Cellular heterogeneity within cancer tissues determines cancer progression and treatment response. Single-cell RNA sequencing (scRNA-seq) has provided a powerful approach for investigating the cellular heterogeneity of both cancer cells and stroma cells in the tumor microenvironment. However, the common practice to characterize cell identity based on the similarity of their gene expression profiles may not really indicate distinct cellular populations with unique roles. Generally, the cell identity and function are orchestrated by the expression of given specific genes tightly regulated by transcription factors (TFs). Therefore, deciphering TF activity is essential for gaining a better understanding of the uniqueness and functionality of each cell type. Herein, metaTF, a computational framework designed to infer TF activity in scRNA-seq data, is introduced and existing methods are outperformed for estimating TF activity. It presents the improved effectiveness in characterizing cell identity during mouse hematopoietic stem cell development. Furthermore, metaTF provides a superior characterization of the functional identity of breast cancer epithelial cells, and identifies a novel subset of neural-regulated T cells within the tumor immune microenvironment, which potentially activates BCL6 in response to neural-related signals. Overall, metaTF enables robust TF activity analysis from scRNA-seq data, significantly enhancing the characterization of cell identity and function.

Viroids, minimalist plant pathogens, pose significant threats to crops by causing severe diseases. Transcriptome profiling technologies have significantly advanced the analysis of viroid-infected host plants, providing critical insights into gene regulation by these pathogens. Despite these advancements, the presence of numerous genes of unknown function continues to limit a complete understanding of the transcriptome data. Co-expression analysis addresses this issue by clustering genes into modules based on global gene expression levels, with genes in the same cluster likely participating in the same biological pathways. In a previous study, we emphasized the importance of basic helix-loop-helix (bHLH) proteins in transcriptional reprogramming in tomato host in response to different potato spindle tuber viroid (PSTVd) strains. In the current research, we delve into tissue-specific gene modules, particularly in root and leaf tissues, governed by bHLH transcription factors (TFs) during PSTVd infections. Utilizing public datasets that span Control (C), mock-inoculated, PSTVd-mild (M), and PSTVd-severe (S23) strains in time-course infections, we uncovered differentially expressed gene modules. These modules were functionally characterized to identify essential hub genes, notably highlighting the regulatory coordination of bHLH TFs, depicted through the significant bifan motif found in these interactions. Expanding on these findings, we explored bipartite networks, discerning both common and unique bHLH TF regulatory roles. Our findings reveal that bHLH TFs play pivotal roles in regulating processes such as energy metabolism and facilitating rapid membrane repair in infected roots. In leaves, changes in the external layers affected photosynthesis, linking bHLH TFs to distinct metabolic functions. Through this holistic approach, we deepen our understanding of viroid-host interactions and the intricate regulatory mechanisms underpinning them.

Diabetic foot ulcer (DFU) is one of the most common and complex complications of diabetes, but the underlying pathophysiology remains unclear. Single-cell RNA sequencing (scRNA-seq) has been conducted to explore novel cell types or molecular profiles of DFU from various perspectives. This study aimed to comprehensively analyze the potential mechanisms underlying impaired re-epithelization of DFU in a single-cell perspective.

We conducted scRNA-seq on tissues from human normal skin, acute wound, and DFU to investigate the potential mechanisms underlying impaired epidermal differentiation and the pathological microenvironment. Pseudo-time and lineage inference analyses revealed the distinct states and transition trajectories of epidermal cells under different conditions. Transcription factor analysis revealed the potential regulatory mechanism of key subtypes of keratinocytes. Cell-cell interaction analysis revealed the regulatory network between the proinflammatory microenvironment and epidermal cells. Laser-capture microscopy coupled with RNA sequencing (LCM-seq) and multiplex immunohistochemistry were used to validate the expression and location of key subtypes of keratinocytes.

Our research provided a comprehensive map of the phenotypic and dynamic changes that occur during epidermal differentiation, alongside the corresponding regulatory networks in DFU. Importantly, we identified two subtypes of keratinocytes: basal cells (BC-2) and diabetes-associated keratinocytes (DAK) that might play crucial roles in the impairment of epidermal homeostasis. BC-2 and DAK showed a marked increase in DFU, with an inactive state and insufficient motivation for epidermal differentiation. BC-2 was involved in the cellular response and apoptosis processes, with high expression of TXNIP, IFITM1, and IL1R2. Additionally, the pro-differentiation transcription factors were downregulated in BC-2 in DFU, indicating that the differentiation process might be inhibited in BC-2 in DFU. DAK was associated with cellular glucose homeostasis. Furthermore, increased CCL2 + CXCL2+ fibroblasts, VWA1+ vascular endothelial cells, and GZMA+CD8+ T cells were detected in DFU. These changes in the wound microenvironment could regulate the fate of epidermal cells through the TNFSF12-TNFRSF12A, IFNG-IFNGR1/2, and IL-1B-IL1R2 pathways, which might result in persistent inflammation and impaired epidermal differentiation in DFU.

Our findings offer novel insights into the pathophysiology of DFU and present potential therapeutic targets that could improve wound care and treatment outcomes for DFU patients.

One of the most important goals of contemporary biology is to understand the principles of the molecular order underlying the complex dynamic architecture of cells. Here, we present an overview of the main driving forces involved in the cellular molecular complexity and in the emergent functional dynamic structures, spanning from the most basic molecular organization levels to the complex emergent integrative systemic behaviors. First, we address the molecular information processing which is essential in many complex fundamental mechanisms such as the epigenetic memory, alternative splicing, regulation of transcriptional system, and the adequate self-regulatory adaptation to the extracellular environment. Next, we approach the biochemical self-organization, which is central to understand the emergency of metabolic rhythms, circadian oscillations, and spatial traveling waves. Such a complex behavior is also fundamental to understand the temporal compartmentalization of the cellular metabolism and the dynamic regulation of many physiological activities. Numerous examples of biochemical self-organization are considered here, which show that practically all the main physiological processes in the cell exhibit this type of dynamic molecular organization. Finally, we focus on the biochemical self-assembly which, at a primary level of organization, is a basic but important mechanism for the order in the cell allowing biomolecules in a disorganized state to form complex aggregates necessary for a plethora of essential structures and physiological functions. In total, more than 500 references have been compiled in this review. Due to these main sources of order, systemic functional structures emerge in the cell, driving the metabolic functionality towards the biological complexity.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

It has been well established that cardiovascular diseases exhibit significant differences between sexes in both preclinical models and humans. In addition, there is growing recognition that disrupted circadian rhythms can contribute to the onset and progression of cardiovascular diseases. However, little is known about sex differences between the cardiac circadian clock and circadian transcriptomes in mice. Here, we show that the core clock genes are expressed in common in both sexes, but the cardiac circadian transcriptome is very sex-specific. Hearts from female mice expressed significantly more rhythmically expressed genes (REGs) than male hearts, and the temporal distribution of REGs was distinctly different between sexes. To test the contribution of the circadian clock in sex-specific gene expression in the heart, we knocked out the core circadian clock factor Bmal1 in adult cardiomyocytes. The sex differences in the circadian transcriptomes were significantly diminished with cardiomyocyte-specific loss of Bmal1. Surprisingly, loss of cardiomyocyte Bmal1 also resulted in a roughly 8-fold reduction in the number of all differentially expressed genes between male and female hearts. We highlight sex-specific changes in several cardiac-specific transcription factors, including Gata4, Nkx2-5, and Tbx5. While there is still much to learn, we conclude that cardiomyocyte-specific Bmal1 is vital in conferring sex-specific gene expression in the adult mouse heart.

The protein interactome of p65/RELA, the most active subunit of the transcription factor (TF) NF-κB, has not been previously determined in living cells. Using p65-miniTurbo fusion proteins and biotin tagging, we identify >350 RELA interactors from untreated and IL-1α-stimulated cells, including many TFs (47% of all interactors) and >50 epigenetic regulators belonging to different classes of chromatin remodeling complexes. A comparison with the interactomes of two point mutants of p65 reveals that the interactions primarily require intact dimerization rather than DNA-binding properties. A targeted RNAi screen for 38 interactors and subsequent functional transcriptome and bioinformatics studies identify gene regulatory (sub)networks, each controlled by RELA in combination with one of the TFs ZBTB5, GLIS2, TFE3/TFEB, or S100A8/A9. The large, dynamic and versatile high-resolution interactome of RELA and its gene regulatory logics provides a rich resource and a new framework for explaining how RELA cooperativity determines gene expression patterns.

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

Gene regulation in most eukaryotes involves two fundamental processes: alterations in genome packaging by nucleosomes, with active cis-regulatory elements (CREs) generally characterized by open-chromatin configuration, and transcriptional activation. Mapping these physical properties and biochemical activities, through profiling chromatin accessibility and active transcription, is a key tool for understanding the logic and mechanisms of transcription and its regulation. However, the relationship between these two states has not been accessible to simultaneous measurement. To this end, we developed KAS-ATAC, a combination of the kethoxal-assisted ssDNA sequencing (KAS-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) methods for mapping single-stranded DNA (and thus active transcription) and chromatin accessibility, respectively, enabling the genome-wide identification of DNA fragments that are simultaneously accessible and contain ssDNA. We use KAS-ATAC to evaluate levels of active transcription over different CRE classes, to estimate absolute levels of transcribed accessible DNA over CREs, to map nucleosomal configurations associated with RNA polymerase activities, and to assess transcription factor association with transcribed DNA through transcription factor binding site (TFBS) footprinting. We observe lower levels of transcription over distal enhancers compared with promoters and distinct nucleosomal configurations around transcription initiation sites associated with active transcription. We find that most TFs associate equally with transcribed and nontranscribed DNA, but a few factors specifically do not exhibit footprints over ssDNA-containing fragments. We anticipate KAS-ATAC to continue to derive useful insights into chromatin organization and transcriptional regulation in other contexts in the future.

Rhabdomyosarcoma (RMS) is a pediatric embryonal solid tumor and the most common pediatric soft tissue sarcoma. The histology and transcriptome of RMS resemble skeletal muscle progenitor cells that have failed to terminally differentiate. Thus, RMS is typically thought to arise from corrupted skeletal muscle progenitor cells during development. However, RMS can occur in body regions devoid of skeletal muscle, suggesting the potential for nonmyogenic cells of origin. Here, we discuss the interplay between RMS driver mutations and cell(s) of origin with an emphasis on driving location specificity. Additionally, we discuss the mechanisms governing RMS transformation events and tumor heterogeneity through the lens of transcriptional networks and epigenetic control. Finally, we reimagine Waddington's developmental landscape to include a plane of transformation connecting distinct lineage landscapes to more accurately reflect the phenomena observed in pediatric cancers.

Intermittent fasting remains a safe and effective strategy to ameliorate various age-related diseases, but its specific mechanisms are not fully understood. Considering that transcription factors (TFs) determine the response to environmental signals, here, we profiled the diurnal expression of 600 samples across four metabolic tissues sampled every 4 over 24 h from mice placed on five different feeding regimens to provide an atlas of TFs in biological space, time, and feeding regimen. Results showed that 1218 TFs exhibited tissue-specific and temporal expression profiles in ad libitum mice, of which 974 displayed significant oscillations at least in one tissue. Intermittent fasting triggered more than 90% (1161 in 1234) of TFs to oscillate somewhere in the body and repartitioned their tissue-specific expression. A single round of fasting generally promoted TF expression, especially in skeletal muscle and adipose tissues, while intermittent fasting mainly suppressed TF expression. Intermittent fasting down-regulated aging pathway and upregulated the pathway responsible for the inhibition of mammalian target of rapamycin (mTOR). Intermittent fasting shifts the diurnal transcriptome atlas of TFs, and mTOR inhibition may orchestrate intermittent fasting-induced health improvements. This atlas offers a reference and resource to understand how TFs and intermittent fasting may contribute to diurnal rhythm oscillation and bring about specific health benefits.

Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.

The shoot apical meristem (SAM), from which all above-ground tissues of plants are derived, is critical to plant morphology and development. In maize (Zea mays), loss-of-function mutant studies have identified several SAM-related genes, most encoding homeobox transcription factors (TFs), located upstream of hierarchical networks of hundreds of genes.

Here, we collect 46 transcriptome and 16 translatome datasets across 62 different tissues or stages from the maize inbred line B73. We construct a dynamic regulome for 27 members of three SAM-related homeobox subfamilies (KNOX, WOX, and ZF-HD) through machine-learning models for the detection of TF targets across different tissues and stages by combining tsCUT&Tag, ATAC-seq, and expression profiling. This dynamic regulome demonstrates the distinct binding specificity and co-factors for these homeobox subfamilies, indicative of functional divergence between and within them. Furthermore, we assemble a SAM dynamic regulome, illustrating potential functional mechanisms associated with plant architecture. Lastly, we generate a wox13a mutant that provides evidence that WOX13A directly regulates Gn1 expression to modulate plant height, validating the regulome of SAM-related homeobox genes.

The SAM-related homeobox transcription-factor regulome presents an unprecedented opportunity to dissect the molecular mechanisms governing SAM maintenance and development, thereby advancing our understanding of maize growth and shoot architecture.

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Not applicable.

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

Aspirations for high crop growth and yield, nutritional quality and bioproduction of materials are challenged by climate change and limited adoption of new technologies. Here, we review recent advances in approaches to profile and model gene regulatory activity over developmental and response time in specific cells, which have revealed the basis of variation in plant phenotypes: both redeployment of key regulators to new contexts and their repurposing to control different slates of genes. New synthetic biology tools allow tunable, spatiotemporal regulation of transgenes, while recent gene-editing technologies enable manipulation of the regulation of native genes. Ultimately, understanding how gene circuitry is wired to control form and function across varied plant species, combined with advanced technology to rewire that circuitry, will unlock solutions to our greatest challenges in agriculture, energy and the environment.

Steady-state levels of RNA transcripts are controlled by their rates of synthesis and degradation. Here we used nascent RNA Bru-seq and BruChase-seq to profile RNA dynamics across 16 human cell lines as part of ENCODE4 Deeply Profiled Cell Lines collection. We show that RNA turnover dynamics differ widely between transcripts of different genes and between different classes of RNA. Gene set enrichment analysis (GSEA) revealed that transcripts encoding proteins belonging to the same pathway often show similar turnover dynamics. Furthermore, transcript isoforms show distinct dynamics suggesting that RNA turnover is important in regulating mRNA isoform choice. Finally, splicing across newly made transcripts appears to be cooperative with either all or none type splicing. These data sets generated as part of ENCODE4 illustrate the intricate and coordinated regulation of RNA dynamics in controlling gene expression to allow for the precise coordination of cellular functions.

Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships.

In this study, we develop a supervised deep learning framework, IGEGRNS, to infer GRNs from scRNA-seq data based on graph embedding. In the framework, contextual information of genes is captured by GraphSAGE, which aggregates gene features and neighborhood structures to generate low-dimensional embedding for genes. Then, the k most influential nodes in the whole graph are filtered through Top-k pooling. Finally, potential regulatory relationships between genes are predicted by stacking CNNs. Compared with nine competing supervised and unsupervised methods, our method achieves better performance on six time-series scRNA-seq datasets.

Our method IGEGRNS is implemented in Python using the Pytorch machine learning library, and it is freely available at https://github.com/DHUDBlab/IGEGRNS.

It has been well established that cardiovascular diseases exhibit significant differences between sexes in both preclinical models and humans. In addition, there is growing recognition that disrupted circadian rhythms can contribute to the onset and progression of cardiovascular diseases. However little is known about sex differences between the cardiac circadian clock and circadian transcriptomes in mice. Here, we show that the the core clock genes are expressed in common in both sexes but the circadian transcriptome of the mouse heart is very sex-specific. Hearts from female mice expressed significantly more rhythmically expressed genes (REGs) than male hearts and the temporal pattern of REGs was distinctly different between sexes. We next used a cardiomyocyte-specific knock out of the core clock gene, Bmal1, to investigate its role in sex-specific gene expression in the heart. All sex differences in the circadian transcriptomes were significantly diminished with cardiomyocyte-specific loss of Bmal1. Surprisingly, loss of cardiomyocyte Bmal1 also resulted in a roughly 8-fold reduction in the number of all the differentially expressed genes between male and female hearts. We conclude that cardiomyocyte-specific Bmal1, and potentially the core clock mechanism, is vital in conferring sex-specific gene expression in the adult mouse heart.

During human pregnancy, extravillous trophoblasts play crucial roles in placental invasion into the maternal decidua and spiral artery remodeling. However, regulatory factors and their action mechanisms modulating human extravillous trophoblast specification have been unknown. By analyzing dynamic changes in transcriptome and enhancer profile during human trophoblast stem cell to extravillous trophoblast differentiation, we define stage-specific regulators, including an early-stage transcription factor, TFAP2C, and multiple late-stage transcription factors. Loss-of-function studies confirm the requirement of all transcription factors identified for adequate differentiation, and we reveal that the dynamic changes in the levels of TFAP2C are essential. Notably, TFAP2C pre-occupies the regulatory elements of the inactive extravillous trophoblast-active genes during the early stage of differentiation, and the late-stage transcription factors directly activate extravillous trophoblast-active genes, including themselves as differentiation further progresses, suggesting sequential actions of transcription factors assuring differentiation. Our results reveal stage-specific transcription factors and their inter-connected regulatory mechanisms modulating extravillous trophoblast differentiation, providing a framework for understanding early human placentation and placenta-related complications.

Transcription factors (TFs) exhibit heterogeneous DNA-binding specificities in individual cells and whole organisms under natural conditions, and de novo motif discovery usually provides multiple motifs, even from a single chromatin immunoprecipitation-sequencing (ChIP-seq) sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored.

Here, we applied MOCCS2, our k-mer-based motif discovery method, to a collection of human TF ChIP-seq samples across diverse TFs and cell types, and systematically computed profiles of TF-binding specificity scores for all k-mers. After quality control, we compiled a set of TF-binding specificity score profiles for 2,976 high-quality ChIP-seq samples, comprising 473 TFs and 398 cell types. Using these high-quality samples, we confirmed that the k-mer-based TF-binding specificity profiles reflected TF- or TF-family dependent DNA-binding specificities. We then compared the binding specificity scores of ChIP-seq samples with the same TFs but with different cell type classes and found that half of the analyzed TFs exhibited differences in DNA-binding specificities across cell type classes. Additionally, we devised a method to detect differentially bound k-mers between two ChIP-seq samples and detected k-mers exhibiting statistically significant differences in binding specificity scores. Moreover, we demonstrated that differences in the binding specificity scores between k-mers on the reference and alternative alleles could be used to predict the effect of variants on TF binding, as validated by in vitro and in vivo assay datasets. Finally, we demonstrated that binding specificity score differences can be used to interpret disease-associated non-coding single-nucleotide polymorphisms (SNPs) as TF-affecting SNPs and provide candidates responsible for TFs and cell types.

Our study provides a basis for investigating the regulation of gene expression in a TF-, TF family-, or cell-type-dependent manner. Furthermore, our differential analysis of binding-specificity scores highlights noncoding disease-associated variants in humans.

Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 2-3 fold higher accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from genomics data, empowering deeper insights into cellular mechanisms.

Transcription factors (TFs) and their binding sites have evolved to interact cooperatively or competitively with each other. Here we examine in detail, across multiple cell lines, such cooperation or competition among TFs both in sequential and spatial proximity (using chromatin conformation capture assays), considering in vivo binding data as well as TF binding motifs in DNA. We ascertain significantly co-occurring ("attractive") or avoiding ("repulsive") TF pairs using robust randomized models that retain the essential characteristics of the experimental data. Across human cell lines TFs organize into two groups, with intra-group attraction and inter-group repulsion. This is true for both sequential and spatial proximity, and for both in vivo binding and sequence motifs. Attractive TF pairs exhibit significantly more physical interactions suggesting an underlying mechanism. The two TF groups differ significantly in their genomic and network properties, as well in their function-while one group regulates housekeeping function, the other potentially regulates lineage-specific functions, that are disrupted in cancer. Weaker binding sites tend to occur in spatially interacting regions of the genome. Our results suggest that a complex pattern of spatial cooperativity of TFs and chromatin has evolved with the genome to support housekeeping and lineage-specific functions.

The development of cotton fiber is regulated by the orchestrated binding of regulatory proteins to cis-regulatory elements associated with developmental genes. The cis-trans regulatory dynamics occurred throughout the course of cotton fiber development are elusive. Here we generated genome-wide high-resolution DNase I hypersensitive sites (DHSs) maps to understand the regulatory mechanisms of cotton ovule and fiber development.

We generated DNase I hypersensitive site (DHS) profiles from cotton ovules at 0 and 3 days post anthesis (DPA) and fibers at 8, 12, 15, and 18 DPA. We obtained a total of 1185 million reads and identified a total of 199,351 DHSs through ~ 30% unique mapping reads. It should be noted that more than half of DNase-seq reads mapped multiple genome locations and were not analyzed in order to achieve a high specificity of peak profile and to avoid bias from repetitive genomic regions. Distinct chromatin accessibilities were observed in the ovules (0 and 3 DPA) compared to the fiber elongation stages (8, 12, 15, and 18 DPA). Besides, the chromatin accessibility during ovules was particularly elevated in genomic regions enriched with transposable elements (TEs) and genes in TE-enriched regions were involved in ovule cell division. We analyzed cis-regulatory modules and revealed the influence of hormones on fiber development from the regulatory divergence of transcription factor (TF) motifs. Finally, we constructed a reliable regulatory network of TFs related to ovule and fiber development based on chromatin accessibility and gene co-expression network. From this network, we discovered a novel TF, WRKY46, which may shape fiber development by regulating the lignin content.

Our results not only reveal the contribution of TEs in fiber development, but also predict and validate the TFs related to fiber development, which will benefit the research of cotton fiber molecular breeding.

Aberrant innate immune signaling in myelodysplastic syndrome (MDS) hematopoietic stem/progenitor cells (HSPCs) has been implicated as a driver of the development of MDS. We herein demonstrated that a prior stimulation with bacterial and viral products followed by loss of the Tet2 gene facilitated the development of MDS via up-regulating the target genes of the Elf1 transcription factor and remodeling the epigenome in hematopoietic stem cells (HSCs) in a manner that was dependent on Polo-like kinases (Plk) downstream of Tlr3/4-Trif signaling but did not increase genomic mutations. The pharmacological inhibition of Plk function or the knockdown of Elf1 expression was sufficient to prevent the epigenetic remodeling in HSCs and diminish the enhanced clonogenicity and the impaired erythropoiesis. Moreover, this Elf1-target signature was significantly enriched in MDS HSPCs in humans. Therefore, prior infection stress and the acquisition of a driver mutation remodeled the transcriptional and epigenetic landscapes and cellular functions in HSCs via the Trif-Plk-Elf1 axis, which promoted the development of MDS.

YAP is a key transcriptional co-activator of TEADs, it regulates cell growth and is frequently activated in cancer. In Malignant Pleural Mesothelioma (MPM), YAP is activated by loss-of-function mutations in upstream components of the Hippo pathway, while, in Uveal Melanoma (UM), YAP is activated in a Hippo-independent manner. To date, it is unclear if and how the different oncogenic lesions activating YAP impact its oncogenic program, which is particularly relevant for designing selective anti-cancer therapies. Here we show that, despite YAP being essential in both MPM and UM, its interaction with TEAD is unexpectedly dispensable in UM, limiting the applicability of TEAD inhibitors in this cancer type. Systematic functional interrogation of YAP regulatory elements in both cancer types reveals convergent regulation of broad oncogenic drivers in both MPM and UM, but also strikingly selective programs. Our work reveals unanticipated lineage-specific features of the YAP regulatory network that provide important insights to guide the design of tailored therapeutic strategies to inhibit YAP signaling across different cancer types.

The development of multi-cellular organisms from a single fertilized egg requires to differentially execute the information encoded in our DNA. This complex process is regulated by the interplay of transcription factors with a chromatin environment, both of which provide the epigenetic information maintaining cell-type specific gene expression patterns. Moreover, transcription factors and their target genes form vast interacting gene regulatory networks which can be exquisitely stable. However, all developmental processes originate from pluripotent precursor cell types. The production of terminally differentiated cells from such cells, therefore, requires successive changes of cell fates, meaning that genes relevant for the next stage of differentiation must be switched on and genes not relevant anymore must be switched off. The stimulus for the change of cell fate originates from extrinsic signals which set a cascade of intracellular processes in motion that eventually terminate at the genome leading to changes in gene expression and the development of alternate gene regulatory networks. How developmental trajectories are encoded in the genome and how the interplay between intrinsic and extrinsic processes regulates development is one of the major questions in developmental biology. The development of the hematopoietic system has long served as model to understand how changes in gene regulatory networks drive the differentiation of the various blood cell types. In this review, we highlight the main signals and transcription factors and how they are integrated at the level of chromatin programming and gene expression control. We also highlight recent studies identifying the cis-regulatory elements such as enhancers at the global level and explain how their developmental activity is regulated by the cooperation of cell-type specific and ubiquitous transcription factors with extrinsic signals.

Inflammatory cascade and extracellular matrix remodeling have been identified as pivotal pathological factors in the progression of intervertebral disc degeneration (IDD), but the mechanisms underlying the aberrant activation of transcription during nucleus pulposus (NP) cell degeneration remain elusive. Super-enhancers (SEs) are large clusters of adjacent lone enhancers, which control expression modes of cellular fate and pathogenic genes. Here, we showed that SEs underwent tremendous remodeling during NP cell degeneration and that SE-related transcripts were most abundant in inflammatory cascade and extracellular matrix remodeling processes. Inhibition of cyclin-dependent kinase 7, a transcriptional kinase-mediated transcriptional initiation in trans-acting SE complex, constricted the transcription of inflammatory cascades, and extracellular matrix remodeling-related genes such as IL1β and MMP3 in NP cells, meanwhile, also restrained the transcription of Mmp16, Tnfrsf21, and Il11ra1 to retard IDD in rats. In summary, our findings clarify SEs control the transcription of genes associated with inflammatory cascade and extracellular matrix remodeling during NP cell degeneration and identify inhibition of the cyclin-dependent kinase 7, required for SE-mediated transcriptional activation, as a therapeutic option for IDD.

Genome-wide analysis of transcription factors and epigenomic features is instrumental to shed light on DNA-templated regulatory processes such as transcription, cellular differentiation or to monitor cellular responses to environmental cues. Two decades of technological developments have led to a rich set of approaches progressively pushing the limits of epigenetic profiling towards single cells. More recently, disruptive technologies using innovative biochemistry came into play. Assays such as CUT&RUN, CUT&Tag and variations thereof show considerable potential to survey multiple TFs or histone modifications in parallel from a single experiment and in native conditions. These are in the path to become the dominant assays for genome-wide analysis of TFs and chromatin modifications in bulk, single-cell, and spatial genomic applications. The principles together with pros and cons are discussed.

Somatic mutations occur as random genetic changes in genes through protein-affecting mutations (PAMs), gene fusions, or copy number alterations (CNAs). Mutations of different types can have a similar phenotypic effect (i.e., allelic heterogeneity) and should be integrated into a unified gene mutation profile. We developed OncoMerge to fill this niche of integrating somatic mutations to capture allelic heterogeneity, assign a function to mutations, and overcome known obstacles in cancer genetics. Application of OncoMerge to TCGA Pan-Cancer Atlas increased detection of somatically mutated genes and improved the prediction of the somatic mutation role as either activating or loss of function. Using integrated somatic mutation matrices increased the power to infer gene regulatory networks and uncovered the enrichment of switch-like feedback motifs and delay-inducing feedforward loops. These studies demonstrate that OncoMerge efficiently integrates PAMs, fusions, and CNAs and strengthens downstream analyses linking somatic mutations to cancer phenotypes.

High throughput sequencing has enabled the interrogation of the transcriptomic landscape of glucagon-secreting alpha cells, insulin-secreting beta cells, and somatostatin-secreting delta cells. These approaches have furthered our understanding of expression patterns that define healthy or diseased islet cell types and helped explicate some of the intricacies between major islet cell crosstalk and glucose regulation. All three endocrine cell types derive from a common pancreatic progenitor, yet alpha and beta cells have partially opposing functions, and delta cells modulate and control insulin and glucagon release. While gene expression signatures that define and maintain cellular identity have been widely explored, the underlying epigenetic components are incompletely characterized and understood. However, chromatin accessibility and remodeling is a dynamic attribute that plays a critical role to determine and maintain cellular identity.

Here, we compare and contrast the chromatin landscape between mouse alpha, beta, and delta cells using ATAC-Seq to evaluate the significant differences in chromatin accessibility. The similarities and differences in chromatin accessibility between these related islet endocrine cells help define their fate in support of their distinct functional roles. We identify patterns that suggest that both alpha and delta cells are poised, but repressed, from becoming beta-like. We also identify patterns in differentially enriched chromatin that have transcription factor motifs preferentially associated with different regions of the genome. Finally, we not only confirm and visualize previously discovered common endocrine- and cell specific- enhancer regions across differentially enriched chromatin, but identify novel regions as well. We compiled our chromatin accessibility data in a freely accessible database of common endocrine- and cell specific-enhancer regions that can be navigated with minimal bioinformatics expertise.

Both alpha and delta cells appear poised, but repressed, from becoming beta cells in murine pancreatic islets. These data broadly support earlier findings on the plasticity in identity of non-beta cells under certain circumstances. Furthermore, differential chromatin accessibility shows preferentially enriched distal-intergenic regions in beta cells, when compared to either alpha or delta cells.

Cis-regulatory elements control gene expression and are dynamic in their structure, reflecting changes to the composition of diverse effector proteins over time1-3. Here we sought to connect the structural changes at cis-regulatory elements to alterations in cellular fate and function. To do this we developed PRINT, a computational method that uses deep learning to correct sequence bias in chromatin accessibility data and identifies multi-scale footprints of DNA-protein interactions. We find that multi-scale footprints enable more accurate inference of TF and nucleosome binding. Using PRINT with single-cell multi-omics, we discover wide-spread changes to the structure and function of candidate cis-regulatory elements (cCREs) across hematopoiesis, wherein nucleosomes slide, expose DNA for TF binding, and promote gene expression. Activity segmentation using the co-variance across cell states identifies "sub-cCREs" as modular cCRE subunits of regulatory DNA. We apply this single-cell and PRINT approach to characterize the age-associated alterations to cCREs within hematopoietic stem cells (HSCs). Remarkably, we find a spectrum of aging alterations among HSCs corresponding to a global gain of sub-cCRE activity while preserving cCRE accessibility. Collectively, we reveal the functional importance of cCRE structure across cell states, highlighting changes to gene regulation at single-cell and single-base-pair resolution.

Lineage plasticity has been proposed as a major source of intratumoral heterogeneity and therapeutic resistance. Here, by employing an inducible genetic engineered mouse model, we illustrate that lineage plasticity enables advanced Pancreatic Ductal Adenocarcinoma (PDAC) tumors to develop spontaneous relapse following elimination of the central oncogenic driver - Yap. Transcriptomic and immunohistochemistry analysis of a large panel of PDAC tumors reveals that within high-grade tumors, small niches of PDAC cells gradually evolve to re-activate pluripotent transcription factors (PTFs), which lessen their dependency on Yap. Comprehensive Cut&Tag analysis demonstrate that although acquisition of PTF expression is coupled with the process of epithelial-to-mesenchymal transition (EMT), PTFs form a core transcriptional regulatory circuitry (CRC) with Jun to overcome Yap dependency, which is distinct from the classic TGFb-induced EMT-TF network. A chemical-genetic screen and follow-up functional studies establish Brd4 as an epigenetic gatekeeper for the PTF-Jun CRC, and strong synergy between BET and Yap inhibitors in blocking PDAC growth.

Lineage-defining transcription factors form densely interconnected circuits in chromatin occupancy assays, but the functional significance of these networks remains underexplored. We reconstructed the functional topology of a leukemia cell transcription network from the direct gene-regulatory programs of eight core transcriptional regulators established in pre-steady state assays coupling targeted protein degradation with nascent transcriptomics. The core regulators displayed narrow, largely non-overlapping direct transcriptional programs, forming a sparsely interconnected functional hierarchy stabilized by incoherent feed-forward loops. BET bromodomain and CDK7 inhibitors disrupted the core regulators' direct programs, acting as mixed agonists/antagonists. The network is predictive of dynamic gene expression behaviors in time-resolved assays and clinically relevant pathway activity in patient populations.