Self-transcribing active regulatory region sequencing (STARR-seq) is a high-throughput sequencing method capable of simultaneously discovering and validating all enhancers within the genome. In this method, candidate sequences are inserted into plasmid vectors and electroporated into cells. Acting as both enhancers and target genes, the self-transcription of these sequences will also be enhanced by themselves. By sequencing the transcriptome and comparing the results with the non-inserted control, the locations and activity of enhancers can be determined. In traditional enhancer discovery strategies, the chromatin open regions and transcription active regions were sequenced and predicted as enhancers. However, the activity of these putative enhancers could only be validated one by one without a high-throughput method. STARR-seq solved this limitation, allowing simultaneous enhancers discovery and activity validation in a high-throughput manner. Since the introduction of STARR-seq, it has been widely used to discover enhancers and validate enhancer activity in a number of organisms and cells. In this review, we present the traditional enhancer prediction methods and the basic principles, development history, specific applications of STARR-seq, and its future prospects, aiming to provide a reference for researchers in related fields conducting enhancer studies.
自转录活性调节区测序(self-transcribing active regulatory region sequencing，STARR-seq)是一种可发现并同时验证全基因组增强子活性的高通量测序方法。其原理为：将待验证序列插入质粒载体并电转入细胞中，该序列在作为增强子提高靶基因转录的同时，其本身也作为靶基因被增强转录。通过对转录组进行测序，并对比未插入片段的测序结果，可获得增强子在基因组位置及活性的信息。在传统增强子研究方法中，通过对染色质开放区域和转录活性区域进行测序以预测增强子，但只能逐一验证预测结果，无法高通量验证增强子活性。STARR-seq技术解决了上述缺陷，可在对全基因组增强子高通量挖掘的同时，对其活性进行可靠的验证。自STARR-seq技术发明以来，已被广泛运用于不同物种与细胞中的增强子发现及活性验证研究。本文对传统增强子预测方法以及STARR-seq技术的基本原理、发展历史和具体运用进行了介绍，并对其发展前景进行展望，以期为后续增强子相关领域研究人员提供参考。.

Mouse FOXA1 and GATA4 are prototypes of pioneer factors, initiating liver cell development by binding to the N1 nucleosome in the enhancer of the ALB1 gene. Using cryoelectron microscopy (cryo-EM), we determined the structures of the free N1 nucleosome and its complexes with FOXA1 and GATA4, both individually and in combination. We found that the DNA-binding domains of FOXA1 and GATA4 mainly recognize the linker DNA and an internal site in the nucleosome, respectively, whereas their intrinsically disordered regions interact with the acidic patch on histone H2A-H2B. FOXA1 efficiently enhances GATA4 binding by repositioning the N1 nucleosome. In vivo DNA editing and bioinformatics analyses suggest that the co-binding mode of FOXA1 and GATA4 plays important roles in regulating genes involved in liver cell functions. Our results reveal the mechanism whereby FOXA1 and GATA4 cooperatively bind to the nucleosome through nucleosome repositioning, opening chromatin by bending linker DNA and obstructing nucleosome packing.

Enhancers are crucial in gene expression regulation, dictating the specificity and timing of transcriptional activity, which highlights the importance of their identification for unravelling the intricacies of genetic regulation. Therefore, it is critical to identify enhancers and their strengths. Repeated sequences in the genome are repeats of the same or symmetrical fragments. There has been a great deal of evidence that repetitive sequences contain enormous amounts of genetic information. Thus, We introduce the W2V-Repeated Index, designed to identify enhancer sequence fragments and evaluates their strength through the analysis of repeated K-mer sequences in enhancer regions. Utilizing the word2vector algorithm for numerical conversion and Manta Ray Foraging Optimization for feature selection, this method effectively captures the frequency and distribution of K-mer sequences. By concentrating on repeated K-mer sequences, it minimizes computational complexity and facilitates the analysis of larger K values. Experiments indicate that our method performs better than all other advanced methods on almost all indicators.

Histone modifications, known as histone marks, are pivotal in regulating gene expression within cells. The vast array of potential combinations of histone marks presents a considerable challenge in decoding the regulatory mechanisms solely through biological experimental approaches. To overcome this challenge, we have developed a method called CatLearning. It utilizes a modified convolutional neural network architecture with a specialized adaptation Residual Network to quantitatively interpret histone marks and predict gene expression. This architecture integrates long-range histone information up to 500Kb and learns chromatin interaction features without 3D information. By using only one histone mark, CatLearning achieves a high level of accuracy. Furthermore, CatLearning predicts gene expression by simulating changes in histone modifications at enhancers and throughout the genome. These findings help comprehend the architecture of histone marks and develop diagnostic and therapeutic targets for diseases with epigenetic changes.

Gene targeting (GT) allows precise manipulation of genome sequences, such as knock-ins and sequence substitutions, but GT in seed plants remains a challenging task. Engineered sequence-specific nucleases (SSNs) are known to facilitate GT via homology-directed repair (HDR) in organisms. Here, we demonstrate that Cas12a and a temperature-tolerant Cas12a variant (ttCas12a) can efficiently establish precise and heritable GT at two loci in Arabidopsis thaliana (Arabidopsis) through a sequential transformation strategy. As a result, ttCas12a showed higher GT efficiency than unmodified Cas12a. In addition, the efficiency of transcriptional and translational enhancers for GT via sequential transformation strategy was also investigated. These enhancers and their combinations were expected to show an increase in GT efficiency in the sequential transformation strategy, similar to previous reports of all-in-one strategies, but only a maximum twofold increase was observed. These results indicate that the frequency of double strand breaks (DSBs) at the target site is one of the most important factors determining the efficiency of genetic GT in plants. On the other hand, a higher frequency of DSBs does not always lead to higher efficiency of GT, suggesting that some additional factors are required for GT via HDR. Therefore, the increase in DSB can no longer be expected to improve GT efficiency, and a new strategy needs to be established in the future. This research opens up a wide range of applications for precise and heritable GT technology in plants.

MDS1 and EVI1 complex locus (MECOM), a transcription factor encoding several variants, has been implicated in progression of ovarian cancer. The function of regulatory regions in regulating MECOM expression in ovarian cancer is not fully understood. In this study, MECOM expression was evaluated in ovarian cancer cell lines treated with bromodomain and extraterminal (BET) inhibitor JQ-1. Oncogenic phenotypes were assayed using assays of CCK-8, colony formation, wound-healing and transwell. Oncogenic phenotypes were estimated in stable sgRNA-transfected OVCAR3 cell lines. Xenograft mouse model was assayed via subcutaneous injection of enhancer-deleted OVCAR3 cell lines. The results displayed that expression of MECOM is downregulated in cell lines treated with JQ-1. Data from published ChIP-sequencing (H3K27Ac) in 3 ovarian cancer cell lines displayed a potential enhancer around the first exon. mRNA and protein expression were downregulated in OVCAR3 cells after deletion of the MECOM enhancer. Similarly, oncogenic phenotypes both in cells and in the xenograft mouse model were significantly attenuated. This study demonstrates that JQ-1 can inhibit the expression of MECOM and tumorigenesis. Deletion of the enhancer activity of MECOM has an indispensable role in inhibiting ovarian cancer progress, which sheds light on a promising opportunity for ovarian cancer treatment through the application of this non-coding DNA deletion.

BACKGROUND: Recognition of enhancer-promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources. Therefore, more and more studies are being done on developing computational techniques, particularly using deep learning and other machine learning techniques, to address such problems. Unfortunately, the majority of current computational methods are based on convolutional neural networks, recurrent neural networks, or a combination of them, which don\'t take into consideration contextual details and the long-range interactions between the enhancer and promoter sequences. A new transformer-based model called EPI-Trans is presented in this study to overcome the aforementioned limitations. The multi-head attention mechanism in the transformer model automatically learns features that represent the long interrelationships between enhancer and promoter sequences. Furthermore, a generic model is created with transferability that can be utilized as a pre-trained model for various cell lines. Moreover, the parameters of the generic model are fine-tuned using a particular cell line dataset to improve performance.
RESULTS: Based on the results obtained from six benchmark cell lines, the average AUROC for the specific, generic, and best models is 94.2%, 95%, and 95.7%, while the average AUPR is 80.5%, 66.1%, and 79.6% respectively.
CONCLUSIONS: This study proposed a transformer-based deep learning model for EPI prediction. The comparative results on certain cell lines show that EPI-Trans outperforms other cutting-edge techniques and can provide superior performance on the challenge of recognizing EPI.

BACKGROUND: Enhancers are important gene regulatory elements that promote the expression of critical genes in development and disease. Aberrant enhancer can modulate cancer risk and activate oncogenes that lead to the occurrence of various cancers. However, the underlying mechanism of most enhancers in cancer remains unclear. Here, we aim to explore the function and mechanism of a crucial enhancer in melanoma.
METHODS: Multi-omics data were applied to identify an enhancer (enh17) involved in melanoma progression. To evaluate the function of enh17, CRISPR/Cas9 technology were applied to knockout enh17 in melanoma cell line A375. RNA-seq, ChIP-seq and Hi-C data analysis integrated with luciferase reporter assay were performed to identify the potential target gene of enh17. Functional experiments were conducted to further validate the function of the target gene ETV4. Multi-omics data integrated with CUT&Tag sequencing were performed to validate the binding profile of the inferred transcription factor STAT3.
RESULTS: An enhancer, named enh17 here, was found to be aberrantly activated and involved in melanoma progression. CRISPR/Cas9-mediated deletion of enh17 inhibited cell proliferation, migration, and tumor growth of melanoma both in vitro and in vivo. Mechanistically, we identified ETV4 as a target gene regulated by enh17, and functional experiments further support ETV4 as a target gene that is involved in cancer-associated phenotypes. In addition, STAT3 acts as a transcription factor binding with enh17 to regulate the transcription of ETV4.
CONCLUSIONS: Our findings revealed that enh17 plays an oncogenic role and promotes tumor progression in melanoma, and its transcriptional regulatory mechanisms were fully elucidated, which may open a promising window for melanoma prevention and treatment.

The abnormality of the p53 tumor suppressor is crucial in lung cancer development, because p53 regulates target gene promoters to combat cancer. Recent studies have shown extensive p53 binding to enhancer elements. However, whether p53 exerts a tumor suppressor role by shaping the enhancer landscape remains poorly understood. In the current study, we employed several functional genomics approaches to assess the enhancer activity at p53 binding sites throughout the genome based on our established TP53 knockout (KO) human bronchial epithelial cells (BEAS-2B). A total of 943 active regular enhancers and 370 super-enhancers (SEs) disappeared upon the deletion of p53, indicating that p53 modulates the activity of hundreds of enhancer elements. We found that one p53-dependent SE, located on chromosome 9 and designated as KLF4-SE, regulated the expression of the Krüppel-like factor 4 ( KLF4) gene. Furthermore, the deletion of p53 significantly decreased the KLF4-SE enhancer activity and the KLF4 expression, but increased colony formation ability in the nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone-induced cell transformation model. Subsequently, in TP53 KO cells, the overexpression of KLF4 partially reversed the increased clonogenic capacity caused by p53 deficiency. Consistently, KLF4 expression also decreased in lung cancer tissues and cell lines. It appeared that overexpression of KLF4 significantly suppressed the proliferation and migration of lung cancer cells. Collectively, our results suggest that the regulation of enhancer formation and activity by p53 is an integral component of the p53 tumor suppressor function. Therefore, our findings offer some novel insights into the regulation mechanism of p53 in lung oncogenesis and introduce a new strategy for screening therapeutic targets.

Transcriptional dysregulation of genes is a hallmark of tumors and can serve as targets for cancer drug development. However, it is extremely challenging to develop small-molecule inhibitors to target abnormally expressed transcription factors (TFs) except for the nuclear receptor family of TFs. Little is known about the interaction between TFs and transcription cofactors in gastroesophageal adenocarcinoma (GEA) or the therapeutic effects of targeting TF and transcription cofactor complexes. In this study, we found that ETS homologous factor (EHF) expression is promoted by a core transcriptional regulatory circuitry (CRC), specifically ELF3-KLF5-GATA6, and interference with its expression suppressed the malignant biological behavior of GEA cells. Importantly, we identified Ajuba LIM protein (AJUBA) as a new coactivator of EHF that cooperatively orchestrates transcriptional network activity in GEA. Furthermore, we identified KRAS signaling as a common pathway downstream of EHF and AJUBA. Applicably, dual targeting of EHF and AJUBA by lipid nanoparticles cooperatively attenuated the malignant biological behaviors of GEA in vitro and in vivo. In conclusion, EHF is upregulated by the CRC and promotes GEA malignancy by interacting with AJUBA through the KRAS pathway. Targeting of both EHF and its coactivator AJUBA through lipid nanoparticles is a novel potential therapeutic strategy.