ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet. 2012;13:840-852. [PMID: 23090257 DOI: 10.1038/nrg3306]

Emerging toolkits for decoding the co-occurrence of modified histones and chromatin proteins

In eukaryotes, DNA is packaged into chromatin with the help of highly conserved histone proteins. Together with DNA-binding proteins, posttranslational modifications (PTMs) on these histones play crucial roles in regulating genome function, cell fate determination, inheritance of acquired traits, cellular states, and diseases. While most studies have focused on individual DNA-binding proteins, chromatin proteins, or histone PTMs in bulk cell populations, such chromatin features co-occur and potentially act cooperatively to accomplish specific functions in a given cell. This review discusses state-of-the-art techniques for the simultaneous profiling of multiple chromatin features in low-input samples and single cells, focusing on histone PTMs, DNA-binding, and chromatin proteins. We cover the origins of the currently available toolkits, compare and contrast their characteristic features, and discuss challenges and perspectives for future applications. Studying the co-occurrence of histone PTMs, DNA-binding proteins, and chromatin proteins in single cells will be central for a better understanding of the biological relevance of combinatorial chromatin features, their impact on genomic output, and cellular heterogeneity.

A multi-omics approach to reveal critical mechanisms of activator protein 1 (AP-1)

The Activator Protein 1 (AP-1) transcription factor complex plays a pivotal role in the regulation of cancer-related genes, influencing cancer cell proliferation, invasion, migration, angiogenesis, and apoptosis. Composed of multiple subunits, AP-1 has diverse roles across different cancer types and environmental contexts, but its specific mechanisms remain unclear. The advent of multi-omics approaches has shed light on a more comprehensive understanding of AP-1's role and mechanism in gene regulation. This review collates recent genome-wide data on AP-1 and provides an overview of its expression, structure, function, and interaction across different diseases. An examination of these findings can illuminate the intricate nature of AP-1 regulation and its significant involvement in the progression of different diseases. Moreover, we discuss the potential use of AP-1 as a target for individual therapy and explore the various challenges associated with such an approach. Ultimately, this review provides valuable insights into the biology of AP-1 and its potential as a therapeutic target for cancer and disease treatments.

Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators (TRs) with query gene sets. Identification of TRs is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

CUT&Tag applied to zebrafish adult tail fins reveals a return of embryonic H3K4me3 patterns during regeneration

Regenerative potential is governed by a complex process of transcriptional reprogramming, involving chromatin reorganization and dynamics in transcription factor binding patterns throughout the genome. The degree to which chromatin and epigenetic changes contribute to this process remains only partially understood. Here we provide a modified CUT&Tag protocol suitable for improved characterization and interrogation of changes in chromatin modifications during adult fin regeneration in zebrafish. Our protocol generates data that recapitulates results from previously published ChIP-Seq methods, requires far fewer cells as input, and significantly improves signal to noise ratios. We deliver high-resolution enrichment maps for H3K4me3 of uninjured and regenerating fin tissues. During regeneration, we find that H3K4me3 levels increase over gene promoters which become transcriptionally active and genes which lose H3K4me3 become silenced. Interestingly, these reprogramming events recapitulate the H3K4me3 patterns observed in developing fin folds of 24-h old zebrafish embryos. Our results indicate that changes in genomic H3K4me3 patterns during fin regeneration occur in a manner consistent with reactivation of developmental programs, demonstrating CUT&Tag to be an effective tool for profiling chromatin landscapes in regenerating tissues.

Revisiting Epigenetics Fundamentals and Its Biomedical Implications

In light of the post-genomic era, epigenetics brings about an opportunity to better understand how the molecular machinery works and is led by a complex dynamic set of mechanisms, often intricate and complementary in many aspects. In particular, epigenetics links developmental biology and genetics, as well as many other areas of knowledge. The present work highlights substantial scopes and relevant discoveries related to the development of the term from its first notions. To our understanding, the concept of epigenetics needs to be revisited, as it is one of the most relevant and multifaceted terms in human knowledge. To redirect future novel experimental or theoretical efforts, it is crucial to compile all significant issues that could impact human and ecological benefit in the most precise and accurate manner. In this paper, the reader can find one of the widest compilations of the landmarks and epistemic considerations of the knowledge of epigenetics across the history of biology from the earliest epigenetic formulation to genetic determinism until the present. In the present work, we link the current body of knowledge and earlier pre-genomic concepts in order to propose a new definition of epigenetics that is faithful to its regulatory nature.

Identification of the New GmJAG1 Transcription Factor Binding Motifs Using DAP-Seq

Interaction between transcription factors (TFs) and motifs is essential for gene regulation and the subsequent phenotype formation. Soybean (Glycine max) JAGGEED 1 (GmJAG1) is a key TF that controls leaf shape, seed number and flower size. To understand the GmJAG1 binding motifs, in this study, we performed the GmJAG1 DNA affinity purification sequencing (DAP-seq) experiment, which is a powerful tool for the de novo motif prediction method. Two new significant GmJAG1 binding motifs were predicted and the EMSA experiments further verified the ability of GmJAG1 bound to these motifs. The potential binding sites in the downstream gene promoter were identified through motif scanning and a potential regulatory network mediated by GmJAG1 was constructed. These results served as important genomic resources for further understanding the regulatory mechanism of GmJAG1.

De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

Decoding chromosome organization using CheC-PLS: chromosome conformation by proximity labeling and long-read sequencing

Genomic approaches have provided detailed insight into chromosome architecture. However, commonly deployed techniques do not preserve connectivity-based information, leaving large-scale genome organization poorly characterized. Here, we developed CheC-PLS: a proximity-labeling technique that indelibly marks, and then decodes, protein-associated sites. CheC-PLS tethers dam methyltransferase to a protein of interest, followed by Nanopore sequencing to identify methylated bases - indicative of in vivo proximity - along reads >100kb. As proof-of-concept we analyzed, in budding yeast, a cohesin-based meiotic backbone that organizes chromatin into an array of loops. Our data recapitulates previously obtained association patterns, and, importantly, exposes variability between cells. Single read data reveals cohesin translocation on DNA and, by anchoring reads onto unique regions, we define the internal organization of the ribosomal DNA locus. Our versatile technique, which we also deployed on isolated nuclei with nanobodies, promises to illuminate diverse chromosomal processes by describing the in vivo conformations of single chromosomes.

Exploring affinity chromatography in proteomics: A comprehensive review

Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.

Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data

The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict candidate regulatory genes of critical transcription factors by searching the whole genome. We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates that were tested via proof-of-concept experiments. Following knockdown, three out of five candidates showed significant effects on the mRNA expression of FOXP3 across multiple donors. This provides insights into the regulatory mechanisms modulating FOXP3 transcriptional expression in Tregs. Overall, at the genome level this represents a high level of accuracy in predicting upstream regulatory genes of key genes of interest.

Exhaustive identification of genome-wide binding events of transcriptional regulators

Genome-wide binding assays aspire to map the complete binding pattern of gene regulators. Common practice relies on replication-duplicates or triplicates-and high stringency statistics to favor false negatives over false positives. Here we show that duplicates and triplicates of CUT&RUN are not sufficient to discover the entire activity of transcriptional regulators. We introduce ICEBERG (Increased Capture of Enrichment By Exhaustive Replicate aGgregation), a pipeline that harnesses large numbers of CUT&RUN replicates to discover the full set of binding events and chart the line between false positives and false negatives. We employed ICEBERG to map the full set of H3K4me3-marked regions, the targets of the co-factor β-catenin, and those of the transcription factor TBX3, in human colorectal cancer cells. The ICEBERG datasets allow benchmarking of individual replicates, comparing the performance of peak calling and replication approaches, and expose the arbitrary nature of strategies to identify reproducible peaks. Instead of a static view of genomic targets, ICEBERG establishes a spectrum of detection probabilities across the genome for a given factor, underlying the intrinsic dynamicity of its mechanism of action, and permitting to distinguish frequent from rare regulation events. Finally, ICEBERG discovered instances, undetectable with other approaches, that underlie novel mechanisms of colorectal cancer progression.

CUT&Tag Applied to Zebrafish Adult Tail Fins Reveals a Return of Embryonic H3K4me3 Patterns During Regeneration

Regenerative potential is governed by a complex process of transcriptional reprogramming, involving chromatin reorganization and dynamics in transcription factor binding patterns throughout the genome. The degree to which chromatin and epigenetic changes contribute to this process remains partially understood. Here we provide a modified CUT&Tag protocol suitable for improved characterization and interrogation of epigenetic changes during adult fin regeneration in zebrafish. Our protocol generates data that recapitulates results from previously published ChIP-Seq methods, requires far fewer cells as input, and significantly improves signal to noise ratios. We deliver high-resolution enrichment maps for H3K4me3 of uninjured and regenerating fin tissues. During regeneration, we find that H3K4me3 levels increase over gene promoters which become transcriptionally active and genes which lose H3K4me3 become silenced. Interestingly, these epigenetic reprogramming events recapitulate the H3K4me3 patterns observed in developing fin folds of 24-hour old zebrafish embryos. Our results indicate that changes in genomic H3K4me3 patterns during fin regeneration occur in a manner consistent with reactivation of developmental programs, demonstrating CUT&Tag to be an effective tool for profiling chromatin landscapes in regenerating tissues.

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Key points

An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.

Quantitative transcriptomic and epigenomic data analysis: a primer

The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.

The CUT&RUN greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a recent development for epigenome mapping, but its unique methodology can hamper proper quantitative analyses. As traditional normalization approaches have been shown to be inaccurate, we sought to determine endogenous normalization factors based on the human genome regions of constant nonspecific signal. This constancy was determined by applying Shannon's information entropy, and the set of normalizer regions, which we named the 'Greenlist', was extensively validated using publicly available datasets. We demonstrate here that the greenlist normalization outperforms the current top standards, and remains consistent across different experimental setups, cell lines and antibodies; the approach can even be applied to different species or to CUT&Tag. Requiring no additional experimental steps and no added cost, this approach can be universally applied to CUT&RUN experiments to greatly minimize the interference of technical variation over the biological epigenome changes of interest.

Designing Epigenome Editors: Considerations of Biochemical and Locus Specificities

The advent of locus-specific protein recruitment technologies has enabled a new class of studies in chromatin biology. Epigenome editors (EEs) enable biochemical modifications of chromatin at almost any specific endogenous locus. Their locus-specificity unlocks unique information including the functional roles of distinct modifications at specific genomic loci. Given the growing interest in using these tools for biological and translational studies, there are many specific design considerations depending on the scientific question or clinical need. Here, we present and discuss important design considerations and challenges regarding the biochemical and locus specificities of epigenome editors. These include how to: account for the complex biochemical diversity of chromatin; control for potential interdependency of epigenome editors and their resultant modifications; avoid sequestration effects; quantify the locus specificity of epigenome editors; and improve locus-specificity by considering concentration, affinity, avidity, and sequestration effects.

Bioorthogonal Chemistry in Cellular Organelles

While bioorthogonal reactions are routinely employed in living cells and organisms, their application within individual organelles remains limited. In this review, we highlight diverse examples of bioorthogonal reactions used to investigate the roles of biomolecules and biological processes as well as advanced imaging techniques within cellular organelles. These innovations hold great promise for therapeutic interventions in personalized medicine and precision therapies. We also address existing challenges related to the selectivity and trafficking of subcellular dynamics. Organelle-targeted bioorthogonal reactions have the potential to significantly advance our understanding of cellular organization and function, provide new pathways for basic research and clinical applications, and shape the direction of cell biology and medical research.

Characterizing genetic variation in the regulation of the ER stress response through computational and cis-eQTL analyses

Misfolded proteins in the endoplasmic reticulum (ER) elicit the ER stress response, a large transcriptional response driven by 3 well-characterized transcription factors (TFs). This transcriptional response is variable across different genetic backgrounds. One mechanism in which genetic variation can lead to transcriptional variability in the ER stress response is through altered binding and activity of the 3 main TFs: XBP1, ATF6, and ATF4. This work attempts to better understand this mechanism by first creating a computational pipeline to identify potential binding sites throughout the human genome. We utilized GTEx data sets to identify cis-eQTLs that fall within predicted TF binding sites (TFBSs). We also utilized the ClinVar database to compare the number of pathogenic vs benign variants at different positions of the binding motifs. Finally, we performed a cis-eQTL analysis on human cell lines experiencing ER stress to identify cis-eQTLs that regulate the variable ER stress response. The majority of these cis-eQTLs are unique to a given condition: control or ER stress. Some of these stress-specific cis-eQTLs fall within putative binding sites of the 3 main ER stress response TFs, providing a potential mechanism by which these cis-eQTLs might be impacting gene expression under ER stress conditions through altered TF binding. This study represents the first cis-eQTL analysis on human samples experiencing ER stress and is a vital step toward identifying the genetic components responsible for the variable ER stress response.

Computational tools for inferring transcription factor activity

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning

Protein-DNA interaction is critical for life activities such as replication, transcription and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called Contrastive Learning And Pre-trained Encoder (CLAPE), which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein-DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the area under ROC curve values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein-ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.

scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting

Single-cell ATAC-seq (scATAC-seq) is a recently developed approach that provides means to investigate open chromatin at single cell level, to assess epigenetic regulation and transcription factors binding landscapes. The sparsity of the scATAC-seq data calls for imputation. Similarly, preprocessing (filtering) may be required to reduce computational load due to the large number of open regions. However, optimal strategies for both imputation and preprocessing have not been yet evaluated together. We present SAPIEnS (scATAC-seq Preprocessing and Imputation Evaluation System), a benchmark for scATAC-seq imputation frameworks, a combination of state-of-the-art imputation methods with commonly used preprocessing techniques. We assess different types of scATAC-seq analysis, i.e. clustering, visualization and digital genomic footprinting, and attain optimal preprocessing-imputation strategies. We discuss the benefits of the imputation framework depending on the task and the number of the dataset features (peaks). We conclude that the preprocessing with the Boruta method is beneficial for the majority of tasks, while imputation is helpful mostly for small datasets. We also implement a SAPIEnS database with pre-computed transcription factor footprints based on imputed data with their activity scores in a specific cell type. SAPIEnS is published at: https://github.com/lab-medvedeva/SAPIEnS. SAPIEnS database is available at: https://sapiensdb.com.

CRISPR-ChIP reveals selective regulation of H3K79me2 by Menin in MLL leukemia

Chromatin regulation involves the selective recruitment of chromatin factors to facilitate DNA repair, replication and transcription. Here we demonstrate the utility of coupling unbiased functional genomics with chromatin immunoprecipitation (CRISPR-ChIP) to identify the factors associated with active chromatin modifications in mammalian cells. Specifically, an integrated reporter containing a cis-regulatory element of interest and a single guide RNA provide a chromatinized template for a direct readout for regulators of histone modifications associated with actively transcribed genes such as H3K4me3 and H3K79me2. With CRISPR-ChIP, we identify all the nonredundant COMPASS complex members required for H3K4me3 and demonstrate that RNA polymerase II is dispensable for the maintenance of H3K4me3. As H3K79me2 has a putative oncogenic function in leukemia cells driven by MLL translocations, using CRISPR-ChIP we reveal a functional partitioning of H3K79 methylation into two distinct regulatory units: an oncogenic DOT1L complex directed by the MLL fusion protein in a Menin-dependent manner and a separate endogenous DOT1L complex, where catalytic activity is directed by MLLT10. Overall, CRISPR-ChIP provides a powerful tool for the unbiased interrogation of the mechanisms underpinning chromatin regulation.

Deep learning applications in single-cell genomics and transcriptomics data analysis

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.

Beyond assembly: the increasing flexibility of single-molecule sequencing technology

The maturation of high-throughput short-read sequencing technology over the past two decades has shaped the way genomes are studied. Recently, single-molecule, long-read sequencing has emerged as an essential tool in deciphering genome structure and function, including filling gaps in the human reference genome, measuring the epigenome and characterizing splicing variants in the transcriptome. With recent technological developments, these single-molecule technologies have moved beyond genome assembly and are being used in a variety of ways, including to selectively sequence specific loci with long reads, measure chromatin state and protein-DNA binding in order to investigate the dynamics of gene regulation, and rapidly determine copy number variation. These increasingly flexible uses of single-molecule technologies highlight a young and fast-moving part of the field that is leading to a more accessible era of nucleic acid sequencing.

GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction

Transcription factors (TFs) are important factors that regulate gene expression. Revealing the mechanism affecting the binding specificity of TFs is the key to understanding gene regulation. Most of the previous studies focus on TF-DNA binding sites at the sequence level, and they seldom utilize the contextual features of DNA sequences. In this paper, we develop an integrated spatiotemporal context-aware neural network framework, named GNet, for predicting TF-DNA binding signal at single nucleotide resolution by achieving three tasks: single nucleotide resolution signal prediction, identification of binding regions at the sequence level, and TF-DNA binding motif prediction. GNet extracts implicit spatial contextual information with a gated highway neural mechanism, which captures large context multi-level patterns using linear shortcut connections, and the idea of it permeates the encoder and decoder parts of GNet. The improved dual external attention mechanism, which learns implicit relationships both within and among samples, and improves the performance of the model. Experimental results on 53 human TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets shows that GNet outperforms the state-of-the-art methods in the three tasks, and the results of cross-species studies on 15 human and 18 mouse TF datasets of the corresponding TF families indicate that GNet also shows the best performance in cross-species prediction over the competitive methods.

Reverse-ChIP Techniques for Identifying Locus-Specific Proteomes: A Key Tool in Unlocking the Cancer Regulome

A phenotypic hallmark of cancer is aberrant transcriptional regulation. Transcriptional regulation is controlled by a complicated array of molecular factors, including the presence of transcription factors, the deposition of histone post-translational modifications, and long-range DNA interactions. Determining the molecular identity and function of these various factors is necessary to understand specific aspects of cancer biology and reveal potential therapeutic targets. Regulation of the genome by specific factors is typically studied using chromatin immunoprecipitation followed by sequencing (ChIP-Seq) that identifies genome-wide binding interactions through the use of factor-specific antibodies. A long-standing goal in many laboratories has been the development of a 'reverse-ChIP' approach to identify unknown binding partners at loci of interest. A variety of strategies have been employed to enable the selective biochemical purification of sequence-defined chromatin regions, including single-copy loci, and the subsequent analytical detection of associated proteins. This review covers mass spectrometry techniques that enable quantitative proteomics before providing a survey of approaches toward the development of strategies for the purification of sequence-specific chromatin as a 'reverse-ChIP' technique. A fully realized reverse-ChIP technique holds great potential for identifying cancer-specific targets and the development of personalized therapeutic regimens.

Next-Generation Sequencing Technology: Current Trends and Advancements

The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.

Multiomics Reveals the Regulatory Mechanisms of Arabidopsis Tissues under Heat Stress

Understanding the mechanisms of responses to high temperatures in Arabidopsis will provide insights into how plants may mitigate heat stress under global climate change. And exploring the interconnections of different modification levels in heat stress response could help us to understand the molecular mechanism of heat stress response in Arabidopsis more comprehensively and precisely. In this paper, we combined multiomics analyses to explore the common heat stress-responsive genes and specific heat-responsive metabolic pathways in Arabidopsis leaf, seedling, and seed tissues. We found that genes such as AT1G54050 play a role in promoting proper protein folding in response to HS (Heat stress). In addition, it was revealed that the binding profile of A1B is altered under elevated temperature conditions. Finally, we also show that two microRNAs, ath-mir156h and ath-mir166b-5p, may be core regulatory molecules in HS. Also elucidated that under HS, plants can regulate specific regulatory mechanisms, such as oxygen levels, by altering the degree of CHH methylation.

Genome-wide analysis of the chromatin sites targeted by HEX 70a storage protein in the honeybee brain and fat body

Hexamerins, the proteins massively stored in the larval haemolymph of insects, are gradually used throughout metamorphosis as a source of raw material and energy for the development of adult tissues. Such behaviour defined hexamerins as storage proteins. Immunofluorescence experiments coupled with confocal microscopy show a hexamerin, HEX 70a, in the nucleus of the brain and fat body cells from honeybee workers, an unexpected localization for a storage protein. HEX 70a colocalizes with fibrillarin, a nucleolar-specific protein and H3 histone, thus suggesting a potential role as a chromatin-binding protein. This was investigated through chromatin immunoprecipitation and high-throughput DNA sequencing (ChIP-seq). The significant HEX 70a-DNA binding sites were mainly localized at the intergenic, promoter and intronic regions. HEX 70a targeted DNA stretches mapped to the genomic regions encompassing genes with relevant functional attributes. Several HEX 70a targeted genes were associated with H3K27ac or/and H3K27me3, known as active and repressive histone marks. Brain and fat body tissues shared a fraction of the HEX 70 targeted genes, and tissue-specific targets were also detected. The presence of overrepresented DNA motifs in the binding sites is consistent with specific HEX 70a-chromatin association. In addition, a search for HEX 70a targets in RNA-seq public libraries of fat bodies from nurses and foragers revealed differentially expressed targets displaying hex 70a-correlated developmental expression, thus supporting a regulatory activity for HEX 70a. Our results support the premise that HEX 70a is a moonlighting protein that binds chromatin and has roles in the brain and fat body cell nuclei, apart from its canonical role as a storage protein.

Biotinylated Tn5 transposase-mediated CUT&Tag efficiently profiles transcription factor-DNA interactions in plants

In contrast to CUT&Tag approaches for profiling bulk histone modifications, current CUT&Tag methods for analysing specific transcription factor (TF)-DNA interactions remain technically challenging due to TFs having relatively low abundance. Moreover, an efficient CUT&Tag strategy for plant TFs is not yet available. Here, we first applied biotinylated Tn5 transposase-mediated CUT&Tag (B-CUT&Tag) to produce high-quality libraries for interrogating TF-DNA interactions. B-CUT&Tag combines streptavidin-biotin-based DNA purification with routine CUT&Tag, optimizing the removal of large amounts of intact chromatin not targeted by specific TFs. The biotinylated chromatin fragments are then purified for construction of deep sequencing libraries or qPCR analysis. We applied B-CUT&Tag to probe genome-wide DNA targets of Squamosa promoter-binding-like protein 9 (SPL9), a well-established TF in Arabidopsis; the resulting profiles were efficient and consistent in demonstrating its well-established target genes in juvenile-adult transition/flowering, trichome development, flavonoid biosynthesis, wax synthesis and branching. Interestingly, our results indicate functions of AtSPL9 in modulating growth-defence trade-offs. In addition, we established a method for applying qPCR after CUT&Tag (B-CUT&Tag-qPCR) and successfully validated the binding of SPL9 in Arabidopsis and PHR2 in rice. Our study thus provides a convenient and highly efficient CUT&Tag strategy for profiling TF-chromatin interactions that is widely applicable to the annotation of cis-regulatory elements for crop improvement.

Epigenetic targets in B- and T-cell lymphomas: latest developments

Non-Hodgkin's lymphomas (NHLs) comprise a diverse group of diseases, either of mature B-cell or of T-cell derivation, characterized by heterogeneous molecular features and clinical manifestations. While most of the patients are responsive to standard chemotherapy, immunotherapy, radiation and/or stem cell transplantation, relapsed and/or refractory cases still have a dismal outcome. Deep sequencing analysis have pointed out that epigenetic dysregulations, including mutations in epigenetic enzymes, such as chromatin modifiers and DNA methyltransferases (DNMTs), are prevalent in both B- cell and T-cell lymphomas. Accordingly, over the past decade, a large number of epigenetic-modifying agents have been developed and introduced into the clinical management of these entities, and a few specific inhibitors have already been approved for clinical use. Here we summarize the main epigenetic alterations described in B- and T-NHL, that further supported the clinical development of a selected set of epidrugs in determined diseases, including inhibitors of DNMTs, histone deacetylases (HDACs), and extra-terminal domain proteins (bromodomain and extra-terminal motif; BETs). Finally, we highlight the most promising future directions of research in this area, explaining how bioinformatics approaches can help to identify new epigenetic targets in B- and T-cell lymphoid neoplasms.

C4S DB: Comprehensive Collection and Comparison for ChIP-Seq Database

Combining multiple binding profiles, such as transcription factors and histone modifications, is a crucial step in revealing the functions of complex biological systems. Although a massive amount of chromatin immunoprecipitation followed by sequencing (ChIP-seq) data is available, existing ChIP-seq databases or repositories focus on individual experiments, and it is difficult to elucidate orchestrated regulation by DNA-binding elements. We developed the Comprehensive Collection and Comparison for ChIP-Seq Database (C4S DB) to provide researchers with insights into the combination of DNA binding elements based on quality-assessed public ChIP-seq data. The C4S DB is based on > 16,000 human ChIP-seq experiments and provides two main web interfaces to discover the relationships between ChIP-seq data. "Gene browser" illustrates the landscape of distributions of binding elements around a specified gene, and "global similarity," a hierarchical clustering heatmap based on a similarity between two ChIP-seq experiments, gives an overview of genome-wide relations of regulatory elements. These functions promote the identification or evaluation of both gene-specific and genome-wide colocalization or mutually exclusive localization. Modern web technologies allow users to search for and aggregate large-scale experimental data through interactive web interfaces with quick responses. The C4S DB is available at https://c4s.site.

Proximity Labeling in Plants

Proteins are workhorses in the cell; they form stable and more often dynamic, transient protein-protein interactions, assemblies, and networks and have an intimate interplay with DNA and RNA. These network interactions underlie fundamental biological processes and play essential roles in cellular function. The proximity-dependent biotinylation labeling approach combined with mass spectrometry (PL-MS) has recently emerged as a powerful technique to dissect the complex cellular network at the molecular level. In PL-MS, by fusing a genetically encoded proximity-labeling (PL) enzyme to a protein or a localization signal peptide, the enzyme is targeted to a protein complex of interest or to an organelle, allowing labeling of proximity proteins within a zoom radius. These biotinylated proteins can then be captured by streptavidin beads and identified and quantified by mass spectrometry. Recently engineered PL enzymes such as TurboID have a much-improved enzymatic activity, enabling spatiotemporal mapping with a dramatically increased signal-to-noise ratio. PL-MS has revolutionized the way we perform proteomics by overcoming several hurdles imposed by traditional technology, such as biochemical fractionation and affinity purification mass spectrometry. In this review, we focus on biotin ligase-based PL-MS applications that have been, or are likely to be, adopted by the plant field. We discuss the experimental designs and review the different choices for engineered biotin ligases, enrichment, and quantification strategies. Lastly, we review the validation and discuss future perspectives.

Development of multifunctional synthetic nucleosomes to interrogate chromatin-mediated protein interactions

Various proteins bind to chromatin to regulate DNA and its associated processes such as replication, transcription, and damage repair. The identification and characterization of these chromatin-associating proteins remain a challenge, as their interactions with chromatin often occur within the context of the local nucleosome or chromatin structure, which makes conventional peptide-based strategies unsuitable. Here, we developed a simple and robust protein labeling chemistry to prepare synthetic multifunctional nucleosomes that carry a photoreactive group, a biorthogonal handle, and a disulfide moiety to examine chromatin-protein interactions in a nucleosomal context. Using the prepared protein- and nucleosome-based photoaffinity probes, we examined a number of protein-protein and protein-nucleosome interactions. In particular, we (i) mapped the binding sites for the HMGN2-nucleosome interaction, (ii) provided the evidence for transition between the active and poised states of DOT1L in recognizing H3K79 within the nucleosome, and (iii) identified OARD1 and LAP2α as nucleosome acidic patch-associating proteins. This study provides powerful and versatile chemical tools for interrogating chromatin-associating proteins.

Spatiotemporal and global profiling of DNA-protein interactions enables discovery of low-affinity transcription factors

Precise dissection of DNA-protein interactions is essential for elucidating the recognition basis, dynamics and gene regulation mechanism. However, global profiling of weak and dynamic DNA-protein interactions remains a long-standing challenge. Here, we establish the light-induced lysine (K) enabled crosslinking (LIKE-XL) strategy for spatiotemporal and global profiling of DNA-protein interactions. Harnessing unique abilities to capture weak and transient DNA-protein interactions, we demonstrate that LIKE-XL enables the discovery of low-affinity transcription-factor/DNA interactions via sequence-specific DNA baits, determining the binding sites for transcription factors that have been previously unknown. More importantly, we successfully decipher the dynamics of the transcription factor subproteome in response to drug treatment in a time-resolved manner, and find downstream target transcription factors from drug perturbations, providing insight into their dynamic transcriptional networks. The LIKE-XL strategy offers a complementary method to expand the DNA-protein profiling toolbox and map accurate DNA-protein interactomes that were previously inaccessible via non-covalent strategies, for better understanding of protein function in health and disease.

Systematic evaluation of chromatin immunoprecipitation sequencing to study histone occupancy in dormancy transitions of grapevine buds

The regulation of DNA accessibility by histone modification has emerged as a paradigm of developmental and environmental programming. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a versatile tool to investigate in vivo protein-DNA interaction and has enabled advances in mechanistic understanding of physiologies. The technique has been successfully demonstrated in several plant species and tissues; however, it has remained challenging in woody tissues, in particular complex structures such as perennating buds. Here we developed a ChIP method specifically for mature dormant buds of grapevine (Vitis vinifera cv. Cabernet Sauvignon). Each step of the protocol was systematically optimized, including crosslinking, chromatin extraction, sonication and antibody validation. Analysis of histone H3-enriched DNA was performed to evaluate the success of the protocol and identify occupancy of histone H3 along grapevine bud chromatin. To our best knowledge, this is the first ChIP experiment protocol optimized for the grapevine bud system.

Titration-based normalization of antibody amount improves consistency of ChIP-seq experiments

Chromatin immunoprecipitation (ChIP) is an antibody-based approach that is frequently utilized in chromatin biology and epigenetics. The challenge in experimental variability by unpredictable nature of usable input amounts from samples and undefined antibody titer in ChIP reaction still remains to be addressed. Here, we introduce a simple and quick method to quantify chromatin inputs and demonstrate its utility for normalizing antibody amounts to the optimal titer in individual ChIP reactions. For a proof of concept, we utilized ChIP-seq validated antibodies against the key enhancer mark, acetylation of histone H3 on lysine 27 (H3K27ac), in the experiments. The results indicate that the titration-based normalization of antibody amounts improves assay outcomes including the consistency among samples both within and across experiments for a broad range of input amounts.

Progress in the study of parvovirus entry pathway

A group of DNA viruses called parvoviruses that have significant effects on cancer therapy and genetic engineering applications. After passing through the cell membrane to reach the cytosol, it moves along the microtubule toward the nuclear membrane. The nuclear localization signal (NLS) is recognized by importin-beta (impβ) and other proteins from the complex outside the nuclear membrane and binds to enter the nucleus via the nuclear pore complex (NPC). There are two main pathways for viruses to enter the nucleus. The classical pathway is through the interaction of imp α and impβ with NLS via NPC. The other is the NPC mediated by the combination of impβ and it. While the capsid is introduced into the nucleus through classical nuclear transduction, there is also a transient nuclear membrane dissolution leading to passive transport into the nucleus, which has been proposed in recent years. This article mainly discusses several nuclear entry pathways and related proteins, providing a reference for subsequent research on viral entry pathways.

RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data

BACKGROUND

Massive amounts of data are produced by combining next-generation sequencing with complex biochemistry techniques to characterize regulatory genomics profiles, such as protein-DNA interaction and chromatin accessibility. Interpretation of such high-throughput data typically requires different computation methods. However, existing tools are usually developed for a specific task, which makes it challenging to analyze the data in an integrative manner.

RESULTS

We here describe the Regulatory Genomics Toolbox (RGT), a computational library for the integrative analysis of regulatory genomics data. RGT provides different functionalities to handle genomic signals and regions. Based on that, we developed several tools to perform distinct downstream analyses, including the prediction of transcription factor binding sites using ATAC-seq data, identification of differential peaks from ChIP-seq data, and detection of triple helix mediated RNA and DNA interactions, visualization, and finding an association between distinct regulatory factors.

CONCLUSION

We present here RGT; a framework to facilitate the customization of computational methods to analyze genomic data for specific regulatory genomics problems. RGT is a comprehensive and flexible Python package for analyzing high throughput regulatory genomics data and is available at: https://github.com/CostaLab/reg-gen . The documentation is available at: https://reg-gen.readthedocs.io.

Epigenetic regulation in metabolic diseases: mechanisms and advances in clinical study

Epigenetics regulates gene expression and has been confirmed to play a critical role in a variety of metabolic diseases, such as diabetes, obesity, non-alcoholic fatty liver disease (NAFLD), osteoporosis, gout, hyperthyroidism, hypothyroidism and others. The term 'epigenetics' was firstly proposed in 1942 and with the development of technologies, the exploration of epigenetics has made great progresses. There are four main epigenetic mechanisms, including DNA methylation, histone modification, chromatin remodelling, and noncoding RNA (ncRNA), which exert different effects on metabolic diseases. Genetic and non-genetic factors, including ageing, diet, and exercise, interact with epigenetics and jointly affect the formation of a phenotype. Understanding epigenetics could be applied to diagnosing and treating metabolic diseases in the clinic, including epigenetic biomarkers, epigenetic drugs, and epigenetic editing. In this review, we introduce the brief history of epigenetics as well as the milestone events since the proposal of the term 'epigenetics'. Moreover, we summarise the research methods of epigenetics and introduce four main general mechanisms of epigenetic modulation. Furthermore, we summarise epigenetic mechanisms in metabolic diseases and introduce the interaction between epigenetics and genetic or non-genetic factors. Finally, we introduce the clinical trials and applications of epigenetics in metabolic diseases.

Profiling Chromatin Accessibility on Replicated DNA with repli-ATAC-Seq

Open or accessible chromatin typifies euchromatic regions and helps define cell type-specific transcription programs. DNA replication massively disorders chromatin composition and structure, and how accessible regions are affected by and recover from this disruption has been unclear. Here, we present repli-ATAC-seq, a protocol to profile accessible chromatin genome-wide on replicated DNA starting from 100,000 cells. In this method, replicated DNA is labeled with a short 5-ethynyl-2'-deoxyuridine (EdU) pulse in cultured cells and isolated from a population of tagmented fragments for amplification and next-generation sequencing. Repli-ATAC-seq provides high-resolution information on chromatin dynamics after DNA replication and reveals new insights into the interplay between DNA replication, transcription, and the chromatin landscape.

Insight into the Structural Basis for Dual Nucleic Acid-Recognition by the Scaffold Attachment Factor B2 Protein

The family of scaffold attachment factor B (SAFB) proteins comprises three members and was first identified as binders of the nuclear matrix/scaffold. Over the past two decades, SAFBs were shown to act in DNA repair, mRNA/(l)ncRNA processing and as part of protein complexes with chromatin-modifying enzymes. SAFB proteins are approximately 100 kDa-sized dual nucleic acid-binding proteins with dedicated domains in an otherwise largely unstructured context, but whether and how they discriminate DNA and RNA binding has remained enigmatic. We here provide the SAFB2 DNA- and RNA-binding SAP and RRM domains in their functional boundaries and use solution NMR spectroscopy to ascribe DNA- and RNA-binding functions. We give insight into their target nucleic acid preferences and map the interfaces with respective nucleic acids on sparse data-derived SAP and RRM domain structures. Further, we provide evidence that the SAP domain exhibits intra-domain dynamics and a potential tendency to dimerize, which may expand its specifically targeted DNA sequence range. Our data provide a first molecular basis of and a starting point towards deciphering DNA- and RNA-binding functions of SAFB2 on the molecular level and serve a basis for understanding its localization to specific regions of chromatin and its involvement in the processing of specific RNA species.

Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence

Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro, and/or in silico methods-a challenge rapidly growing alongside the advent of next-generation sequencing technologies and their enormous extension of 'omics' data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available 'Big Data' have recently proliferated with the use of artificial intelligence (AI). Here, we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research.

Nascent DNA sequencing and its diverse applications in genome integrity research

Multiple DNA repair pathways and biological responses to DNA damage have evolved to protect cells from various types of lesions to which they are subjected. Although DNA repair systems are mechanistically distinct, all process the damaged region and then insert new bases to fill the gap. In 1969, Robert Painter developed an assay called "unscheduled" DNA synthesis (UDS), which measures DNA repair synthesis as the uptake of radiolabeled DNA precursors distinct from replicative synthesis. Contemporary detection of nascent DNA during repair by next-generation sequencing grants genome-wide information about the nature of lesions that threaten genome integrity. Recently, we developed the SAR-seq (synthesis associated with repair sequencing) method, which provides a high-resolution view of UDS. SAR-seq has been utilized to map programmed DNA repair sites in non-dividing neurons, replication initiation zones, monitor 53BP1 function in countering end-resection, and to identify regions of the genome that fail to complete replication during S phase but utilize repair synthesis during mitosis (MiDAS). As an example of SAR-seq, we present data showing that sites replicated during mitosis correspond to common fragile sites, which have been linked to tumor progression, cellular senescence, and aging.

ChIP-seq profiling of H3K4me3 and H3K27me3 in an invasive insect, Bactrocera dorsalis

Introduction: While it has been suggested that histone modifications can facilitate animal responses to rapidly changing environments, few studies have profiled whole-genome histone modification patterns in invasive species, leaving the regulatory landscape of histone modifications in invasive species unclear. Methods: Here, we screen genome-wide patterns of two important histone modifications, trimethylated Histone H3 Lysine 4 (H3K4me3) and trimethylated Histone H3 Lysine 27 (H3K27me3), in adult thorax muscles of a notorious invasive pest, the Oriental fruit fly Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), using Chromatin Immunoprecipitation with high-throughput sequencing (ChIP-seq). Results: We identified promoters featured by the occupancy of H3K4me3, H3K27me3 or bivalent histone modifications that were respectively annotated with unique genes key to muscle development and structure maintenance. In addition, we found H3K27me3 occupied the entire body of genes, where the average enrichment was almost constant. Transcriptomic analysis indicated that H3K4me3 is associated with active gene transcription, and H3K27me3 is mostly associated with transcriptional repression. Importantly, we identified genes and putative motifs modified by distinct histone modification patterns that may possibly regulate flight activity. Discussion: These findings provide the first evidence of histone modification signature in B. dorsalis, and will be useful for future studies of epigenetic signature in other invasive insect species.

Computational prediction and characterization of cell-type-specific and shared binding sites

MOTIVATION

Cell-type-specific gene expression is maintained in large part by transcription factors (TFs) selectively binding to distinct sets of sites in different cell types. Recent research works have provided evidence that such cell-type-specific binding is determined by TF's intrinsic sequence preferences, cooperative interactions with co-factors, cell-type-specific chromatin landscapes and 3D chromatin interactions. However, computational prediction and characterization of cell-type-specific and shared binding sites is rarely studied.

RESULTS

In this article, we propose two computational approaches for predicting and characterizing cell-type-specific and shared binding sites by integrating multiple types of features, in which one is based on XGBoost and another is based on convolutional neural network (CNN). To validate the performance of our proposed approaches, ChIP-seq datasets of 10 binding factors were collected from the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines, each of which was further categorized into cell-type-specific (GM12878- and K562-specific) and shared binding sites. Then, multiple types of features for these binding sites were integrated to train the XGBoost- and CNN-based models. Experimental results show that our proposed approaches significantly outperform other competing methods on three classification tasks. Moreover, we identified independent feature contributions for cell-type-specific and shared sites through SHAP values and explored the ability of the CNN-based model to predict cell-type-specific and shared binding sites by excluding or including DNase signals. Furthermore, we investigated the generalization ability of our proposed approaches to different binding factors in the same cellular environment.

AVAILABILITY AND IMPLEMENTATION

The source code is available at: https://github.com/turningpoint1988/CSSBS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A new CUT&RUN low volume-urea (LoV-U) protocol optimized for transcriptional co-factors uncovers Wnt/β-catenin tissue-specific genomic targets

Upon WNT/β-catenin pathway activation, stabilized β-catenin travels to the nucleus where it associates with the TCF/LEF transcription factors, constitutively bound to genomic Wnt-responsive elements (WREs), to activate target gene transcription. Discovering the binding profile of β-catenin is therefore required to unambiguously assign direct targets of WNT signaling. Cleavage under targets and release using nuclease (CUT&RUN) has emerged as prime technique for mapping the binding profile of DNA-interacting proteins. Here, we present a modified version of CUT&RUN, named LoV-U (low volume and urea), that enables the robust and reproducible generation of β-catenin binding profiles, uncovering direct WNT/β-catenin target genes in human cells, as well as in cells isolated from developing mouse tissues. CUT&RUN-LoV-U outperforms original CUT&RUN when targeting co-factors that do not bind the DNA, can profile all classes of chromatin regulators and is well suited for simultaneous processing of several samples. We believe that the application of our protocol will allow the detection of the complex system of tissue-specific WNT/β-catenin target genes, together with other non-DNA-binding transcriptional regulators that act downstream of ontogenetically fundamental signaling cascades.

Structural basis for cell type specific DNA binding of C/EBPβ: The case of cell cycle inhibitor p15INK4b promoter

C/EBPβ is a key regulator of numerous cellular processes, but it can also contribute to tumorigenesis and viral diseases. It binds to specific DNA sequences (C/EBP sites) and interacts with other transcription factors to control expression of multiple eukaryotic genes in a tissue and cell-type dependent manner. A body of evidence has established that cell-type-specific regulatory information is contained in the local DNA sequence of the binding motif. In human epithelial cells, C/EBPβ is an essential cofactor for TGFβ signaling in the case of Smad2/3/4 and FoxO-dependent induction of the cell cycle inhibitor, p15INK4b. In the TGFβ-responsive region 2 of the p15INK4b promoter, the Smad binding site is flanked by a C/EBP site, CTTAA•GAAAG, which differs from the canonical, palindromic ATTGC•GCAAT motif. The X-ray crystal structure of C/EBPβ bound to the p15INK4b promoter fragment shows how GCGC-to-AAGA substitution generates changes in the intermolecular interactions in the protein-DNA interface that enhances C/EBPβ binding specificity, limits possible epigenetic regulation of the promoter, and generates a DNA element with a unique pattern of methyl groups in the major groove. Significantly, CT/GA dinucleotides located at the 5'ends of the double stranded element maintain local narrowing of the DNA minor groove width that is necessary for DNA recognition. Our results suggest that C/EBPβ would accept all forms of modified cytosine in the context of the CpT site. This contrasts with the effect on the consensus motif, where C/EBPβ binding is modestly increased by cytosine methylation, but substantially decreased by hydroxymethylation.

Transcriptomic profiling implicates PAF1 in both active and repressive immune regulatory networks

BACKGROUND

Sitting at the interface of gene expression and host-pathogen interaction, polymerase associated factor 1 complex (PAF1C) is a rising player in the innate immune response. The complex localizes to the nucleus and associates with chromatin to modulate RNA polymerase II (RNAPII) elongation of gene transcripts. Performing this function at both proximal and distal regulatory elements, PAF1C interacts with many host factors across such sites, along with several microbial proteins during infection. Therefore, translating the ubiquity of PAF1C into specific impacts on immune gene expression remains especially relevant.

RESULTS

Advancing past work, we treat PAF1 knockout cells with a slate of immune stimuli to identify key trends in PAF1-dependent gene expression with broad analytical depth. From our transcriptomic data, we confirm PAF1 is an activator of traditional immune response pathways as well as other cellular pathways correlated with pathogen defense. With this model, we employ computational approaches to refine how PAF1 may contribute to both gene activation and suppression. Specifically focusing on transcriptional motifs and regulons, we predict gene regulatory elements strongly associated with PAF1, including those implicated in an immune response. Overall, our results suggest PAF1 is involved in innate immunity at several distinct axes of regulation.

CONCLUSIONS

By identifying PAF1-dependent gene expression across several pathogenic contexts, we confirm PAF1C to be a key mediator of innate immunity. Combining these transcriptomic profiles with potential regulatory networks corroborates the previously identified functions of PAF1C. With this, we foster new avenues for its study as a regulator of innate immunity, and our results will serve as a basis for targeted study of PAF1C in future validation studies.