ChIP-seq: advantages and challenges of a maturing technology

Cut&tag: a powerful epigenetic tool for chromatin profiling

Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

Genome-wide mapping of the binding sites of myocyte enhancer factor 2A in chicken primary myoblasts

Myocyte enhancer factor 2A (MEF2A) is a transcription factor that plays a critical role in cell proliferation, differentiation and apoptosis. In contrast to the wide characterization of its regulation mechanism in mammalian skeletal muscle, its role in chickens is limited. Especially, its wide target genes remain to be identified. Therefore, we utilized Cleavage Under Targets and Tagmentation (CUT&Tag) technology to reveal the genome-wide binding profile of MEF2A in chicken primary myoblasts thus gaining insights into its potential role in muscle development. Our results revealed that MEF2A binding sites were primarily distributed in intergenic and intronic regions. Within the promoter region, although only 8.87% of MEF2A binding sites were found, these binding sites were concentrated around the transcription start site (TSS). Following peak annotation, a total of 1903 genes were identified as potential targets of MEF2A. Gene Ontology (GO) enrichment analysis further revealed that MEF2A target genes may be involved in the regulation of embryonic development in multiple organ systems, including muscle development, gland development, and visual system development. Moreover, a comparison of the MEF2A target genes identified in chicken primary myoblasts with those in mouse C2C12 cells revealed 388 target genes are conserved across species, 1515 target genes are chicken specific. Among these conserved genes, ankyrin repeat and SOCS box containing 5 (ASB5), transmembrane protein 182 (TMEM182), myomesin 2 (MYOM2), leucyl and cystinyl aminopeptidase (LNPEP), actinin alpha 2 (ACTN2), sorbin and SH3 domain containing 1 (SORBS1), ankyrin 3 (ANK3), sarcoglycan delta (SGCD), and ORAI calcium release-activated calcium modulator 1 (ORAI1) exhibited consistent expression patterns with MEF2A during embryonic muscle development. Finally, TMEM182, as an important negative regulator of muscle development, has been validated to be regulated by MEF2A by dual-luciferase and quantitative real-time PCR (qPCR) assays. In summary, our study for the first time provides a wide landscape of MEF2A target genes in chicken primary myoblasts, which supports the active role of MEF2A in chicken muscle development.

Activated PARP1/FAK/COL5A1 signaling facilitates the tumorigenesis of cholesterol-resistant ovarian cancer cells through promoting EMT

Cancer cells require plentiful cholesterol for membrane biogenesis and other functional needs due to fast proliferating, leading to the interaction of cholesterol or its metabolites with cancer-related pathways. However, the impact of long-lasting high cholesterol concentrations on tumorigenesis and its underlying mechanisms remains largely unexplored. To the best of our knowledge, this study is the first to establish a cholesterol-resistant ovarian cancer cells, whose intracellular total cholesterol level up to 6-8 mmol/L. We confirmed that high cholesterol facilitated the progression of ovarian cancer in vitro and in vivo. Notably, our findings revealed significant upregulation of collagen type V alpha 1 chain (COL5A1) expression in cholesterol-resistant ovarian cancer cells and human ovarian cancer tissue, which was depended on FAK/Src activation. Mechanistically, PARP1 directly bound to FAK in response to activate FAK/Src/COL5A1 signaling. Intriguingly, COL5A1 depletion significantly impeded the tumorigenesis of these cells, concomitant with a decrease in epithelial-mesenchymal transition (EMT) progression. In conclusion, PARP1/FAK/COL5A1 signaling activation facilitated progression of cholesterol-resistant ovarian cancer cells by promoting EMT, thereby broadening a new therapeutic opportunity.

A transcription network underlies the dual genomic coordination of mitochondrial biogenesis

Mitochondrial biogenesis requires the expression of genes encoded by both the nuclear and mitochondrial genomes. However, aside from a handful transcriptional factors regulating specific subsets of mitochondrial genes, the overall architecture of the transcriptional control of mitochondrial biogenesis remains to be elucidated. The mechanisms coordinating these two genomes are largely unknown. We performed a targeted RNAi screen in developing eyes with reduced mitochondrial DNA content, anticipating a synergistic disruption of tissue development due to impaired mitochondrial biogenesis and mtDNA deficiency. Among 638 transcription factors annotated in Drosophila genome, 77 were identified as potential regulators of mitochondrial biogenesis. Utilizing published ChIP-seq data of positive hits, we constructed a regulatory network revealing the logic of the transcription regulation of mitochondrial biogenesis. Multiple transcription factors in core layers had extensive connections, collectively governing the expression of nearly all mitochondrial genes, whereas factors sitting on the top layer may respond to cellular cues to modulate mitochondrial biogenesis through the underlying network. CG1603, a core component of the network, was found to be indispensable for the expression of most nuclear mitochondrial genes, including those required for mtDNA maintenance and gene expression, thus coordinating nuclear genome and mtDNA activities in mitochondrial biogenies. Additional genetics analyses validated YL-1, a transcription factor upstream of CG1603 in the network, as a regulator controlling CG1603 expression and mitochondrial biogenesis.

Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein

DNA-protein interactions exert the fundamental structure of many pivotal biological processes, such as DNA replication, transcription, and gene regulation. However, accurate and efficient computational methods for identifying these interactions are still lacking. In this study, we propose a method ESM-DBP through refining the DNA-binding protein sequence repertory and domain-adaptive pretraining based the general protein language model. Our method considers the lacking exploration of general language model for DNA-binding protein domain-specific knowledge, so we screen out 170,264 DNA-binding protein sequences to construct the domain-adaptive language model. Experimental results on four downstream tasks show that ESM-DBP provides a better feature representation of DNA-binding protein compared to the original language model, resulting in improved prediction performance and outperforming the state-of-the-art methods. Moreover, ESM-DBP can still perform well even for those sequences with only a few homologous sequences. ChIP-seq on two predicted cases further support the validity of the proposed method.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Extracting regulatory active chromatin footprint from cell-free DNA

Cell-free DNA (cfDNA) has emerged as a pivotal player in precision medicine, revolutionizing the diagnostic and therapeutic landscape. While its clinical applications have significantly increased in recent years, current cfDNA assays have limited ability to identify the active transcriptional programs that govern complex disease phenotypes and capture the heterogeneity of the disease. To address these limitations, we have developed a non-invasive platform to enrich and examine the active chromatin fragments (cfDNAac) in peripheral blood. The deconvolution of the cfDNAac signal from traditional nucleosomal chromatin fragments (cfDNAnuc) yields a catalog of features linking these circulating chromatin signals in blood to specific regulatory elements across the genome, including enhancers, promoters, and highly transcribed genes, mirroring the epigenetic data from the ENCODE project. Notably, these cfDNAac counts correlate strongly with RNA polymerase II activity and exhibit distinct expression patterns for known circadian genes. Additionally, cfDNAac signals across gene bodies and promoters show strong correlations with whole blood gene expression levels defined by GTEx. This study illustrates the utility of cfDNAac analysis for investigating epigenomics and gene expression, underscoring its potential for a wide range of clinical applications in precision medicine.

Molecular mechanisms underlying gene regulatory variation of maize metabolic traits

Variation in gene expression levels is pervasive among individuals and races or varieties, and has substantial agronomic consequences, for example, by contributing to hybrid vigor. Gene expression level variation results from mutations in regulatory sequences (cis) and/or transcription factor (TF) activity (trans), but the mechanisms underlying cis- and/or trans-regulatory variation of complex phenotypes remain largely unknown. Here, we investigated gene expression variation mechanisms underlying the differential accumulation of the insecticidal compounds maysin and chlorogenic acid in silks of widely used maize (Zea mays) inbreds, B73 and A632. By combining transcriptomics and cistromics, we identified 1,338 silk direct targets of the maize R2R3-MYB TF Pericarp color1 (P1), consistent with it being a regulator of maysin and chlorogenic acid biosynthesis. Among these P1 targets, 464 showed allele-specific expression (ASE) between B73 and A632 silks. Allelic DNA-affinity purification sequencing identified 34 examples in which P1 allelic specific binding (ASB) correlated with cis-expression variation. From previous yeast one-hybrid studies, we identified 9 TFs potentially implicated in the control of P1 targets, with ASB to 83 out of 464 ASE genes (cis) and differential expression of 4 out of 9 TFs between B73 and A632 silks (trans). These results provide a molecular framework for understanding universal mechanisms underlying natural variation of gene expression levels, and how the regulation of metabolic diversity is established.

Genome-wide binding analysis unveils critical implication of B-Myb-mediated transactivation in cancers

B-Myb, also known as MYB proto-oncogene like 2 (MYBL2), is an important transcription factor implicated in transcription regulation, cell cycle and tumorigenesis. However, the molecular mechanism underlying B-Myb-controlled transactivation in different cell contexts as well as its functional implication in cancers remains elusive. In this study, we have conducted a comprehensive genome-wide analysis of B-Myb binding sites in multiple immortalized or cancer cell lines and identified its critical target genes. The results revealed that B-Myb regulates a common set of core cell cycle genes and cell type-specific genes through collaboration with other important transcription factors (e.g. NFY and MuvB complex) and binding to cell type-invariant promoters and cell type-specific enhancers and super-enhancers. KIF2C, UBE2C and MYC were further validated as B-Myb target genes. Loss-of-function analysis demonstrated that KIF2C knockdown inhibited tumor cell growth both in vitro and in vivo, suppressed cell motility and cell cycle progression, accompanied with defects in microtubule organization and mitosis, strongly suggesting that KIF2C is a critical regulator of cancer cell growth and mitosis, and maintains high cancer cell motility ability and microtubule dynamics. Pan-cancer transcriptomic analysis revealed that the overexpression of both B-Myb and KIF2C presents as independent prognostic markers in various types of cancer. Notably, B-Myb associates with NFYB, binds to target gene promoters, enhancers and super-enhancers, and provokes a cascade of oncogenic gene expression profiles in cancers. Overall, our results highly suggest the critical implication of B-Myb-mediated gene regulation in cancers, and the promising therapeutic and prognostic potentials of B-Myb and KIF2C for cancer diagnosis and treatment.

Tracking induced pluripotent stem cell differentiation with a fluorescent genetically encoded epigenetic probe

Epigenetic modifications (methylation, acetylation, etc.) of core histones play a key role in regulation of gene expression. Thus, the epigenome changes strongly during various biological processes such as cell differentiation and dedifferentiation. Classical methods of analysis of epigenetic modifications such as mass-spectrometry and chromatin immuno-precipitation, work with fixed cells only. Here we present a genetically encoded fluorescent probe, MPP8-Green, for detecting H3K9me3, a histone modification associated with inactive chromatin. This probe, based on the chromodomain of MPP8, allows for visualization of H3K9me3 epigenetic landscapes in single living cells. We used this probe to track changes in H3K9me3 landscapes during the differentiation of induced pluripotent stem cells (iPSCs) into induced neurons. Our findings revealed two major waves of global H3K9me3 reorganization during 4-day differentiation, namely on the first and third days, whereas nearly no changes occurred on the second and fourth days. The proposed method LiveMIEL (Live-cell Microscopic Imaging of Epigenetic Landscapes), which combines genetically encoded epigenetic probes and machine learning approaches, enables classification of multiparametric epigenetic signatures of single cells during stem cell differentiation and potentially in other biological models.

Molecular and immune pathobiology of human angiosarcoma

Angiosarcoma is a rare endothelial-derived malignancy that is extremely diverse in anatomy, aetiology, molecular and immune characteristics. While novel therapeutic approaches incorporating targeted agents and immunotherapy have yielded significant improvements in patient outcomes across several cancers, their impact on angiosarcoma remains modest. Contributed by its heterogeneous nature, there is currently a lack of novel drug targets in this disease entity and no reliable biomarkers that predict response to conventional treatment. This review aims to examine the molecular and immune landscape of angiosarcoma in association with its aetiology, anatomical sites, prognosis and therapeutic options. We summarise current efforts to characterise angiosarcoma subtypes based on molecular and immune profiling. Finally, we highlight promising technologies such as single-cell spatial "omics" that may further our understanding of angiosarcoma and propose strategies that can be similarly applied for the study of other rare cancers.

Identification of transcription factor co-binding patterns with non-negative matrix factorization

Transcription factor (TF) binding to DNA is critical to transcription regulation. Although the binding properties of numerous individual TFs are well-documented, a more detailed comprehension of how TFs interact cooperatively with DNA is required. We present COBIND, a novel method based on non-negative matrix factorization (NMF) to identify TF co-binding patterns automatically. COBIND applies NMF to one-hot encoded regions flanking known TF binding sites (TFBSs) to pinpoint enriched DNA patterns at fixed distances. We applied COBIND to 5699 TFBS datasets from UniBind for 401 TFs in seven species. The method uncovered already established co-binding patterns and new co-binding configurations not yet reported in the literature and inferred through motif similarity and protein-protein interaction knowledge. Our extensive analyses across species revealed that 67% of the TFs shared a co-binding motif with other TFs from the same structural family. The co-binding patterns captured by COBIND are likely functionally relevant as they harbor higher evolutionarily conservation than isolated TFBSs. Open chromatin data from matching human cell lines further supported the co-binding predictions. Finally, we used single-molecule footprinting data from mouse embryonic stem cells to confirm that the COBIND-predicted co-binding events associated with some TFs likely occurred on the same DNA molecules.

SpikeFlow: automated and flexible analysis of ChIP-Seq data with spike-in control

ChIP with reference exogenous genome (ChIP-Rx) is widely used to study histone modification changes across different biological conditions. A key step in the bioinformatics analysis of this data is calculating the normalization factors, which vary from the standard ChIP-seq pipelines. Choosing and applying the appropriate normalization method is crucial for interpreting the biological results. However, a comprehensive pipeline for complete ChIP-Rx data analysis is lacking. To address these challenges, we introduce SpikeFlow, an integrated Snakemake workflow that combines features from various existing tools to streamline ChIP-Rx data processing and enhance usability. SpikeFlow automates spike-in data scaling and provides multiple normalization options. It also performs peak calling and differential analysis with distinct modalities, enabling the detection of enrichment regions for histone modifications and transcription factor binding. Our workflow runs in-depth quality control at all the processing steps and generates an analysis report with tables and graphs to facilitate results interpretation. We validated the pipeline by performing a comparative analysis with DiffBind and SpikChIP, demonstrating robust performances in various biological models. By combining diverse functionalities into a single platform, SpikeFlow aims to simplify ChIP-Rx data analysis for the research community.

pyPAGE: A framework for Addressing biases in gene-set enrichment analysis-A case study on Alzheimer's disease

Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimer's disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimer's disease.

Enricherator: A Bayesian Method for Inferring Regularized Genome-wide Enrichments from Sequencing Count Data

A pervasive question in biological research studying gene regulation, chromatin structure, or genomics is where, and to what extent, does a signal of interest arise genome-wide? This question is addressed using a variety of methods relying on high-throughput sequencing data as their final output, including ChIP-seq for protein-DNA interactions,1 GapR-seq for measuring supercoiling,2 and HBD-seq or DRIP-seq for R-loop positioning.3,4 Current computational methods to calculate genome-wide enrichment of the signal of interest usually do not properly handle the count-based nature of sequencing data, they often do not make use of the local correlation structure of sequencing data, and they do not apply any regularization of enrichment estimates. This can result in unrealistic estimates of the true underlying biological enrichment of interest, unrealistically low estimates of confidence in point estimates of enrichment (or no estimates of confidence at all), unrealistic gyrations in enrichment estimates at very close (<10 bp) genomic loci due to noise inherent in sequencing data, and in a multiple-hypothesis testing problem during interpretation of genome-wide enrichment estimates. We developed a tool called Enricherator to infer genome-wide enrichments from sequencing count data. Enricherator uses the variational Bayes algorithm to fit a generalized linear model to sequencing count data and to sample from the approximate posterior distribution of enrichment estimates (https://github.com/jwschroeder3/enricherator). Enrichments inferred by Enricherator more precisely identify known binding sites in cases where low coverage between binding sites leads to false-positive peak calls in these noisy regions of the genome; these benefits extend to published datasets.

Geometric deep learning of protein-DNA binding specificity

Predicting protein-DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein-DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein-DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology.

A comparative analysis of ENCODE and Cistrome in the context of TF binding signal

BACKGROUND

With the rise of publicly available genomic data repositories, it is now common for scientists to rely on computational models and preprocessed data, either as control or to discover new knowledge. However, different repositories adhere to the different principles and guidelines, and data processing plays a significant role in the quality of the resulting datasets. Two popular repositories for transcription factor binding sites data - ENCODE and Cistrome - process the same biological samples in alternative ways, and their results are not always consistent. Moreover, the output format of the processing (BED narrowPeak) exposes a feature, the signalValue, which is seldom used in consistency checks, but can offer valuable insight on the quality of the data.

RESULTS

We provide evidence that data points with high signalValue(s) (top 25% of values) are more likely to be consistent between ENCODE and Cistrome in human cell lines K562, GM12878, and HepG2. In addition, we show that filtering according to said high values improves the quality of predictions for a machine learning algorithm that detects transcription factor interactions based only on positional information. Finally, we provide a set of practices and guidelines, based on the signalValue feature, for scientists who wish to compare and merge narrowPeaks from ENCODE and Cistrome.

CONCLUSIONS

The signalValue feature is an informative feature that can be effectively used to highlight consistent areas of overlap between different sources of TF binding sites that expose it. Its applicability extends to downstream to positional machine learning algorithms, making it a powerful tool for performance tweaking and data aggregation.

The epigenetic mechanism of metabolic risk in bipolar disorder

Bipolar disorder (BD) is a complex and severe mental illness that causes significant suffering to patients. In addition to the burden of depressive and manic symptoms, patients with BD are at an increased risk for metabolic syndrome (MetS). MetS includes factors associated with an increased risk of atherosclerotic cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM), which may increase the mortality rate of patients with BD. Several studies have suggested a link between BD and MetS, which may be explained at an epigenetic level. We have focused on epigenetic mechanisms to review the causes of metabolic risk in BD.

The genome of Eleocharis vivipara elucidates the genetics of C3-C4 photosynthetic plasticity and karyotype evolution in the Cyperaceae

Eleocharis vivipara, an amphibious sedge in the Cyperaceae family, has several remarkable properties, most notably its alternate use of C3 photosynthesis underwater and C4 photosynthesis on land. However, the absence of genomic data has hindered its utility for evolutionary and genetic research. Here, we present a high-quality genome for E. vivipara, representing the first chromosome-level genome for the Eleocharis genus, with an approximate size of 965.22 Mb mainly distributed across 10 chromosomes. Its Hi-C pattern, chromosome clustering results, and one-to-one genome synteny across two subgroups indicates a tetraploid structure with chromosome count 2n = 4x = 20. Phylogenetic analysis suggests that E. vivipara diverged from Cyperus esculentus approximately 32.96 million years ago (Mya), and underwent a whole-genome duplication (WGD) about 3.5 Mya. Numerous fusion and fission events were identified between the chromosomes of E. vivipara and its close relatives. We demonstrate that E. vivipara has holocentromeres, a chromosomal feature which can maintain the stability of such chromosomal rearrangements. Experimental transplantation and cross-section studies showed its terrestrial culms developed C4 Kranz anatomy with increased number of chloroplasts in the bundle sheath (BS) cells. Gene expression and weighted gene co-expression network analysis (WGCNA) showed overall elevated expression of core genes associated with the C4 pathway, and significant enrichment of genes related to modified culm anatomy and photosynthesis efficiency. We found evidence of mixed nicotinamide adenine dinucleotide - malic enzyme and phosphoenolpyruvate carboxykinase type C4 photosynthesis in E. vivipara, and hypothesize that the evolution of C4 photosynthesis predates the WGD event. The mixed type is dominated by subgenome A and supplemented by subgenome B. Collectively, our findings not only shed light on the evolution of E. vivipara and karyotype within the Cyperaceae family, but also provide valuable insights into the transition between C3 and C4 photosynthesis, offering promising avenues for crop improvement and breeding.

Applications of single-cell technologies in drug discovery for tumor treatment

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

OpenNucleome for high-resolution nuclear structural and dynamical modeling

The intricate structural organization of the human nucleus is fundamental to cellular function and gene regulation. Recent advancements in experimental techniques, including high-throughput sequencing and microscopy, have provided valuable insights into nuclear organization. Computational modeling has played significant roles in interpreting experimental observations by reconstructing high-resolution structural ensembles and uncovering organization principles. However, the absence of standardized modeling tools poses challenges for furthering nuclear investigations. We present OpenNucleome-an open-source software designed for conducting GPU-accelerated molecular dynamics simulations of the human nucleus. OpenNucleome offers particle-based representations of chromosomes at a resolution of 100 KB, encompassing nuclear lamina, nucleoli, and speckles. This software furnishes highly accurate structural models of nuclear architecture, affording the means for dynamic simulations of condensate formation, fusion, and exploration of non-equilibrium effects. We applied OpenNucleome to uncover the mechanisms driving the emergence of 'fixed points' within the nucleus-signifying genomic loci robustly anchored in proximity to specific nuclear bodies for functional purposes. This anchoring remains resilient even amidst significant fluctuations in chromosome radial positions and nuclear shapes within individual cells. Our findings lend support to a nuclear zoning model that elucidates genome functionality. We anticipate OpenNucleome to serve as a valuable tool for nuclear investigations, streamlining mechanistic explorations and enhancing the interpretation of experimental observations.

Nanoscale Structure, Interactions, and Dynamics of Centromere Nucleosomes

Centromeres are specific segments of chromosomes comprising two types of nucleosomes: canonical nucleosomes containing an octamer of H2A, H2B, H3, and H4 histones and CENP-A nucleosomes in which H3 is replaced with its analogue CENP-A. This modification leads to a difference in DNA wrapping (∼121 bp), considerably less than 147 bp in canonical nucleosomes. We used atomic force microscopy (AFM) and high-speed AFM (HS-AFM) to characterize nanoscale features and dynamics for both types of nucleosomes. For both nucleosomes, spontaneous asymmetric unwrapping of DNA was observed, and this process occurs via a transient state with ∼100 bp DNA wrapped around the core, followed by a rapid dissociation of DNA. Additionally, HS-AFM revealed higher stability of CENP-A nucleosomes compared with H3 nucleosomes in which dissociation of the histone core occurs prior to the nucleosome dissociation. These results help elucidate the differences between these nucleosomes and the potential biological necessity for CENP-A nucleosomes.

The Velvet transcription factor PnVeA regulates necrotrophic effectors and secondary metabolism in the wheat pathogen Parastagonospora nodorum

The fungus Parastagonospora nodorum causes septoria nodorum blotch on wheat. The role of the fungal Velvet-family transcription factor VeA in P. nodorum development and virulence was investigated here. Deletion of the P. nodorum VeA ortholog, PnVeA, resulted in growth abnormalities including pigmentation, abolished asexual sporulation and highly reduced virulence on wheat. Comparative RNA-Seq and RT-PCR analyses revealed that the deletion of PnVeA also decoupled the expression of major necrotrophic effector genes. In addition, the deletion of PnVeA resulted in an up-regulation of four predicted secondary metabolite (SM) gene clusters. Using liquid-chromatography mass-spectrometry, it was observed that one of the SM gene clusters led to an accumulation of the mycotoxin alternariol. PnVeA is essential for asexual sporulation, full virulence, secondary metabolism and necrotrophic effector regulation.

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.

Buckwheat OMICS: present status and future prospects

Buckwheat (Fagopyrum spp.) is an underutilized resilient crop of North Western Himalayas belonging to the family Polygonaceae and is a source of essential nutrients and therapeutics. Common Buckwheat and Tatary Buckwheat are the two main cultivated species used as food. It is the only grain crop possessing rutin, an important metabolite with high nutraceutical potential. Due to its inherent tolerance to various biotic and abiotic stresses and a short life cycle, Buckwheat has been proposed as a model crop plant. Nutritional security is one of the major concerns, breeding for a nutrient-dense crop such as Buckwheat will provide a sustainable solution. Efforts toward improving Buckwheat for nutrition and yield are limited due to the lack of available: genetic resources, genomics, transcriptomics and metabolomics. In order to harness the agricultural importance of Buckwheat, an integrated breeding and OMICS platforms needs to be established that can pave the way for a better understanding of crop biology and developing commercial varieties. This, coupled with the availability of the genome sequences of both Buckwheat species in the public domain, should facilitate the identification of alleles/QTLs and candidate genes. There is a need to further our understanding of the molecular basis of the genetic regulation that controls various economically important traits. The present review focuses on: the food and nutritional importance of Buckwheat, its various omics resources, utilization of omics approaches in understanding Buckwheat biology and, finally, how an integrated platform of breeding and omics will help in developing commercially high yielding nutrient rich cultivars in Buckwheat.

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.

How Transcription Factor Clusters Shape the Transcriptional Landscape

In eukaryotic cells, gene transcription typically occurs in discrete periods of promoter activity, interspersed with intervals of inactivity. This pattern deviates from simple stochastic events and warrants a closer examination of the molecular interactions that activate the promoter. Recent studies have identified transcription factor (TF) clusters as key precursors to transcriptional bursting. Often, these TF clusters form at chromatin segments that are physically distant from the promoter, making changes in chromatin conformation crucial for promoter-TF cluster interactions. In this review, I explore the formation and constituents of TF clusters, examining how the dynamic interplay between chromatin architecture and TF clustering influences transcriptional bursting. Additionally, I discuss techniques for visualizing TF clusters and provide an outlook on understanding the remaining gaps in this field.

Unraveling the complexity: Advanced methods in analyzing DNA, RNA, and protein interactions

Exploring the intricate interplay within and between nucleic acids, as well as their interactions with proteins, holds pivotal significance in unraveling the molecular complexities steering cancer initiation and progression. To investigate these interactions, a diverse array of highly specific and sensitive molecular techniques has been developed. The selection of a particular technique depends on the specific nature of the interactions. Typically, researchers employ an amalgamation of these different techniques to obtain a comprehensive and holistic understanding of inter- and intramolecular interactions involving DNA-DNA, RNA-RNA, DNA-RNA, or protein-DNA/RNA. Examining nucleic acid conformation reveals alternative secondary structures beyond conventional ones that have implications for cancer pathways. Mutational hotspots in cancer often lie within sequences prone to adopting these alternative structures, highlighting the importance of investigating intra-genomic and intra-transcriptomic interactions, especially in the context of mutations, to deepen our understanding of oncology. Beyond these intramolecular interactions, the interplay between DNA and RNA leads to formations like DNA:RNA hybrids (known as R-loops) or even DNA:DNA:RNA triplex structures, both influencing biological processes that ultimately impact cancer. Protein-nucleic acid interactions are intrinsic cellular phenomena crucial in both normal and pathological conditions. In particular, genetic mutations or single amino acid variations can alter a protein's structure, function, and binding affinity, thus influencing cancer progression. It is thus, imperative to understand the differences between wild-type (WT) and mutated (MT) genes, transcripts, and proteins. The review aims to summarize the frequently employed methods and techniques for investigating interactions involving nucleic acids and proteins, highlighting recent advancements and diverse adaptations of each technique.

The functional evolution of collembolan Ubx on the regulation of abdominal appendage formation

Folsomia candida is a tiny soil-living arthropod belonging to the Collembola, which is an outgroup to Insecta. It resembles insects as having a pair of antennae and three pairs of thorax legs, while it also possesses three abdominal appendages: a ventral tube located in the first abdominal segment (A1), a retinaculum in A3, and a furca in A4. Collembolan Ubx and AbdA specify abdominal appendages, but they are unable to repress appendage marker gene Dll. The genetic basis of collembolan appendage formation and the mechanisms by which Ubx and AbdA regulate Dll transcription and appendage development remains unknown. In this study, we analysed the developmental transcriptomes of F. candida and identified candidate appendage formation genes, including Ubx (FcUbx). The expression data revealed the dominance of Dll over Ubx during the embryonic 3.5 and 4.5 days, suggesting that Ubx is deficient in suppressing Dll at early appendage formation stages. Furthermore, via electrophoretic mobility shift assays and dual luciferase assays, we found that the binding and repression capacity of FcUbx on Drosophila Dll resembles those of the longest isoform of Drosophila Ubx (DmUbx_Ib), while the regulatory mechanism of the C-terminus of FcUbx on Dll repression is similar to that of the crustacean Artemia franciscana Ubx (AfUbx), demonstrating that the function of collembolan Ubx is intermediate between that of Insecta and Crustacea. In summary, our study provides novel insights into collembolan appendage formation and sheds light on the functional evolution of Ubx. Additionally, we propose a model that collembolan Ubx regulates abdominal segments in a context-specific manner.

TEENA: an integrated web server for transposable element enrichment analysis in various model and non-model organisms

Transposable elements (TEs) are abundant in the genomes of various eukaryote organisms. Increasing evidence suggests that TEs can play crucial regulatory roles-usually by creating cis-elements (e.g. enhancers and promoters) bound by distinct transcription factors (TFs). TE-derived cis-elements have gained unprecedented attentions recently, and one key step toward their understanding is to identify the enriched TEs in distinct genomic intervals (e.g. a set of enhancers or TF binding sites) as candidates for further study. Nevertheless, such analysis remains challenging for researchers unfamiliar with TEs or lack strong bioinformatic skills. Here, we present TEENA (Transposable Element ENrichment Analyzer) to streamline TE enrichment analysis in various organisms. It implements an optimized pipeline, hosts the genome/gene/TE annotations of almost one hundred species, and provides multiple parameters to enable its flexibility. Taking genomic interval data as the only user-supplied file, it can automatically retrieve the corresponding annotations and finish a routine analysis in a couple minutes. Multiple case studies demonstrate that it can produce highly reliable results matching previous knowledge. TEENA can be freely accessed at: https://sun-lab.yzu.edu.cn/TEENA. Due to its easy-to-use design, we expect it to facilitate the studies of the regulatory function of TEs in various model and non-model organisms.

A continuum of zinc finger transcription factor retention on native chromatin underlies dynamic genome organization

Transcription factor (TF) residence on chromatin translates into quantitative transcriptional or structural outcomes on genome. Commonly used formaldehyde crosslinking fixes TF-DNA interactions cumulatively and compromises the measured occupancy level. Here we mapped the occupancy level of global or individual zinc finger TFs like CTCF and MAZ, in the form of highly resolved footprints, on native chromatin. By incorporating reinforcing perturbation conditions, we established S-score, a quantitative metric to proxy the continuum of CTCF or MAZ retention across different motifs on native chromatin. The native chromatin-retained CTCF sites harbor sequence features within CTCF motifs better explained by S-score than the metrics obtained from other crosslinking or native assays. CTCF retention on native chromatin correlates with local SUMOylation level, and anti-correlates with transcriptional activity. The S-score successfully delineates the otherwise-masked differential stability of chromatin structures mediated by CTCF, or by MAZ independent of CTCF. Overall, our study established a paradigm continuum of TF retention across binding sites on native chromatin, explaining the dynamic genome organization.

Allele-specific binding variants causing ChIP-seq peak height of histone modification are not enriched in expression QTL annotations

BACKGROUND

Genome sequence variants affecting complex traits (quantitative trait loci, QTL) are enriched in functional regions of the genome, such as those marked by certain histone modifications. These variants are believed to influence gene expression. However, due to the linkage disequilibrium among nearby variants, pinpointing the precise location of QTL is challenging. We aimed to identify allele-specific binding (ASB) QTL (asbQTL) that cause variation in the level of histone modification, as measured by the height of peaks assayed by ChIP-seq (chromatin immunoprecipitation sequencing). We identified DNA sequences that predict the difference between alleles in ChIP-seq peak height in H3K4me3 and H3K27ac histone modifications in the mammary glands of cows.

RESULTS

We used a gapped k-mer support vector machine, a novel best linear unbiased prediction model, and a multiple linear regression model that combines the other two approaches to predict variant impacts on peak height. For each method, a subset of 1000 sites with the highest magnitude of predicted ASB was considered as candidate asbQTL. The accuracy of this prediction was measured by the proportion where the predicted direction matched the observed direction. Prediction accuracy ranged between 0.59 and 0.74, suggesting that these 1000 sites are enriched for asbQTL. Using independent data, we investigated functional enrichment in the candidate asbQTL set and three control groups, including non-causal ASB sites, non-ASB variants under a peak, and SNPs (single nucleotide polymorphisms) not under a peak. For H3K4me3, a higher proportion of the candidate asbQTL were confirmed as ASB when compared to the non-causal ASB sites (P < 0.01). However, these candidate asbQTL did not enrich for the other annotations, including expression QTL (eQTL), allele-specific expression QTL (aseQTL) and sites conserved across mammals (P > 0.05).

CONCLUSIONS

We identified putatively causal sites for asbQTL using the DNA sequence surrounding these sites. Our results suggest that many sites influencing histone modifications may not directly affect gene expression. However, it is important to acknowledge that distinguishing between putative causal ASB sites and other non-causal ASB sites in high linkage disequilibrium with the causal sites regarding their impact on gene expression may be challenging due to limitations in statistical power.

asteRIa enables robust interaction modeling between chromatin modifications and epigenetic readers

Chromatin, the nucleoprotein complex consisting of DNA and histone proteins, plays a crucial role in regulating gene expression by controlling access to DNA. Chromatin modifications are key players in this regulation, as they help to orchestrate DNA transcription, replication, and repair. These modifications recruit epigenetic 'reader' proteins, which mediate downstream events. Most modifications occur in distinctive combinations within a nucleosome, suggesting that epigenetic information can be encoded in combinatorial chromatin modifications. A detailed understanding of how multiple modifications cooperate in recruiting such proteins has, however, remained largely elusive. Here, we integrate nucleosome affinity purification data with high-throughput quantitative proteomics and hierarchical interaction modeling to estimate combinatorial effects of chromatin modifications on protein recruitment. This is facilitated by the computational workflow asteRIa which combines hierarchical interaction modeling, stability-based model selection, and replicate-consistency checks for a stable estimation of Robust Interactions among chromatin modifications. asteRIa identifies several epigenetic reader candidates responding to specific interactions between chromatin modifications. For the polycomb protein CBX8, we independently validate our results using genome-wide ChIP-Seq and bisulphite sequencing datasets. We provide the first quantitative framework for identifying cooperative effects of chromatin modifications on protein binding.

CBX4 suppresses CD8+ T cell antitumor immunity by reprogramming glycolytic metabolism

Rationale: CD8+ T cells undergo a series of metabolic reprogramming processes during their activation and proliferation, including increased glycolysis, decreased aerobic oxidation of sugars, increased amino acid metabolism and increased protein synthesis. However, it is still unclear what factors regulate these metabolic reprogramming processes in CD8+ T cells in the tumor immune microenvironment. Methods: T cell chromobox protein 4 (CBX4) knock-out mice models were used to determine the role of CBX4 in CD8+ T cells on the tumor immune microenvironment and tumor progression. Flow cytometry, Cut-Tag qPCR, Chip-seq, immunoprecipitation, metabolite detection, lentivirus infection and adoptive T cells transfer were performed to explore the underlying mechanisms of CBX4 knock-out in promoting CD8+ T cell activation and inhibiting tumor growth. Results: We found that CBX4 expression was induced in tumor-infiltrating CD8+ T cells and inhibited CD8+ T cell function by regulating glucose metabolism in tumor tissue. Mechanistically, CBX4 increases the expression of the metabolism-associated molecule aldolase B (Aldob) through sumoylation of trans-acting transcription factor 1 (SP1) and Krüppel-like factor 3 (KLF3). In addition, Aldob inhibits glycolysis and ATP synthesis in T cells by reducing the phosphorylation of the serine/threonine protein kinase (Akt) and ultimately suppresses CD8+ T cell function. Significantly, knocking out CBX4 may improve the efficacy of anti-PD-1 therapy by enhancing the function of CD8+ T cells in the tumor microenvironment. Conclusion: CBX4 is involved in CD8+ T cell metabolic reprogramming and functional persistence in tumor tissues, and serves as an inhibitor in CD8+ T cells' glycolysis and effector function.

Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph

Motivation

The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking.

Results

Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.

Regulatory role of Mycobacterium tuberculosis MtrA on dormancy/resuscitation revealed by a novel target gene-mining strategy

Introduction

The unique dormancy of Mycobacterium tuberculosis plays a significant role in the major clinical treatment challenge of tuberculosis, such as its long treatment cycle, antibiotic resistance, immune escape, and high latent infection rate.

Methods

To determine the function of MtrA, the only essential response regulator, one strategy was developed to establish its regulatory network according to high-quality genome-wide binding sites.

Results and discussion

The complex modulation mechanisms were implied by the strong bias distribution of MtrA binding sites in the noncoding regions, and 32.7% of the binding sites were located inside the target genes. The functions of 288 potential MtrA target genes predicted according to 294 confirmed binding sites were highly diverse, and DNA replication and damage repair, lipid metabolism, cell wall component biosynthesis, cell wall assembly, and cell division were the predominant pathways. Among the 53 pathways shared between dormancy/resuscitation and persistence, which accounted for 81.5% and 93.0% of the total number of pathways, respectively, MtrA regulatory genes were identified not only in 73.6% of their mutual pathways, but also in 75.4% of the pathways related to dormancy/resuscitation and persistence respectively. These results suggested the pivotal roles of MtrA in regulating dormancy/resuscitation and the apparent relationship between dormancy/resuscitation and persistence. Furthermore, the finding that 32.6% of the MtrA regulons were essential in vivo and/or in vitro for M. tuberculosis provided new insight into its indispensability. The findings mentioned above indicated that MtrA is a novel promising therapeutic target for tuberculosis treatment since the crucial function of MtrA may be a point of weakness for M. tuberculosis.

Salinity-responsive histone PTMs identified in the gills and gonads of Mozambique tilapia (Oreochromis mossambicus)

BACKGROUND

Histone post-translational modifications (PTMs) are epigenetic marks that can be induced by environmental stress and elicit heritable patterns of gene expression. To investigate this process in an ecological context, we characterized the influence of salinity stress on histone PTMs within the gills, kidney, and testes of Mozambique tilapia (Oreochromis mossambicus). A total of 221 histone PTMs were quantified in each tissue sample and compared between freshwater-adapted fish exposed to salinity treatments that varied in intensity and duration.

RESULTS

Four salinity-responsive histone PTMs were identified in this study. When freshwater-adapted fish were exposed to seawater for two hours, the relative abundance of H1K16ub significantly increased in the gills. Long-term salinity stress elicited changes in both the gills and testes. When freshwater-adapted fish were exposed to a pulse of severe salinity stress, where salinity gradually increased from freshwater to a maximum of 82.5 g/kg, the relative abundance of H1S1ac significantly decreased in the gills. Under the same conditions, the relative abundance of both H3K14ac and H3K18ub decreased significantly in the testes of Mozambique tilapia.

CONCLUSIONS

This study demonstrates that salinity stress can alter histone PTMs in the gills and gonads of Mozambique tilapia, which, respectively, signify a potential for histone PTMs to be involved in salinity acclimation and adaptation in euryhaline fishes. These results thereby add to a growing body of evidence that epigenetic mechanisms may be involved in such processes.

Structural dynamics in chromatin unraveling by pioneer transcription factors

Pioneer transcription factors are proteins with a dual function. First, they regulate transcription by binding to nucleosome-free DNA regulatory elements. Second, they bind to DNA while wrapped around histone proteins in the chromatin and mediate chromatin opening. The molecular mechanisms that connect the two functions are yet to be discovered. In recent years, pioneer factors received increased attention mainly because of their crucial role in promoting cell fate transitions that could be used for regenerative therapies. For example, the three factors required to induce pluripotency in somatic cells, Oct4, Sox2, and Klf4 were classified as pioneer factors and studied extensively. With this increased attention, several structures of complexes between pioneer factors and chromatin structural units (nucleosomes) have been resolved experimentally. Furthermore, experimental and computational approaches have been designed to study two unresolved, key scientific questions: First, do pioneer factors induce directly local opening of nucleosomes and chromatin fibers upon binding? And second, how do the unstructured tails of the histones impact the structural dynamics involved in such conformational transitions? Here we review the current knowledge about transcription factor-induced nucleosome dynamics and the role of the histone tails in this process. We discuss what is needed to bridge the gap between the static views obtained from the experimental structures and the key structural dynamic events in chromatin opening. Finally, we propose that integrating nuclear magnetic resonance spectroscopy with molecular dynamics simulations is a powerful approach to studying pioneer factor-mediated dynamics of nucleosomes and perhaps small chromatin fibers using native DNA sequences.

Ketogenic diet modifies ribosomal protein dysregulation in KMT2D Kabuki syndrome

BACKGROUND

Kabuki syndrome (KS) is a genetic disorder caused by DNA mutations in KMT2D, a lysine methyltransferase that methylates histones and other proteins, and therefore modifies chromatin structure and subsequent gene expression. Ketones, derived from the ketogenic diet, are histone deacetylase inhibitors that can 'open' chromatin and encourage gene expression. Preclinical studies have shown that the ketogenic diet rescues hippocampal memory neurogenesis in mice with KS via the epigenetic effects of ketones.

METHODS

Single-cell RNA sequencing and mass spectrometry-based proteomics were used to explore molecular mechanisms of disease in individuals with KS (n = 4) versus controls (n = 4).

FINDINGS

Pathway enrichment analysis indicated that loss of function mutations in KMT2D are associated with ribosomal protein dysregulation at an RNA and protein level in individuals with KS (FDR <0.05). Cellular proteomics also identified immune dysregulation and increased abundance of other lysine modification and histone binding proteins, representing a potential compensatory mechanism. A 12-year-old boy with KS, suffering from recurrent episodes of cognitive decline, exhibited improved cognitive function and neuropsychological assessment performance after 12 months on the ketogenic diet, with concomitant improvement in transcriptomic ribosomal protein dysregulation.

INTERPRETATION

Our data reveals that lysine methyltransferase deficiency is associated with ribosomal protein dysfunction, with secondary immune dysregulation. Diet and the production of bioactive molecules such as ketone bodies serve as a significant environmental factor that can induce epigenetic changes and improve clinical outcomes. Integrating transcriptomic, proteomic, and clinical data can define mechanisms of disease and treatment effects in individuals with neurodevelopmental disorders.

FUNDING

This study was supported by the Dale NHMRC Investigator Grant (APP1193648) (R.D), Petre Foundation (R.D), and The Sydney Children's Hospital Foundation/Kids Research Early and Mid-Career Researcher Grant (E.T).

An epigenetic toolbox for conservation biologists

Ongoing climatic shifts and increasing anthropogenic pressures demand an efficient delineation of conservation units and accurate predictions of populations' resilience and adaptive potential. Molecular tools involving DNA sequencing are nowadays routinely used for these purposes. Yet, most of the existing tools focusing on sequence-level information have shortcomings in detecting signals of short-term ecological relevance. Epigenetic modifications carry valuable information to better link individuals, populations, and species to their environment. Here, we discuss a series of epigenetic monitoring tools that can be directly applied to various conservation contexts, complementing already existing molecular monitoring frameworks. Focusing on DNA sequence-based methods (e.g. DNA methylation, for which the applications are readily available), we demonstrate how (a) the identification of epi-biomarkers associated with age or infection can facilitate the determination of an individual's health status in wild populations; (b) whole epigenome analyses can identify signatures of selection linked to environmental conditions and facilitate estimating the adaptive potential of populations; and (c) epi-eDNA (epigenetic environmental DNA), an epigenetic-based conservation tool, presents a non-invasive sampling method to monitor biological information beyond the mere presence of individuals. Overall, our framework refines conservation strategies, ensuring a comprehensive understanding of species' adaptive potential and persistence on ecologically relevant timescales.

Transcriptome-wide expression landscape and starch synthesis pathway co-expression network in sorghum

The gene expression landscape across different tissues and developmental stages reflects their biological functions and evolutionary patterns. Integrative and comprehensive analyses of all transcriptomic data in an organism are instrumental to obtaining a comprehensive picture of gene expression landscape. Such studies are still very limited in sorghum, which limits the discovery of the genetic basis underlying complex agricultural traits in sorghum. We characterized the genome-wide expression landscape for sorghum using 873 RNA-sequencing (RNA-seq) datasets representing 19 tissues. Our integrative analysis of these RNA-seq data provides the most comprehensive transcriptomic atlas for sorghum, which will be valuable for the sorghum research community for functional characterizations of sorghum genes. Based on the transcriptome atlas, we identified 595 housekeeping genes (HKGs) and 2080 tissue-specific expression genes (TEGs) for the 19 tissues. We identified different gene features between HKGs and TEGs, and we found that HKGs have experienced stronger selective constraints than TEGs. Furthermore, we built a transcriptome-wide co-expression network (TW-CEN) comprising 35 modules with each module enriched in specific Gene Ontology terms. High-connectivity genes in TW-CEN tend to express at high levels while undergoing intensive selective pressure. We also built global and seed-preferential co-expression networks of starch synthesis pathways, which indicated that photosynthesis and microtubule-based movement play important roles in starch synthesis. The global transcriptome atlas of sorghum generated by this study provides an important functional genomics resource for trait discovery and insight into starch synthesis regulation in sorghum.

Fibroblast-localized lncRNA CFIRL promotes cardiac fibrosis and dysfunction in dilated cardiomyopathy

CFIRL is a long noncoding RNA (lncRNA), we previously identified as the most significantly upregulated lncRNA in the failing hearts of patients with dilated cardiomyopathy (DCM). In this study, we determined the function of CFIRL and its role in DCM. Real-time polymerase chain reaction and in situ hybridization assays revealed that CFIRL was primarily localized in the nucleus of cardiac fibroblasts and robustly increased in failing hearts. Global knockdown or fibroblast-specific knockout of CFIRL attenuated transverse aortic constriction (TAC)-induced cardiac dysfunction and fibrosis in vivo. Overexpression of CFIRL in vitro promoted fibroblast proliferation and aggravated angiotensin II-induced differentiation to myofibroblasts. CFIRL knockdown attenuated these effects. Mechanistically, RNA pull-down assay and gene expression profiling revealed that CFIRL recruited ENO1, a newly identified noncanonical transcriptional factor, to activate IL-6 transcription. IL-6 exerted a paracrine effect on cardiomyocytes to promote cardiac hypertrophy, which can be prevented by CFIRL knockdown. These findings uncover the critical role of CFIRL, a fibroblast-associated lncRNA, in heart failure by facilitating crosstalk between fibroblasts and cardiomyocytes. CFIRL knockdown might be a potent strategy to prevent cardiac remodeling in heart failure, particularly in DCM.

Testing the Significance of Ranked Gene Sets in Genome-wide Transcriptome

Background

Popular gene set enrichment analysis approaches assumed that genes in the gene set contributed to the statistics equally. However, the genes in the transcription factors (TFs) derived gene sets, or gene sets constructed by TF targets identified by the ChIP-Seq experiment, have a rank attribute, as each of these genes have been assigned with a p-value which indicates the true or false possibilities of the ownerships of the genes belong to the gene sets.

Objectives

Ignoring the rank information during the enrichment analysis will lead to improper statistical inference. We address this issue by developing of new method to test the significance of ranked gene sets in genome-wide transcriptome profiling data.

Methods

A method was proposed by first creating ranked gene sets and gene lists and then applying weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights to the genes in the gene set, a new software called "Flaver" was developed.

Results

Theoretical properties of the proposed method were established, and its differences over the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55 human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method have higher tendency to be differentially expressed across the tissues analyzed than its competitors. It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and GSEA (17%), in analyzing well-documented human RNA transcriptome datasets.

Conclusions

The method is outstanding in detecting gene sets of which the gene ranks were correlated with the expression levels of the genes in the transcriptome data.

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles

Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually change expression level of downstream gene. Here, we propose a computational framework, PERD, to Predict the Enhancers Responsive to Drug. A machine learning model was trained to predict the genome-wide chromatin accessibility from transcriptome data using the paired expression and chromatin accessibility data collected from ENCODE and ROADMAP. Then the model was applied to the perturbed gene expression data from Connectivity Map (CMAP) and Cancer Drug-induced gene expression Signature DataBase (CDS-DB) and identify drug responsive enhancers with significantly altered chromatin accessibility. Furthermore, the drug responsive enhancers were related to the pharmacogenomics genome-wide association studies (PGx GWAS). Stepping on the traditional drug-associated gene signatures, PERD holds the promise to enhance the causality of drug perturbation by providing candidate regulatory element of those drug associated genes.

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.

Identifying competing endogenous RNA regulatory networks and hub genes in alcoholic liver disease for early diagnosis and potential therapeutic target insights

Alcoholic liver disease (ALD) has a complex pathogenesis. Although early-stage ALD can be reversed by ceasing alcohol consumption, early symptoms are difficult to detect, and several factors contribute to making alcohol difficult to quit. Continued alcohol abuse worsens the condition, meaning it may gradually progress into alcoholic hepatitis and cirrhosis, ultimately, resulting in irreversible consequences. Therefore, effective treatments are urgently needed for early-stage ALD. Current research mainly focuses on preventing the progression of alcoholic fatty liver to alcoholic hepatitis and cirrhosis. However, challenges remain in identifying key therapeutic targets and understanding the molecular mechanisms that underlie the treatment of alcoholic hepatitis and cirrhosis, such as the limited discovery of effective therapeutic targets and treatments. Here, we downloaded ALD microarray data from Gene Expression Omnibus and used bioinformatics to compare and identify the hub genes involved in the progression of alcoholic fatty liver to alcoholic hepatitis and cirrhosis. We also predicted target miRNAs and long non-coding RNAs (lncRNAs) to elucidate the regulatory mechanisms (the mRNA-miRNA-lncRNA axis) underlying this progression, thereby building a competitive endogenous RNA (ceRNA) mechanism for lncRNA, miRNA, and mRNA. This study provides a theoretical basis for the early treatment of alcoholic hepatitis and cirrhosis and identifies potential therapeutic targets.

Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data

MOTIVATION

In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs.

RESULTS

To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.

AVAILABILITY AND IMPLEMENTATION

STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.

Evaluation of the RNA-dependence of PRC2 binding to chromatin in human pluripotent stem cells

Polycomb Repressive Complex 2 (PRC2), an important histone modifier and epigenetic repressor, has been known to interact with RNA for almost two decades. In our previous publication (Long, Hwang et al. 2020), we presented data supporting the functional importance of RNA interaction in maintaining PRC2 occupancy on chromatin, using comprehensive approaches including an RNA-binding mutant of PRC2 and an rChIP-seq assay. Recently, concerns have been expressed regarding whether the RNA-binding mutant has impaired histone methyltransferase activity and whether the rChIP-seq assay can potentially generate artifacts. Here we provide new data that support a number of our original findings. First, we found the RNA-binding mutant to be fully capable of maintaining H3K27me3 levels in human induced pluripotent stem cells. The mutant had reduced methyltransferase activity in vitro, but only on some substrates at early time points. Second, we found that our rChIP-seq method gave consistent data across antibodies and cell lines. Third, we further optimized rChIP-seq by using lower concentrations of RNase A and incorporating a catalytically inactive mutant RNase A as a control, as well as using an alternative RNase (RNase T1). The EZH2 rChIP-seq results using the optimized protocols supported our original finding that RNA interaction contributes to the chromatin occupancy of PRC2.

Discovery and characterization of sgRNA-sequence-independent DNA cleavage from CRISPR/Cas9 in mouse embryos

The CRISPR/Cas9 system can induce off-target effects in programmed gene editing, but there have been few reports on cleavage detection and their affection in embryo development. To study these events, sgRNAs with different off-target rates were designed and compared after micro-injected into mouse zygotes, and γH2AX was used for DNA cleavage sites analysis by immunostaining and CUT&Tag. Although the low off-target sgRNA were usually selected for production gene editing animals, γH2AX immunofluorescence indicated that there was a relative DSB peak at 15 h after Cas9 system injection, and the number of γH2AX foci at the peak was significantly higher in the low off-target sgRNA-injected group than in the control group. Further, the result of CUT&Tag sequencing analysis showed more double-strand breaks (DSBs) related sequences were detected in low off-target sgRNA-injected group than control and the distribution of DSB related sequences had no chromosome specificity. Gene Ontology (GO) annotation analysis of the DSB related sequences showed that these sequences were mainly concentrated at genes associated with some important biological processes, molecular functions, and cell components. In a conclusion, there are many sgRNA-sequence-independent DSBs in early mouse embryos when the Cas9 system is used for gene editing and the DSB related sequence could be detected and characterized in the genome. These results and method should also be considered in using or optimizing the Cas9 system.

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.