a cis-regulation level. The current version of Cis-Cardio catalogs a total of 45,382,361 genomic regions from 1,013 human and mouse epigenetic datasets, including ATAC-seq, DNase-seq, Histone ChIP-seq, TF/TcoF ChIP-seq, RNA polymerase ChIP-seq, and Cohesin ChIP-seq. Importantly, Cis-Cardio provides six analysis tools, including region overlap analysis, element upstream/downstream analysis, transcription regulator enrichment analysis, variant interpretation, and protein-protein interaction-based co-regulatory analysis. Additionally, Cis-Cardio provides detailed and abundant (epi-) genetic annotations in cis-regulatory regions, such as super-enhancers, enhancers, transcription factor binding sites (TFBSs), methylation sites, common SNPs, risk SNPs, expression quantitative trait loci (eQTLs), motifs, DNase I hypersensitive sites (DHSs), and 3D chromatin interactions. In summary, Cis-Cardio is a valuable resource for elucidating and analyzing regulatory cues of cardiovascular-specific cis-regulatory elements. The platform is freely available at http://www.licpathway.net/Cis-Cardio/index.html.

Identification and comprehensive analysis of super-enhancer related genes involved in epithelial-to-mesenchymal transition in lung adenocarcinoma

Lung adenocarcinoma is a disease with a high mortality rate, and its mechanism is still unclear. Super-enhancers play an important role in gene expression and also affect the occurrence and development of lung adenocarcinoma, so more and more people pay attention to them. In order to explore the influence of super-enhancer related genes on tumor development, we identified super-enhancer regulated genes related to Epithelial-to-mesenchymal transition (EMT). By analyzing the single-cell sequencing data and the TCGA database of lung adenocarcinoma, we suggest that the up-regulation of TMSB10 in lung adenocarcinoma and its association with poor prognosis may be due to the regulation of super-enhancers during tumor cell metastasis. Using the TCGA lung adenocarcinoma data set, the samples were divided into TMSB10 high-expression group and low-expression group, and it was found that there were significant differences in immune infiltration between the high-expression group and the low-expression group. We parted 513 samples into eight TMSB10-related molecular subtypes using differentially expressed genes of high and low TMSB10 expression groups. We concentrated on four molecular subtypes with the most significant clusters, each with its own characteristics in terms of Immune cell infiltration, prognosis, or pathological stages. In order to predict the four molecular subtypes, we established a prediction model using random forest, and the external test results showed that the prediction accuracy of the model was 0.87. This study may provide potential help for the study of the mechanism of metastasis and invasion of lung adenocarcinoma cells and personalized treatment of lung adenocarcinoma.

Transcriptional regulation of amino acid metabolism by KDM2B, in the context of ncPRC1.1 and in concert with MYC and ATF4

Introduction

KDM2B encodes a JmjC domain-containing histone lysine demethylase, which functions as an oncogene in several types of tumors, including TNBC. This study was initiated to address the cancer relevance of the results of our earlier work, which had shown that overexpression of KDM2B renders mouse embryonic fibroblasts (MEFs) resistant to oxidative stress by regulating antioxidant mechanisms.

Methods

We mainly employed a multi-omics strategy consisting of RNA-Seq, quantitative TMT proteomics, Mass-spectrometry-based global metabolomics, ATAC-Seq and ChIP-seq, to explore the role of KDM2B in the resistance to oxidative stress and intermediary metabolism. These data and data from existing patient datasets were analyzed using bioinformatic tools, including exon-intron-split analysis (EISA), FLUFF and clustering analyses. The main genetic strategy we employed was gene silencing with shRNAs. ROS were measured by flow cytometry, following staining with CellROX and various metabolites were measured with biochemical assays, using commercially available kits. Gene expression was monitored with qRT-PCR and immunoblotting, as indicated.

Results

The knockdown of KDM2B in basal-like breast cancer cell lines lowers the levels of GSH and sensitizes the cells to ROS inducers, GSH targeting molecules, and DUB inhibitors. To address the mechanism of GSH regulation, we knocked down KDM2B in MDA-MB-231 cells and we examined the effects of the knockdown, using a multi-omics strategy. The results showed that KDM2B, functioning in the context of ncPRC1.1, regulates a network of epigenetic and transcription factors, which control a host of metabolic enzymes, including those involved in the SGOC, glutamate, and GSH metabolism. They also showed that KDM2B enhances the chromatin accessibility and expression of MYC and ATF4, and that it binds in concert with MYC and ATF4, the promoters of a large number of transcriptionally active genes, including many, encoding metabolic enzymes. Additionally, MYC and ATF4 binding sites were enriched in genes whose accessibility depends on KDM2B, and analysis of a cohort of TNBCs expressing high or low levels of KDM2B, but similar levels of MYC and ATF4 identified a subset of MYC targets, whose expression correlates with the expression of KDM2B. Further analyses of basal-like TNBCs in the same cohort, revealed that tumors expressing high levels of all three regulators exhibit a distinct metabolic signature that carries a poor prognosis.

Conclusions

The present study links KDM2B, ATF4, and MYC in a transcriptional network that regulates the expression of multiple metabolic enzymes, including those that control the interconnected SGOC, glutamate, and GSH metabolic pathways. The co-occupancy of the promoters of many transcriptionally active genes, by all three factors, the enrichment of MYC binding sites in genes whose chromatin accessibility depends on KDM2B, and the correlation of the levels of KDM2B with the expression of a subset of MYC target genes in tumors that express similar levels of MYC, suggest that KDM2B regulates both the expression and the transcriptional activity of MYC. Importantly, the concerted expression of all three factors also defines a distinct metabolic subset of TNBCs with poor prognosis. Overall, this study identifies novel mechanisms of SGOC regulation, suggests novel KDM2B-dependent metabolic vulnerabilities in TNBC, and provides new insights into the role of KDM2B in the epigenetic regulation of transcription.

A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome

Enhancers are non-coding DNA elements that play a crucial role in enhancing the transcription rate of a specific gene in the genome. Experiments for identifying enhancers can be restricted by their conditions and involve complicated, time-consuming, laborious, and costly steps. To overcome these challenges, computational platforms have been developed to complement experimental methods that enable high-throughput identification of enhancers. Over the last few years, the development of various enhancer computational tools has resulted in significant progress in predicting putative enhancers. Thus, researchers are now able to use a variety of strategies to enhance and advance enhancer study. In this review, an overview of machine learning (ML)-based prediction methods for enhancer identification and related databases has been provided. The existing enhancer-prediction methods have also been reviewed regarding their algorithms, feature selection processes, validation techniques, and software utility. In addition, the advantages and drawbacks of these ML approaches and guidelines for developing bioinformatic tools have been highlighted for a more efficient enhancer prediction. This review will serve as a useful resource for experimentalists in selecting the appropriate ML tool for their study, and for bioinformaticians in developing more accurate and advanced ML-based predictors.

LPAD: using network construction and label propagation to detect topologically associating domains from Hi-C data

With the development of chromosome conformation capture technique, the study of spatial conformation of a genome based on Hi-C technique has made a quantum leap. Previous studies reveal that genomes are folded into hierarchy of three-dimensional (3D) structures associated with topologically associating domains (TADs), and detecting TAD boundaries is of great significance in the chromosome-level analysis of 3D genome architecture. In this paper, we propose a novel TAD identification method, LPAD, which first extracts node correlations from global interactions of chromosomes based on the random walk with restart and then builds an undirected graph from Hi-C contact matrix. Next, LPAD designs a label propagation-based approach to discover communities and generates TADs. Experimental results verify the effectiveness and quality of TAD detections compared with existing methods. Furthermore, experimental evaluation of chromatin immunoprecipitation sequencing data shows that LPAD performs high enrichment of histone modifications remarkably nearby the TAD boundaries, and these results demonstrate LPAD's advantages on TAD identification accuracy.

Super-enhancer landscape rewiring in cancer: The epigenetic control at distal sites

Super-enhancers evolve as elements at the top of the hierarchical control of gene expression. They are important end-gatherers of signaling pathways that control stemness, differentiation or adaptive responses. Many epigenetic regulations focus on these regions, and not surprisingly, during the process of tumorigenesis, various alterations can account for their dysfunction. Super-enhancers are emerging as key drivers of the aberrant gene expression landscape that sustain the aggressiveness of cancer cells. In this review, we will describe and discuss about the structure of super-enhancers, their epigenetic regulation, and the major changes affecting their functionality in cancer.