Methods for ChIP-seq analysis: A practical workflow and advanced applications

KLF11/TMEM87B promoted the occurrence of glioma and decreased TMZ sensitivity

Whether KLF11 functions as a tumor promoter or inhibitor depends on the type of tumor. Our previous reports revealed the oncogenic role of KLF11 in glioma. In this study, TMEM87B was identified as a downstream gene of KLF11 through ChIP-seq assay, and the binding of KLF11 to the promoter area of TMEM87B was demonstrated using luciferase assay. KLF11 positively regulated the expression of TMEM87B mRNA and protein in glioma cell lines. Furthermore. TMEM87B was highly expressed in glioma samples, which indicated a poor prognosis in glioma patients. The elimination of TMEM87B reduced the proliferation and migration cell viability, along with the formation of tumor spheroids, while increasing TMZ sensitivity, whereas the overexpression of TMEM87B had the opposite effect. Furthermore, both the knockdown of TMEM87B and TMZ treatment could retard tumor growth in xenograft mice, and their combination significantly reduced tumor size and weight. Our findings identified the effects of the KLF11/ TMEM87B axis on glioma progression and TMZ sensitivity, which could provide new targets for glioma therapy.

Harnessing technologies to unravel gastric cancer heterogeneity

Gastric cancer arises from complex carcinogenic factor interactions, with limited treatment options due to the lack of targetable driver gene mutations and significant tumor heterogeneity. Recent studies have provided promising novel approaches to improve our understanding of gastric cancer heterogeneity through integrated characterization, combining genomics with emerging technologies. Delineating the molecular changes and targeting specific molecular subtypes will enhance the efficacy of gastric cancer treatment and improve clinical outcomes. This review provides a comprehensive overview of current technologies used in gastric cancer research, highlighting key discoveries and treatment strategies driven by these innovations. Finally, we discuss the emerging technology-guided directions and potential breakthroughs that could enhance the understanding of gastric cancer tumor heterogeneity, ultimately improving clinical outcomes.

Benchmark of chromatin-protein interaction methods in haploid round spermatids

Introduction

Chromatin-protein interactions are fundamental for regulation of gene transcription. While chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) has long been the gold standard for mapping these interactions, emerging techniques such as CUT&RUN and CUT&Tag, which offer advantages such as low-input requirements and high signal-to-noise ratios, have aroused great attention. However, research addressing the potential biases introduced by enzyme-based tagmentation approaches and comparative assessment with ChIP-seq remain absent.

Methods

This study aims to systematically evaluate and compare the performance of ChIP-seq, CUT&Tag, and CUT&RUN for profiling genome-wide transcription factors and histone modification binding.

Results

Our analysis revealed that all three methods reliably detect histone modifications and transcription factor enrichment, with CUT&Tag standing out for its comparatively higher signal-to-noise ratio. Detailed peak comparison revealed unique and overlapping enrichment among the three techniques. Additionally, CUT&Tag can identify novel CTCF peaks compared with the other two methods. A strong correlation was observed between CUT&Tag signal intensity and chromatin accessibility, highlighting its ability to generate high-resolution signals in accessible regions.

Discussion

The systematic comparison summarizes the differences between CUT&Tag and CUT&RUN in terms of the signal-to-noise ratio and bias toward accessible chromatin. Considering the experimental procedures, signal specificity, and inherent biases, we recommend tailoring the choice of method to the type of chromatin-protein interaction under study. CUT&Tag offers a promising alternative for applications requiring high sensitivity and reduced background noise.

An extensive review on infectious disease diagnosis using machine learning techniques and next generation sequencing: State-of-the-art and perspectives

Infectious diseases, including tuberculosis (TB), HIV/AIDS, and emerging pathogens like COVID-19 pose severe global health challenges due to their rapid spread and significant morbidity and mortality rates. Next-generation sequencing (NGS) and machine learning (ML) have emerged as transformative technologies for enhancing disease diagnosis and management.

OBJECTIVE

This review aims to explore integrating ML techniques with NGS for diagnosing infectious diseases, highlighting their effectiveness and identifying existing challenges.

METHODS

A comprehensive literature review spanning the past decade was conducted using reputable databases, including IEEE Xplore, PubMed, Scopus, SpringerLink, and Science Direct. Research papers, articles, and conference proceedings meeting stringent quality criteria were analysed to assess the performance of ML algorithms applied to NGS and metagenomic NGS (mNGS) data.

RESULTS

The findings reveal that ML algorithms, such as deep neural networks (DNNs), support vector machines (SVM), and K-nearest neighbours (KNN), achieve high accuracy rates, often exceeding 95 %, in diagnosing infectious diseases. Deep learning methods excel in genomic and metagenomic data analysis, while traditional algorithms like Gaussian mixture models (GMM) also demonstrate robust classification capabilities. Challenges include reliance on single data types and difficulty distinguishing closely related pathogens.

CONCLUSION

The integration of ML and NGS significantly advances infectious disease diagnosis, offering rapid and precise detection capabilities. Addressing current limitations can further enhance the effectiveness of these technologies, ultimately improving global public health outcomes.

Unraveling ER dimerization dynamics in endocrine disruption based on a BRET-focused approach

Endocrine-disrupting chemicals (EDCs) are exogenous compounds that interact with the estrogen receptor (ER), thereby disrupting estrogen-mediated signaling. In a previous study, we employed a bioluminescence resonance energy transfer (BRET) system to assess ER dimerization for detecting EDCs. To further determine whether the BRET assay could be used independently to identify EDCs, we investigated ER-EDC interactions before and after dimerization. Results from isothermal titration calorimetry (ITC) and dynamic light scattering (DLS) revealed that ER dimerization can be mediated by EDCs. Consequently, the BRET assay proved effective in detecting dimerization and clarifying its relevance to EDC-induced signaling disruption. Additionally, to examine EDC-induced transcriptional changes, we performed chromatin immunoprecipitation sequencing (ChIP-seq), followed by gene ontology (GO) analysis. These analyses demonstrated that EDCs affect various signaling pathways, including those involved in antibody-dependent cytotoxicity, bone morphogenetic protein (BMP) signaling in cardiac induction, and hepatocyte growth factor receptor signaling. Overall, this study elucidates the molecular mechanisms by which EDCs influence ER dimerization and signaling. These findings highlight the utility of the BRET-based assay for EDC detection and contribute to a deeper understanding of the systemic effects of EDCs on endocrine disruption.

Epigenetics factors in schizophrenia: future directions for etiologic and therapeutic study approaches

Schizophrenia is a complex, heterogeneous, and highly disabling severe mental disorder whose pathogenesis has not yet been fully elucidated. Epigenetics, as a bridge between genetic and environmental factors, plays an important role in the pathophysiology of schizophrenia. Over the past decade, epigenetic-wide association studies have rapidly become an important branch of psychiatric research, especially in deciphering the molecular mechanisms of schizophrenia. This review systematically analyzes recent advances in epigenome-wide association studies (EWAS) of schizophrenia, focusing on technological developments. We synthesize findings from large-scale EWAS alongside emerging evidence on DNA methylation patterns, histone modifications, and regulatory networks, emphasizing their roles in disease mechanisms and treatment responses. In addition, this review provides a prospective outlook, evaluating the impact that technological developments may have on future studies of schizophrenia. With the continuous advancement of high-throughput sequencing technology and the increasing maturity of big data analysis methods, epigenetics is expected to have a significant impact on the early diagnosis, prognosis assessment and even personalized treatment of schizophrenia.

Dataset of histone H3K4me2 modified genes in the liver of female Sprague-Dawley rats with chronic antipsychotic drugs of olanzapine or clozapine

Epigenetic histone modifications have been found to be associated with the development of metabolic disorders. However, the potential contribution of these modifications to metabolic disturbances induced by chronic treatment with second-generation antipsychotic drugs (SGAs) remains unclear. This dataset presents chromatin immunoprecipitation (ChIP) data on H3K4me methylation in hepatic tissue of rats treated with olanzapine or clozapine for 9 weeks for a comprehensive view of the effects of SGAs on H3K4me methylation in an animal model. It offers new insights into the epigenetic mechanisms underlying SGAs-induced metabolic side effects.

A semidominant point mutation of Mediator tail subunit MED5b in Arabidopsis leads to altered enrichment of H3K27me3 and reduced expression of targets of MYC2

The Mediator complex coordinates regulatory input for transcription driven by RNA polymerase II in eukaryotes. reduced epidermal fluorescence4-3 (ref4-3) is a semidominant mutation that results in a single amino acid substitution in the Mediator tail subunit Med5b. Previous characterization of ref4-3 revealed altered expression of a variety of loci in Arabidopsis, including those contributing to phenylpropanoid biosynthesis. Examination of existing RNA-seq data indicated that loci enriched for the transcriptionally repressive chromatin modification H3K27me3 are overrepresented among genes that are misregulated in ref4-3. We used ChIP-seq and RNA-seq to examine the possibility that perturbation of H3K27me3 homeostasis in ref4-3 plants contributed to altered transcript levels. We observed that ref4-3 results in a modest global reduction of H3K27me3 at enriched loci and that this reduction is not dependent on gene expression; however, altered H3K27me3 was not strongly predictive of altered expression in ref4-3 plants. Instead, our analyses revealed a substantial enrichment of targets of the MYC2 transcriptional regulator among genes that exhibit decreased expression in ref4-3. Consistent with previous characterization of ref4-3, we observed that ref4-3-dependent decreased expression of MYC2 targets can be suppressed by loss of another Mediator tail subunit, MED25. This observation is consistent with previous biochemical characterization of MYC2. Our data highlight the diverse and distinct impacts that a single amino acid change in the tail subunit of Mediator can have on transcriptional circuits and raise the prospect that Mediator directly contributes to H3K27me3 homeostasis in plants.

Recent advances in basic research on prostate cancer: Where we are heading?

In the over 80 years since androgens were found to play a pivotal role in prostate cancer (PCa) progression, androgen deprivation therapy (ADT) has been a cornerstone in treating advanced PCa. Castration-resistant PCa persists, however, with some of these tumors evolving to androgen receptor (AR)-independent forms like neuroendocrine PCa. The development of novel diagnostic and therapeutic approaches to PCa is therefore crucial. This review provides an overview of recent basic research in PCa, focusing on two main areas: PCa cells and their tumor microenvironments. The first section describes current knowledge on the intricate mechanisms of AR signaling pathways, emphasizing the roles of coactivators and chromatin state alterations in gene regulation. Genomic analyses have revealed recurrent mutations and copy number alterations critical for precision medicine. Liquid biopsy has become a promising tool for real-time tumor monitoring, identifying genetic alterations in circulating-tumor DNA or extracellular vesicles. The second section describes the tumor microenvironment of PCa, highlighting its immunosuppressive landscape and the potential of combining ADT with immunotherapy. Advanced techniques, including single-cell RNA sequencing and spatial transcriptomics offer insights into cellular heterogeneity and interactions within the tumor microenvironment, paving the way for novel therapeutic strategies. Integration of these diverse research areas will provide a comprehensive understanding of the current state and future directions of PCa research, underscoring the importance of personalized medicine and the dynamic nature of cancer treatment strategies.

Enhancer Enh483 regulates myoblast proliferation and differentiation of buffalo myoblasts by targeting FAXC

A detailed understanding of the precise regulatory mechanisms governing buffalo skeletal muscle is crucial for improving meat quality and yield. Proper skeletal muscle fate decisions necessitate the accurate regulation of key enhancers. This study screened nine potential enhancers linked to muscle development by analysing ATAC-seq data from buffalo myoblasts during the proliferative and differentiative phases. The enhancer activity of these candidates was confirmed in buffalo myoblasts, C2C12, and human skeletal muscle myoblasts using a dual-luciferase reporter system. The CRISPRi system and RT-qPCR were used to test the effects of 9 candidate enhancers on buffalo myoblasts. The active enhancer, Enh483, was selected based on its significant impact. Upon successful inhibition of Enh483 using CRISPRi, decreases in the expression of buffalo myogenic proliferation marker genes (PCNA, CyclinD1, and CDK2) were observed via RT-qPCR and Western blot. Subsequent proliferation assays using CCK-8 and EdU confirmed the promotive effect of Enh483 on buffalo myogenic cell proliferation. Following a 5-day differentiation induction period, changes in the expression of differentiation marker genes (MyoD1, MyoG, and MyHC) were analysed using RT-qPCR and Western blot. Additionally, fused myotube numbers were quantified, and the impact of Enh483 on buffalo myogenic cell differentiation was assessed through immunofluorescence. Our findings indicate that Enh483 facilitates buffalo myogenic cell differentiation. Further interaction analysis utilising 3C-PCR revealed a direct association between Enh483 and the FAXC promoter. In summary, the results from this study lay a foundational framework for deciphering the intricate regulatory mechanisms underpinning buffalo muscle development.

EnsembleSE: identification of super-enhancers based on ensemble learning

Super-enhancers (SEs) are typically located in the regulatory regions of genes, driving high-level gene expression. Identifying SEs is crucial for a deeper understanding of gene regulatory networks, disease mechanisms, and the development and physiological processes of organisms, thus exerting a profound impact on research and applications in the life sciences field. Traditional experimental methods for identifying SEs are costly and time-consuming. Existing methods for predicting SEs based solely on sequence data use deep learning for feature representation and have achieved good results. However, they overlook biological features related to physicochemical properties, leading to low interpretability. Additionally, the complex model structure often requires extensive labeled data for training, which limits their further application in biological data. In this paper, we integrate the strengths of different models and proposes an ensemble model based on an integration strategy to enhance the model's generalization ability. It designs a multi-angle feature representation method that combines local structure and global information to extract high-dimensional abstract relationships and key low-dimensional biological features from sequences. This enhances the effectiveness and interpretability of the model's input features, providing technical support for discovering cell-specific and species-specific patterns of SEs. We evaluated the performance on both mouse and human datasets using five metrics, including area under the receiver operating characteristic curve accuracy, and others. Compared to the latest models, EnsembleSE achieved an average improvement of 4.5% in F1 score and an average improvement of 8.05% in recall, demonstrating the robustness and adaptability of the model on a unified test set. Source codes are available at https://github.com/2103374200/EnsembleSE-main.

Gene Doping Detection From the Perspective of 3D Genome

Since the early 20th century, the concept of doping was first introduced. To achieve better athletic performance, chemical substances were used. By the mid-20th century, it became gradually recognized that the illegal use of doping substances can seriously endangered athletes' health and compromised the fairness of sports competitions. Over the past 30 years, the World Anti-Doping Agency (WADA) has established corresponding rules and regulations to prohibit athletes from using doping substances or restrict the use of certain drugs, and isotope, chromatography, and mass spectrometry techniques were accredited to detect doping substances. With the development of gene editing technology, many genetic diseases have been effectively treated, but enabled by the same technology, doping has also the potential to pose a threat to sports in the form of gene doping. WADA has explicitly indicated gene doping in the Prohibited List as a prohibited method (M3) and approved qPCR detection. However, gene doping can easily evade detection, if the target genes' upstream regulatory elements are considered, the task became more challenging. Hi-C experiment driven 3D genome technology, through perspectives such as topologically associating domain (TAD) and chromatin loop, provides a more comprehensive and in-depth understanding of gene regulation and expression, thereby better preventing the potential use of 3D genome level gene doping. In this work, we will explore gene doping from a different perspective by analyzing recent studies on gene doping and explore related genes under 3D genome.

Airborne fine particulate matter exposure induces transcriptomic alterations resembling asthmatic signatures: insights from integrated omics analysis

Fine particulate matter (PM2.5), an atmospheric pollutant that settles deep in the respiratory tract, is highly harmful to human health. Despite its well-known impact on lung function and its ability to exacerbate asthma, the molecular basis of this effect is not fully understood. This integrated transcriptomic and epigenomic data analysis from publicly available datasets aimed to determine the impact of PM2.5 exposure and its association with asthma in human airway epithelial cells. Differential gene expression and binding analyses identified 349 common differentially expressed genes and genes associated with differentially enriched H3K27ac regions in both conditions. Co-expression network analysis revealed three preserved modules (Protein Folding, Cell Migration, and Hypoxia Response) significantly correlated with PM2.5 exposure and preserved in asthma networks. Pathways dysregulated in both conditions included epithelial function, hypoxia response, interleukin-17 and TNF signaling, and immune/inflammatory processes. Hub genes like TGFB2, EFNA5, and PFKFB3 were implicated in airway remodeling, cell migration, and hypoxia-induced glycolysis. These findings elucidate common altered expression patterns and processes between PM2.5 exposure and asthma, helping to understand their molecular connection. This provides guidance for future research to utilize them as potential biomarkers or therapeutic targets and generates evidence supporting the need for implementing effective air quality management strategies.

The Evolution of Next-Generation Sequencing Technologies

The genetic information that dictates the structure and function of all life forms is encoded in the DNA. In 1953, Watson and Crick first presented the double helical structure of a DNA molecule. Their findings unearthed the desire to elucidate the exact composition and sequence of DNA molecules. Discoveries and the subsequent development and optimization of techniques that allowed for deciphering the DNA sequence has opened new doors in research, biotech, and healthcare. The application of high-throughput sequencing technologies in these industries has positively impacted and will continue to contribute to the betterment of humanity and the global economy. Improvements, such as the use of radioactive molecules for DNA sequencing to the use of florescent dyes and the implementation of polymerase chain reaction (PCR) for amplification, led to sequencing a few hundred base pairs in days, to automation, where sequencing of thousands of base pairs in hours became possible. Significant advances have been made, but there is still room for improvement. Here, we look at the history and the technology of the currently available next-generation sequencing platforms and the possible applications of such technologies to biomedical research and beyond.

ChIP-seq and structural analyses delineating the regulatory mechanism of master regulator EsrB in Edwardsiella piscicida

As a response regulator of the EsrA-EsrB two-component system, EsrB is conserved in Hafniaceae and plays a crucial role in virulence and pathogenicity. EsrB possesses DNA binding abilities, enabling it to regulate the transcription of virulence genes to confront different stresses and achieve systematic infections. Here, ChIP-seq analysis of EsrB in Dulbecco's Modified Eagle's Medium (DMEM) (mimicking in vivo environments) revealed that EsrB preferred to bind to virulence-associated promoters with a distinct 7'-4-7'' pseudopalindromic DNA motif and interact with metabolic-related promoters with a high AT DNA motif. The crystal structure of the C-terminal of EsrB (EsrBC) was solved at 2.20-Å resolution. Specifically, Lys181 enabled the DNA-binding affinity of EsrB and promoted the in vitro and in vivo pathogenicity of Edwardsiella piscicida. Moreover, EsrB directly regulated the expression of genes associated with basal metabolism, including iron and tricarboxylic acid (TCA) cycles. Furthermore, EsrB enhanced iron transport capability and the enzyme activity of succinate dehydrogenase and pyruvate dehydrogenase in DMEM. Collectively, our structural and ChIP-seq analysis provides valuable insights into the DNA binding mechanism of EsrB which will facilitate our understanding of EsrB coordinating virulence and basal metabolism gene expression.

IMPORTANCE

As a crucial virulence regulator, EsrB possesses a LuxR-like superfamily domain at the C-terminal, which is conserved within the canonical NarL family regulators. Due to its critically important role in virulence and pathogenicity in fish hosts, the DNA binding ability has been believed to allow EsrB to regulate genes associated with the invasion process of host cells and basal metabolism in response to environmental stimuli. The lack of EsrB's crystal structure has been a major obstacle in understanding the molecular mechanisms of EsrB-DNA interaction which choreographs EsrB-mediated pathogenic behavior. Here, we conducted ChIP-seq and solved the crystal structure of the C-terminal of EsrB (EsrBC) at 2.20-Å resolution, which revealed that EsrB preferred to bind to virulence-associated promoters with a distinct 7'-4-7' pseudopalindromic DNA motif and interacted with metabolic-related promoters with a high AT DNA motif in Dulbecco's Modified Eagle's Medium (DMEM) (mimicking in vivo environments). Our results facilitate a detailed understanding of EsrB's regulatory role in Edwardsiella piscicida pathogenesis and expand our knowledge of virulence regulators in the family Hafniaceae.

Discoveries by the genome profiling, symbolic powers of non-next generation sequencing methods

Next-generation sequencing and other sequencing approaches have made significant progress in DNA analysis. However, there are indispensable advantages in the nonsequencing methods. They have their justifications such as being speedy, cost-effective, multi-applicable, and straightforward. Among the nonsequencing methods, the genome profiling method is worthy of reviewing because of its high potential. This article first reviews its basic properties, highlights the key concept of species identification dots (spiddos), and then summarizes its various applications.

Innovative Insights into Single-Cell Technologies and Multi-Omics Integration in Livestock and Poultry

In recent years, single-cell RNA sequencing (scRNA-seq) has marked significant strides in livestock and poultry research, especially when integrated with multi-omics approaches. These advancements provide a nuanced view into complex regulatory networks and cellular dynamics. This review outlines the application of scRNA-seq in key species, including poultry, swine, and ruminants, with a focus on outcomes related to cellular heterogeneity, developmental biology, and reproductive mechanisms. We emphasize the synergistic power of combining scRNA-seq with epigenomic, proteomic, and spatial transcriptomic data, enhancing molecular breeding precision, optimizing health management strategies, and refining production traits in livestock and poultry. The integration of these technologies offers a multidimensional approach that not only broadens the scope of data analysis but also provides actionable insights for improving animal health and productivity.

Epigenetic biomarkers in Alzheimer's disease: Diagnostic and prognostic relevance

Alzheimer's disease (AD) is a leading cause of cognitive decline in the aging population, presenting a critical need for early diagnosis and effective prognostic tools. Epigenetic modifications, including DNA methylation, histone modifications, and non-coding RNAs, have emerged as promising biomarkers for AD due to their roles in regulating gene expression and potential for reversibility. This review examines the current landscape of epigenetic biomarkers in AD, emphasizing their diagnostic and prognostic relevance. DNA methylation patterns in genes such as APP, PSEN1, and PSEN2 are highlighted for their strong associations with AD pathology. Alterations in DNA methylation at specific CpG sites have been consistently observed in AD patients, suggesting their utility in early detection. Histone modifications, such as acetylation and methylation, also play a crucial role in chromatin remodelling and gene expression regulation in AD. Dysregulated histone acetylation and methylation have been linked to AD progression, making these modifications valuable biomarkers. Non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), further contribute to the epigenetic regulation in AD. miRNAs can modulate gene expression post-transcriptionally and have been found in altered levels in AD, while lncRNAs can influence chromatin structure and gene expression. The presence of these non-coding RNAs in biofluids like blood and cerebrospinal fluid positions them as accessible and minimally invasive biomarkers. Technological advancements in detecting and quantifying epigenetic modifications have propelled the field forward. Techniques such as next-generation sequencing, bisulfite sequencing, and chromatin immunoprecipitation assays offer high sensitivity and specificity, enabling the detailed analysis of epigenetic changes in clinical samples. These tools are instrumental in translating epigenetic research into clinical practice. This review underscores the potential of epigenetic biomarkers to enhance the early diagnosis and prognosis of AD, paving the way for personalized therapeutic strategies and improved patient outcomes. The integration of these biomarkers into clinical workflows promises to revolutionize AD management, offering hope for better disease monitoring and intervention.

Identification of co-localised transcription factors based on paired motifs analysis

The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA cis-regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co-localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell-line-specific patterns. Finally, in human prostate cancer cells (PC-3) and human prostate normal cells (RWPE-2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC-3 and RWPE-2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC-3 cancer cells.

Genome-Wide Mapping of Autonomously Replicating Sequences in the Marine Diatom Phaeodactylum tricornutum

Autonomously replicating sequences (ARSs) are important accessories in episomal vectors that allow them to be replicated and stably maintained within transformants. Despite their importance, no information on ARSs in diatoms has been reported. Therefore, we attempted to identify ARS candidates in the model diatom, Phaeodactylum tricornutum, via chromatin immunoprecipitation sequencing. In this study, subunits of the origin recognition complex (ORC), ORC2 and ORC4, were used to screen for ARS candidates. ORC2 and ORC4 bound to 355 sites on the P. tricornutum genome, of which 69 were constantly screened after multiple attempts. The screened ARS candidates had an AT-richness of approximately 50% (44.39-52.92%) and did not have conserved sequences. In addition, ARS candidates were distributed randomly but had a dense distribution pattern at several sites. Their positions tended to overlap with those of the genetic region (73.91%). Compared to the ARSs of several other eukaryotic organisms, the characteristics of the screened ARS candidates are complex. Thus, our findings suggest that the diatom has a distinct and unique native ARSs.

Synthetic Promoters in Gene Therapy: Design Approaches, Features and Applications

Gene therapy is a promising approach to the treatment of various inherited diseases, but its development is complicated by a number of limitations of the natural promoters used. The currently used strong ubiquitous natural promoters do not allow for the specificity of expression, while natural tissue-specific promoters have lowactivity. These limitations of natural promoters can be addressed by creating new synthetic promoters that achieve high levels of tissue-specific target gene expression. This review discusses recent advances in the development of synthetic promoters that provide a more precise regulation of gene expression. Approaches to the design of synthetic promoters are reviewed, including manual design and bioinformatic methods using machine learning. Examples of successful applications of synthetic promoters in the therapy of hereditary diseases and cancer are presented, as well as prospects for their clinical use.

Transcriptomic era of cancers in females: new epigenetic perspectives and therapeutic prospects

In the era of transcriptomics, the role of epigenetics in the study of cancers in females has gained increasing recognition. This article explores the impact of epigenetic modifications, such as DNA methylation, histone modification, and non-coding RNA, on cancers in females, including breast, cervical, and ovarian cancers (1). Our findings suggest that these epigenetic markers not only influence tumor onset, progression, and metastasis but also present novel targets for therapeutic intervention. Detailed analyses of DNA methylation patterns have revealed aberrant events in cancer cells, particularly promoter region hypermethylation, which may lead to silencing of tumor suppressor genes. Furthermore, we examined the complex roles of histone modifications and long non-coding RNAs in regulating the expression of cancer-related genes, thereby providing a scientific basis for developing targeted epigenetic therapies. Our research emphasizes the importance of understanding the functions and mechanisms of epigenetics in cancers in females to develop effective treatment strategies. Future therapeutic approaches may include drugs targeting specific epigenetic markers, which could not only improve therapeutic outcomes but also enhance patient survival and quality of life. Through these efforts, we aim to offer new perspectives and hope for the prevention, diagnosis, and treatment of cancers in females.

The dual effects of Benzo(a)pyrene/Benzo(a)pyrene-7,8-dihydrodiol-9,10-epoxide on DNA Methylation

Benzo(a)pyrene (BaP) is one of the most thoroughly studied polycyclic aromatic hydrocarbons(PAHs) and a widespread organic pollutant in various areas of human life. Its teratogenic, immunotoxic and carcinogenic effects on organisms are well documented and widely recognized by researchers. In the body, BaP is enzymatically converted to form a more active benzo(a)pyrene-7,8-dihydrodiol-9,10-epoxide (BPDE). BaP/BPDE has the potential to trigger gene mutations, influence epigenetic modifications and cause damage to cellular structures, ultimately contributing to disease onset and progression. However, there are different points of view when studying epigenetics using BaP/BPDE. On the one hand, it is claimed in cancer research that BaP/BPDE contributes to gene hypermethylation and, in particular, induces the hypermethylation of tumor's suppressor gene promoters, leading to gene silencing and subsequent cancer development. Conversely, studies in human and animal populations suggest that exposure to BaP results in genome-wide DNA hypomethylation, potentially leading to adverse outcomes in inflammatory diseases. This apparent contradiction has not been summarized in research for almost four decades. This article presents a comprehensive review of the current literature on the influence of BaP/BPDE on DNA methylation regulation. It demonstrates that BaP/BPDE exerts a dual-phase regulatory effect on methylation, which is influenced by factors such as the concentration and duration of BaP/BPDE exposure, experimental models and detection methods used in various studies. Acute/high concentration exposure to BaP/BPDE often results in global demethylation of DNA, which is associated with inhibition of DNA methyltransferase 1 (DNMT1) after exposure. At certain specific gene loci (e.g., RAR-β), BPDE can form DNA adducts, recruiting DNMT3 and leading to hypermethylation at specific sites. By integrating these different mechanisms, our goal is to unravel the patterns and regulations of BaP/BPDE-induced DNA methylation changes and provide insights into future precision therapies targeting epigenetics.

Advances in omics data for eosinophilic esophagitis: moving towards multi-omics analyses

Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.

Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. ALL originates from precursor lymphocytes that acquire multiple genomic changes over time, including chromosomal rearrangements and point mutations. While a large variety of genomic defects was identified and characterized in ALL over the past 30 years, it was only in recent years that the clonal heterogeneity was recognized. Thanks to the latest advancements in single-cell sequencing techniques, which have evolved from the analysis of a few hundred cells to the analysis of thousands of cells simultaneously, the study of tumor heterogeneity now becomes possible. Different modalities can be explored at the single-cell level: DNA, RNA, epigenetic modifications, and intracellular and cell surface proteins. In this review, we describe these techniques and highlight their advantages and limitations in the study of ALL biology. Moreover, multiomics technologies and the incorporation of the spatial dimension can provide insight into intercellular communication. We describe how the different single-cell sequencing technologies help to unravel the molecular complexity of ALL, shedding light on its development, its heterogeneity, its interaction with the leukemia microenvironment and possible relapse mechanisms.

Predicting protein synergistic effect in Arabidopsis using epigenome profiling

Histone modifications can regulate transcription epigenetically by marking specific genomic loci, which can be mapped using chromatin immunoprecipitation sequencing (ChIP-seq). Here we present QHistone, a predictive database of 1534 ChIP-seqs from 27 histone modifications in Arabidopsis, offering three key functionalities. Firstly, QHistone employs machine learning to predict the epigenomic profile of a query protein, characterized by its most associated histone modifications, and uses these modifications to infer the protein's role in transcriptional regulation. Secondly, it predicts synergistic regulatory activities between two proteins by comparing their profiles. Lastly, it detects previously unexplored co-regulating protein pairs by screening all known proteins. QHistone accurately identifies histone modifications associated with specific known proteins, and allows users to computationally validate their results using gene expression data from various plant tissues. These functions demonstrate an useful approach to utilizing epigenome data for gene regulation analysis, making QHistone a valuable resource for the scientific community ( https://qhistone.paoyang.ipmb.sinica.edu.tw ).

Systems immunology approaches to study T cells in health and disease

T cells are dynamically regulated immune cells that are implicated in a variety of diseases ranging from infection, cancer and autoimmunity. Recent advancements in sequencing methods have provided valuable insights in the transcriptional and epigenetic regulation of T cells in various disease settings. In this review, we identify the key sequencing-based methods that have been applied to understand the transcriptomic and epigenomic regulation of T cells in diseases.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

H3K4me3 changes occur in cell wall genes during the development of Fagopyrum tataricum morphogenic and non-morphogenic calli

Epigenetic changes accompany the dynamic changes in the cell wall composition during the development of callus cells. H3K4me3 is responsible for active gene expression and reaction to environmental cues. Chromatin immunoprecipitation (ChIP) is a powerful technique for studying the interplay between epigenetic modifications and the DNA regions of interest. In combination with sequencing, it can provide the genome-wide enrichment of the specific epigenetic mark, providing vital information on its involvement in the plethora of cellular processes. Here, we describe the genome-wide distribution of H3K4me3 in morphogenic and non-morphogenic callus of Fagopyrum tataricum. Levels of H3K4me3 were higher around the transcription start site, in agreement with the role of this mark in transcriptional activation. The global levels of methylation were higher in the non-morphogenic callus, which indicated increased gene activation compared to the morphogenic callus. We also employed ChIP to analyse the changes in the enrichment of this epigenetic mark on the cell wall-related genes in both calli types during the course of the passage. Enrichment of H3K4me3 on cell wall genes was specific for callus type, suggesting that the role of this mark in cell-wall remodelling is complex and involved in many processes related to dedifferentiation and redifferentiation. This intricacy of the cell wall composition was supported by the immunohistochemical analysis of the cell wall epitopes' distribution of pectins and extensins. Together, these data give a novel insight into the involvement of H3K4me3 in the regeneration processes in F. tataricum in vitro callus tissue culture.

The role of epigenetics in women's reproductive health: the impact of environmental factors

This paper explores the significant role of epigenetics in women's reproductive health, focusing on the impact of environmental factors. It highlights the crucial link between epigenetic modifications-such as DNA methylation and histones post-translational modifications-and reproductive health issues, including infertility and pregnancy complications. The paper reviews the influence of pollutants like PM2.5, heavy metals, and endocrine disruptors on gene expression through epigenetic mechanisms, emphasizing the need for understanding how dietary, lifestyle choices, and exposure to chemicals affect gene expression and reproductive health. Future research directions include deeper investigation into epigenetics in female reproductive health and leveraging gene editing to mitigate epigenetic changes for improving IVF success rates and managing reproductive disorders.

Deletion of Trps1 regulatory elements recapitulates postnatal hip joint abnormalities and growth retardation of Trichorhinophalangeal syndrome in mice

Trichorhinophalangeal syndrome (TRPS) is a genetic disorder caused by point mutations or deletions in the gene-encoding transcription factor TRPS1. TRPS patients display a range of skeletal dysplasias, including reduced jaw size, short stature, and a cone-shaped digit epiphysis. Certain TRPS patients experience early onset coxarthrosis that leads to a devastating drop in their daily activities. The etiologies of congenital skeletal abnormalities of TRPS were revealed through the analysis of Trps1 mutant mouse strains. However, early postnatal lethality in Trps1 knockout mice has hampered the study of postnatal TRPS pathology. Here, through epigenomic analysis we identified two previously uncharacterized candidate gene regulatory regions in the first intron of Trps1. We deleted these regions, either individually or simultaneously, and examined their effects on skeletal morphogenesis. Animals that were deleted individually for either region displayed only modest phenotypes. In contrast, the Trps1Δint/Δint mouse strain with simultaneous deletion of both genomic regions exhibit postnatal growth retardation. This strain displayed delayed secondary ossification center formation in the long bones and misshaped hip joint development that resulted in acetabular dysplasia. Reducing one allele of the Trps1 gene in Trps1Δint mice resulted in medial patellar dislocation that has been observed in some patients with TRPS. Our novel Trps1 hypomorphic strain recapitulates many postnatal pathologies observed in human TRPS patients, thus positioning this strain as a useful animal model to study postnatal TRPS pathogenesis. Our observations also suggest that Trps1 gene expression is regulated through several regulatory elements, thus guaranteeing robust expression maintenance in skeletal cells.

Identification, Design, and Application of Noncoding Cis-Regulatory Elements

Cis-regulatory elements (CREs) play a pivotal role in orchestrating interactions with trans-regulatory factors such as transcription factors, RNA-binding proteins, and noncoding RNAs. These interactions are fundamental to the molecular architecture underpinning complex and diverse biological functions in living organisms, facilitating a myriad of sophisticated and dynamic processes. The rapid advancement in the identification and characterization of these regulatory elements has been marked by initiatives such as the Encyclopedia of DNA Elements (ENCODE) project, which represents a significant milestone in the field. Concurrently, the development of CRE detection technologies, exemplified by massively parallel reporter assays, has progressed at an impressive pace, providing powerful tools for CRE discovery. The exponential growth of multimodal functional genomic data has necessitated the application of advanced analytical methods. Deep learning algorithms, particularly large language models, have emerged as invaluable tools for deconstructing the intricate nucleotide sequences governing CRE function. These advancements facilitate precise predictions of CRE activity and enable the de novo design of CREs. A deeper understanding of CRE operational dynamics is crucial for harnessing their versatile regulatory properties. Such insights are instrumental in refining gene therapy techniques, enhancing the efficacy of selective breeding programs, pushing the boundaries of genetic innovation, and opening new possibilities in microbial synthetic biology.

RNA-Independent Regulatory Functions of lncRNA in Complex Disease

During the metagenomics era, high-throughput sequencing efforts both in mice and humans indicate that non-coding RNAs (ncRNAs) constitute a significant fraction of the transcribed genome. During the past decades, the regulatory role of these non-coding transcripts along with their interactions with other molecules have been extensively characterized. However, the study of long non-coding RNAs (lncRNAs), an ncRNA regulatory class with transcript lengths that exceed 200 nucleotides, revealed that certain non-coding transcripts are transcriptional "by-products", while their loci exert their downstream regulatory functions through RNA-independent mechanisms. Such mechanisms include, but are not limited to, chromatin interactions and complex promoter-enhancer competition schemes that involve the underlying ncRNA locus with or without its nascent transcription, mediating significant or even exclusive roles in the regulation of downstream target genes in mammals. Interestingly, such RNA-independent mechanisms often drive pathological manifestations, including oncogenesis. In this review, we summarize selective examples of lncRNAs that regulate target genes independently of their produced transcripts.

Functional Genomics in Psoriasis

Psoriasis is an autoimmune cutaneous condition that significantly impacts quality of life and represents a burden on society due to its prevalence. Genome-wide association studies (GWASs) have pinpointed several psoriasis-related risk loci, underlining the disease's complexity. Functional genomics is paramount to unveiling the role of such loci in psoriasis and disentangling its complex nature. In this review, we aim to elucidate the main findings in this field and integrate our discussion with gold-standard techniques in molecular biology-i.e., Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-and high-throughput technologies. These tools are vital to understanding how disease risk loci affect gene expression in psoriasis, which is crucial in identifying new targets for personalized treatments in advanced precision medicine.

Visualizing set relationships: EVenn's comprehensive approach to Venn diagrams

Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step-by-step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user-friendly tool for the in-depth exploration of multiomics data.

A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle

Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.

Exploring the impact of insufficient thermal ablation on hepatocellular carcinoma: NDST2 overexpression mechanism and its role in facilitating growth and invasion of residual cancer cells

OBJECTIVE

Incomplete thermal ablation (ITA) fosters the malignancy of residual cells in Hepatocellular carcinoma (HCC) with unclear mechanisms now. This study aims to investigate the expression changes of NDST2 following ITA of HCC and its impact on residual cancer cells.

METHODS

An in vitro model of heat stress-induced liver cancer was constructed to measure the expression of NDST2 using Quantitative Real-Time PCR and Western blotting experiments. The sequencing data from nude mice were used for validation. The clinical significance of NDST2 in HCC was evaluated by integrating datasets. Gene ontology and pathway analysis were conducted to explore the potential signaling pathways regulated by NDST2. Additionally, NDST2 was knocked down in heat stress-induced HCC cells, and the effects of NDST2 on these cells were verified using Cell Counting Kit-8 assays, scratch assays, and Transwell assays.

RESULTS

NDST2 expression levels are elevated in HCC, leading to a decrease in overall survival rates of HCC patients. Upregulation of immune checkpoint levels in high NDST2-expressing HCC may contribute to immune evasion by liver cancer cells. Additionally, the low mutation rate of NDST2 in HCC suggests a relatively stable expression of NDST2 in this disease. Importantly, animal and cell models treated with ITA demonstrate upregulated expression of NDST2. Knockdown of NDST2 in heat stress-induced liver cancer cells results in growth inhibition associated with gene downregulation.

CONCLUSION

The upregulation of NDST2 can accelerate the progression of residual HCC after ITA, suggesting a potential role for NDST2 in the therapeutic efficacy and prognosis of residual HCC.

Untangling the gordian knot: The intertwining interactions between developmental hormone signaling and epigenetic mechanisms in insects

Hormones control developmental and physiological processes, often by regulating the expression of multiple genes simultaneously or sequentially. Crosstalk between hormones and epigenetics is pivotal to dynamically coordinate this process. Hormonal signals can guide the addition and removal of epigenetic marks, steering gene expression. Conversely, DNA methylation, histone modifications and non-coding RNAs can modulate regional chromatin structure and accessibility and regulate the expression of numerous (hormone-related) genes. Here, we provide a review of the interplay between the classical insect hormones, ecdysteroids and juvenile hormones, and epigenetics. We summarize the mode-of-action and roles of these hormones in post-embryonic development, and provide a general overview of epigenetic mechanisms. We then highlight recent advances on the interactions between these hormonal pathways and epigenetics, and their involvement in development. Furthermore, we give an overview of several 'omics techniques employed in the field. Finally, we discuss which questions remain unanswered and possible avenues for future research.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)

BACKGROUND

Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred.

RESULTS

We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations.

CONCLUSIONS

Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

E2F1 promotes cell migration in hepatocellular carcinoma via FNDC3B

FNDC3B (fibronectin type III domain containing 3B) is highly expressed in hepatocellular carcinoma (HCC) and other cancer types, and fusion genes involving FNDC3B have been identified in HCC and leukemia. Growing evidence suggests the significance of FNDC3B in tumorigenesis, particularly in cell migration and tumor metastasis. However, its regulatory mechanisms remain elusive. In this study, we employed bioinformatic, gene regulation, and protein-DNA interaction screening to investigate the transcription factors (TFs) involved in regulating FNDC3B. Initially, 338 candidate TFs were selected based on previous chromatin immunoprecipitation (ChIP)-seq experiments available in public domain databases. Through TF knockdown screening and ChIP coupled with Droplet Digital PCR assays, we identified that E2F1 (E2F transcription factor 1) is crucial for the activation of FNDC3B. Overexpression or knockdown of E2F1 significantly impacts the expression of FNDC3B. In conclusion, our study elucidated the mechanistic link between FNDC3B and E2F1. These findings contribute to a better understanding of FNDC3B in tumorigenesis and provide insights into potential therapeutic targets for cancer treatment.

Identification of the Efficient Enhancer Elements in FVIII-Padua for Gene Therapy Study of Hemophilia A

Hemophilia A (HA) is a common X-linked recessive hereditary bleeding disorder. Coagulation factor VIII (FVIII) is insufficient in patients with HA due to the mutations in the F8 gene. The restoration of plasma levels of FVIII via both recombinant B-domain-deleted FVIII (BDD-FVIII) and B-domain-deleted F8 (BDDF8) transgenes was proven to be helpful. FVIII-Padua is a 23.4 kb tandem repeat mutation in the F8 associated with a high F8 gene expression and thrombogenesis. Here we screened a core enhancer element in FVIII-Padua for improving the F8 expression. In detail, we identified a 400 bp efficient enhancer element, C400, in FVIII-Padua for the first time. The core enhancer C400 extensively improved the transcription of BDDF8 driven by human elongation factor-1 alpha in HepG2, HeLa, HEK-293T and induced pluripotent stem cells (iPSCs) with different genetic backgrounds, as well as iPSCs-derived endothelial progenitor cells (iEPCs) and iPSCs-derived mesenchymal stem cells (iMSCs). The expression of FVIII protein was increased by C400, especially in iEPCs. Our research provides a novel molecular target to enhance expression of FVIII protein, which has scientific value and application prospects in both viral and nonviral HA gene therapy strategies.

Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells

The functional performance of immune cells relies on a complex transcriptional regulatory network. The three-dimensional structure of chromatin can affect chromatin status and gene expression patterns, and plays an important regulatory role in gene transcription. Currently available techniques for studying chromatin spatial structure include chromatin conformation capture techniques and their derivatives, chromatin accessibility sequencing techniques, and others. Additionally, the recently emerged deep learning technology can be utilized as a tool to enhance the analysis of data. In this review, we elucidate the definition and significance of the three-dimensional chromatin structure, summarize the technologies available for studying it, and describe the research progress on the chromatin spatial structure of dendritic cells, macrophages, T cells, B cells, and neutrophils.

Bacterial nucleoid is a riddle wrapped in a mystery inside an enigma

Bacterial chromosome, the nucleoid, is traditionally modeled as a rosette of DNA mega-loops, organized around proteinaceous central scaffold by nucleoid-associated proteins (NAPs), and mixed with the cytoplasm by transcription and translation. Electron microscopy of fixed cells confirms dispersal of the cloud-like nucleoid within the ribosome-filled cytoplasm. Here, I discuss evidence that the nucleoid in live cells forms DNA phase separate from riboprotein phase, the "riboid." I argue that the nucleoid-riboid interphase, where DNA interacts with NAPs, transcribing RNA polymerases, nascent transcripts, and ssRNA chaperones, forms the transcription zone. An active part of phase separation, transcription zone enforces segregation of the centrally positioned information phase (the nucleoid) from the surrounding action phase (the riboid), where translation happens, protein accumulates, and metabolism occurs. I speculate that HU NAP mostly tiles up the nucleoid periphery-facilitating DNA mobility but also supporting transcription in the interphase. Besides extruding plectonemically supercoiled DNA mega-loops, condensins could compact them into solenoids of uniform rings, while HU could support rigidity and rotation of these DNA rings. The two-phase cytoplasm arrangement allows the bacterial cell to organize the central dogma activities, where (from the cell center to its periphery) DNA replicates and segregates, DNA is transcribed, nascent mRNA is handed over to ribosomes, mRNA is translated into proteins, and finally, the used mRNA is recycled into nucleotides at the inner membrane. The resulting information-action conveyor, with one activity naturally leading to the next one, explains the efficiency of prokaryotic cell design-even though its main intracellular transportation mode is free diffusion.

Loop-extruding Smc5/6 organizes transcription-induced positive DNA supercoils

The structural maintenance of chromosomes (SMC) protein complexes-cohesin, condensin, and the Smc5/6 complex (Smc5/6)-are essential for chromosome function. At the molecular level, these complexes fold DNA by loop extrusion. Accordingly, cohesin creates chromosome loops in interphase, and condensin compacts mitotic chromosomes. However, the role of Smc5/6's recently discovered DNA loop extrusion activity is unknown. Here, we uncover that Smc5/6 associates with transcription-induced positively supercoiled DNA at cohesin-dependent loop boundaries on budding yeast (Saccharomyces cerevisiae) chromosomes. Mechanistically, single-molecule imaging reveals that dimers of Smc5/6 specifically recognize the tip of positively supercoiled DNA plectonemes and efficiently initiate loop extrusion to gather the supercoiled DNA into a large plectonemic loop. Finally, Hi-C analysis shows that Smc5/6 links chromosomal regions containing transcription-induced positive supercoiling in cis. Altogether, our findings indicate that Smc5/6 controls the three-dimensional organization of chromosomes by recognizing and initiating loop extrusion on positively supercoiled DNA.

The landscape of super-enhancer regulates remote target gene transcription through loop domains in adipose tissue of pig

BACKGROUND

A super-enhancer (SE) is a huge cluster of multiple enhancers that control the key genes for cell identity and function. The rise of advanced chromatin immunoprecipitation sequencing (ChIP-seq) technology such as Cleavage Under Targets and Tagmentation (CUT&Tag) allows more SEs to be discovered. However, SE studies in Luchuan and Duroc pigs are very rare in animal husbandry.

RESULTS

We used the CUT&Tag technique to identify 145 and 378 SEs from the adipose tissues of Luchuan and Duroc pigs, respectively. There were significant differences in the peak coverage ratio of SE peaks in the gene promoter region between the two breeds. Not only that, peak signals at the start and end point of the SE peak profile showed obvious spikes. The proximal target genes of SE were highly expressed compared with the background genes and the typical enhancer target genes. Subsequently, in conjoint analysis with high-throughput chromosome conformation capture sequencing (Hi-C seq) data, we predicted the remote regulatory genes of SE and found that their expression level was related to the distance of SE extended to the loop's anchor, but not the length of loops. According to our prediction model, SEs can maintain promoter accessibility of partial remote target genes through loop domains. Finally, a batch of SEs closely related to fat metabolism traits were obtained by performing a coalition analysis of quantitative trait loci and SE data.

CONCLUSIONS

This work enabled us to obtain hundreds of SEs from Luchuan and Duroc pigs. Our model provides a new method for predicting the SE remote target genes based on loop domains, and to further explore the potential role of super-enhancer in the regulation of fat metabolism.

bHLH-regulated routes in anther development in rice and Arabidopsis

Anther development is a complex process essential for plant reproduction and crop yields. In recent years, significant progress has been made in the identification and characterization of the bHLH transcription factor family involved in anther regulation in rice and Arabidopsis, two extensively studied model plants. Research on bHLH transcription factors has unveiled their crucial function in controlling tapetum development, pollen wall formation, and other anther-specific processes. By exploring deeper into regulatory mechanisms governing anther development and bHLH transcription factors, we can gain important insights into plant reproduction, thereby accelerating crop yield improvement and the development of new plant breeding strategies. This review provides an overview of the current knowledge on anther development in rice and Arabidopsis, emphasizing the critical roles played by bHLH transcription factors in this process. Recent advances in gene expression analysis and functional studies are highlighted, as they have significantly enhanced our understanding of the regulatory networks involved in anther development.

Recent reactivation of a pathogenicity-associated transposable element is associated with major chromosomal rearrangements in a fungal wheat pathogen

Transposable elements (TEs) are key drivers of genomic variation contributing to recent adaptation in most species. Yet, the evolutionary origins and insertion dynamics within species remain poorly understood. We recapitulate the spread of the pathogenicity-associated Styx element across five species that last diverged ∼11 000 years ago. We show that the element likely originated in the Zymoseptoria fungal pathogen genus and underwent multiple independent reactivation events. Using a global 900-genome panel of the wheat pathogen Zymoseptoria tritici, we assess Styx copy number variation and identify renewed transposition activity in Oceania and South America. We show that the element can mobilize to create additional Styx copies in a four-generation pedigree. Importantly, we find that new copies of the element are not affected by genomic defenses suggesting minimal control against the element. Styx copies are preferentially located in recombination breakpoints and likely triggered multiple types of large chromosomal rearrangements. Taken together, we establish the origin, diversification and reactivation of a highly active TE with likely major consequences for chromosomal integrity and the expression of disease.

Churros: a Docker-based pipeline for large-scale epigenomic analysis

The epigenome, which reflects the modifications on chromatin or DNA sequences, provides crucial insight into gene expression regulation and cellular activity. With the continuous accumulation of epigenomic datasets such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) data, there is a great demand for a streamlined pipeline to consistently process them, especially for large-dataset comparisons involving hundreds of samples. Here, we present Churros, an end-to-end epigenomic analysis pipeline that is environmentally independent and optimized for handling large-scale data. We successfully demonstrated the effectiveness of Churros by analyzing large-scale ChIP-seq datasets with the hg38 or Telomere-to-Telomere (T2T) human reference genome. We found that applying T2T to the typical analysis workflow has important impacts on read mapping, quality checks, and peak calling. We also introduced a useful feature to study context-specific epigenomic landscapes. Churros will contribute a comprehensive and unified resource for analyzing large-scale epigenomic data.

Position Weight Matrix or Acyclic Probabilistic Finite Automaton: Which model to use? A decision rule inferred for the prediction of transcription factor binding sites

Prediction of transcription factor binding sites (TFBS) is an example of application of Bioinformatics where DNA molecules are represented as sequences of A, C, G and T symbols. The most used model in this problem is Position Weight Matrix (PWM). Notwithstanding the advantage of being simple, PWMs cannot capture dependency between nucleotide positions, which may affect prediction performance. Acyclic Probabilistic Finite Automata (APFA) is an alternative model able to accommodate position dependencies. However, APFA is a more complex model, which means more parameters have to be learned. In this paper, we propose an innovative method to identify when position dependencies influence preference for PWMs or APFAs. This implied using position dependency features extracted from 1106 sets of TFBS to infer a decision tree able to predict which is the best model - PWM or APFA - for a given set of TFBSs. According to our results, as few as three pinpointed features are able to choose the best model, providing a balance of performance (average precision) and model simplicity.