Emerging properties of animal gene regulatory networks

Specificity landscapes of 40 R2R3-MYBs reveal how paralogs target different cis-elements by homodimeric binding

Paralogous transcription factors (TFs) frequently recognize highly similar DNA motifs. Homodimerization can help distinguish them according to their different dimeric configurations. Here, by studying R2R3-MYB TFs, we show that homodimerization can also directly change the recognized DNA motifs to distinguish between similar TFs. By high-throughput SELEX, we profiled the specificity landscape for 40 R2R3-MYBs of subfamily VIII and curated 833 motif models. The dimeric models show that homodimeric binding has evoked specificity changes for AtMYBs. Focusing on AtMYB2 as an example, we show that homodimerization has modified its specificity and allowed it to recognize additional cis-regulatory sequences that are different from the closely related CCWAA-box AtMYBs and are unique among all AtMYBs. Genomic sites described by the modified dimeric specificities of AtMYB2 are conserved in evolution and involved in AtMYB2-specific transcriptional activation. Collectively, this study provides rich data on sequence preferences of VIII R2R3-MYBs and suggests an alternative mechanism that guides closely related TFs to respective cis-regulatory sites.

From genes to patterns: a framework for modeling the emergence of embryonic development from transcriptional regulation

Understanding embryonic patterning, the process by which groups of cells are partitioned into distinct identities defined by gene expression, is a central challenge in developmental biology. This complex phenomenon is driven by precise spatial and temporal regulation of gene expression across many cells, resulting in the emergence of highly organized tissue structures. While similar emergent behavior is well understood in other fields, such as statistical mechanics, the regulation of gene expression in development remains less clear, particularly regarding how molecular-level gene interactions lead to the large-scale patterns observed in embryos. In this study, we present a modeling framework that bridges the gap between molecular gene regulation and tissue-level embryonic patterning. Beginning with basic chemical reaction models of transcription at the single-gene level, we progress to model gene regulatory networks (GRNs) that mediate specific cellular functions. We then introduce phenomenological models of pattern formation, including the French Flag and Temporal Patterning/Speed Regulation models, and integrate them with molecular/GRN realizations. To facilitate understanding and application of our models, we accompany our mathematical framework with computer simulations, providing intuitive and simple code for each model. A key feature of our framework is the explicit articulation of underlying assumptions at each level of the model, from transcriptional regulation to tissue patterning. By making these assumptions clear, we provide a foundation for future experimental and theoretical work to critically examine and challenge them, thereby improving the accuracy and relevance of gene regulatory models in developmental biology. As a case study, we explore how different strategies for integrating enhancer activity affect the robustness and evolvability of GRNs that govern embryonic pattern formation. Our simulations suggest that a two-step regulation strategy, enhancer activation followed by competitive integration at the promoter, ensures more standardized integration of new enhancers into developmental GRNs, highlighting the adaptability of eukaryotic transcription. These findings shed new light on the transcriptional mechanisms underlying embryonic patterning, while the overall modeling framework serves as a foundation for future experimental and theoretical investigations.

Optogenetic manipulation of nuclear Dorsal reveals temporal requirements and consequences for transcription

Morphogen gradients convey essential spatial information during tissue patterning. Although the concentration and timing of morphogen exposure are both crucial, how cells interpret these graded inputs remains challenging to address. We employed an optogenetic system to acutely and reversibly modulate the nuclear concentration of the morphogen Dorsal (DL), homolog of NF-κB, which orchestrates dorsoventral patterning in the Drosophila embryo. By controlling DL nuclear concentration while simultaneously recording target gene outputs in real time, we identified a critical window for DL action that is required to instruct patterning and characterized the resulting effect on spatiotemporal transcription of target genes in terms of timing, coordination and bursting. We found that a transient decrease in nuclear DL levels at nuclear cycle 13 leads to reduced expression of the mesoderm-associated gene snail (sna) and partial derepression of the neurogenic ectoderm-associated target short gastrulation (sog) in ventral regions. Surprisingly, the mispatterning elicited by this transient change in DL was detectable at the level of single-cell transcriptional bursting kinetics, specifically affecting long inter-burst durations. Our approach of using temporally resolved and reversible modulation of a morphogen in vivo, combined with mathematical modeling, establishes a framework for understanding the stimulus-response relationships that govern embryonic patterning.

The combinatorial binding syntax of transcription factors in forebrain-specific enhancers

Tissue-specific gene regulation in mammals involves the coordinated binding of multiple transcription factors (TFs). Using the forebrain as a model, we investigated the syntax of TF occupancy to determine tissue-specific enhancer regions. We analyzed forebrain-exclusive enhancers from the VISTA Enhancer Browser and a curated set of 23 TFs relevant to forebrain development and disease. Our findings revealed multiple distinct patterns of combinatorial TF binding, with the HES5-FOXP2-GATA3 triad being the most frequent in forebrain-specific enhancers. This syntactic structure was detected in 2614 enhancers from a genome-wide catalog of 25,000 predicted human forebrain enhancers. Notably, this catalog represents a computationally predicted dataset, distinct from the in vivo validated set of enhancers obtained from the VISTA Enhancer Browser. The shortlisted 2614 enhancers were further analyzed using genome-wide epigenetic data and evaluated for evolutionary conservation and disease relevance. Our findings highlight the value of these 2614 enhancers in forebrain-specific gene regulation and provide a framework for discovering tissue-specific enhancers, enhancing the understanding of enhancer function.

The proximity-based protein interactome and regulatory logics of the transcription factor p65 NF-κB/RELA

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.

AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks

Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these 'behavioral catalogs' for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

Drivers of vessel progenitor fate define intermediate mesoderm dimensions by inhibiting kidney progenitor specification

Proper organ formation depends on the precise delineation of organ territories containing defined numbers of progenitor cells. Kidney progenitors reside in bilateral stripes of posterior mesoderm that are referred to as the intermediate mesoderm (IM). Previously, we showed that the transcription factors Hand2 and Osr1 act to strike a balance between the specification of the kidney progenitors in the IM and the vessel progenitors in the laterally adjacent territory. Recently, the transcription factor Npas4l - an early and essential driver of vessel and blood progenitor formation - was shown to inhibit kidney development. Here we demonstrate how kidney progenitor specification is coordinated by hand2, osr1, and npas4l. We find that npas4l and the IM marker pax2a are transiently co-expressed in the posterior lateral mesoderm, and npas4l is necessary to inhibit IM formation. Consistent with the expression of npas4l flanking the medial and lateral sides of the IM, our findings suggest roles for npas4l in defining the IM boundaries at each of these borders. At the lateral IM border, hand2 promotes and osr1 inhibits the formation of npas4l-expressing lateral vessel progenitors, and hand2 requires npas4l to inhibit IM formation and to promote vessel formation. Meanwhile, npas4l appears to have an additional role in suppressing IM fate at the medial border: npas4l loss-of-function enhances hand2 mutant IM defects and results in excess IM generated outside of the lateral hand2-expressing territory. Together, our findings reveal that establishment of the medial and lateral boundaries of the IM requires inhibition of kidney progenitor specification by the neighboring drivers of vessel progenitor fate.

Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo

Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results. Here, we present SDEvelo, a generative approach to inferring RNA velocity by modeling the dynamics of unspliced and spliced RNAs via multivariate stochastic differential equations (SDE). Uniquely, SDEvelo explicitly models inherent uncertainty in transcriptional dynamics while estimating a cell-specific latent time across genes. Using both simulated and four scRNA-seq and spatial transcriptomics datasets, we show that SDEvelo can model the random dynamic patterns of mature-state cells while accurately detecting carcinogenesis. Additionally, the estimated gene-shared latent time can facilitate many downstream analyses for biological discovery. We demonstrate that SDEvelo is computationally scalable and applicable to both scRNA-seq and sequencing-based spatial transcriptomics data.

Foxo3-mediated physiological cell competition ensures robust tissue patterning throughout vertebrate development

Unfit cells with defective signalling or gene expression are eliminated through competition with neighbouring cells. However, physiological roles and mechanisms of cell competition in vertebrates remain unclear. In addition, universal mechanisms regulating diverse cell competition are unknown. Using zebrafish imaging, we reveal that cell competition ensures robust patterning of the spinal cord and muscle through elimination of cells with unfit sonic hedgehog activity, driven by cadherin-mediated communication between unfit and neighbouring fit cells and subsequent activation of the Smad-Foxo3-reactive oxygen species axis. We identify Foxo3 as a common marker of loser cells in various types of cell competition in zebrafish and mice. Foxo3-mediated physiological cell competition is required for eliminating various naturally generated unfit cells and for the consequent precise patterning during zebrafish embryogenesis and organogenesis. Given the implication of Foxo3 downregulation in age-related diseases, cell competition may be a defence system to prevent abnormalities throughout development and adult homeostasis.

LM-Merger: A workflow for merging logical models with an application to gene regulation

Motivation

Gene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of automated approaches for merging existing models.

Results

We present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (d) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response. This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases.

Availability and implementation

The workflow and accompanying tools, including modules for model standardization, automated logical model merging, and evaluation, are available at https://github.com/IlyaLab/LogicModelMerger/.

Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives

BACKGROUND

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM OF REVIEW

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Advantage looping: Gene regulatory circuits between microRNAs and their target transcription factors in plants

The 20 to 24 nucleotide microRNAs (miRNAs) and their target transcription factors (TF) have emerged as key regulators of diverse processes in plants, including organ development and environmental resilience. In several instances, the mature miRNAs degrade the TF-encoding transcripts, while their protein products in turn bind to the promoters of the respective miRNA-encoding genes and regulate their expression, thus forming feedback loops (FBLs) or feedforward loops (FFLs). Computational analysis suggested that such miRNA-TF loops are recurrent motifs in gene regulatory networks (GRNs) in plants as well as animals. In recent years, modeling and experimental studies have suggested that plant miRNA-TF loops in GRNs play critical roles in driving organ development and abiotic stress responses. Here, we discuss the miRNA-TF FBLs and FFLs that have been identified and studied in plants over the past decade. We then provide some insights into the possible roles of such motifs within GRNs. Lastly, we provide perspectives on future directions for dissecting the functions of miRNA-centric GRNs in plants.

Edge Removal and Q-Learning for Stabilizability of Boolean Networks

This article develops a new edge removal mechanism for the global stabilizability of Boolean networks (BNs). In order to achieve the edge removal control, several control variables are properly placed into the dynamics of BNs based on the fundamental logical operators. On the basis of the new edge removal mechanism, several necessary and sufficient conditions are obtained for the global stabilizability and set stabilizability of BNs. Furthermore, a kind of stable edge removal control is proposed and achieved via the -learning algorithm to optimize the edge removal mechanism. As an application, the edge removal control is used to verify whether or not the mammalian cortical area development model can be made stabilizable to the expected stable states.

Optogenetic manipulation of nuclear Dorsal reveals temporal requirements and consequences for transcription

Morphogen gradients convey essential spatial information during tissue patterning. While both concentration and timing of morphogen exposure are crucial, how cells interpret these graded inputs remains challenging to address. We employed an optogenetic system to acutely and reversibly modulate the nuclear concentration of the morphogen Dorsal (DL), homologue of NF-κB, which orchestrates dorso-ventral patterning in the Drosophila embryo. By controlling DL nuclear concentration while simultaneously recording target gene outputs in real time, we identified a critical window for DL action that is required to instruct patterning, and characterized the resulting effect on spatio-temporal transcription of target genes in terms of timing, coordination, and bursting. We found that a transient decrease in nuclear DL levels at nuclear cycle 13 leads to reduced expression of the mesoderm-associated gene snail (sna) and partial derepression of the neurogenic ectoderm-associated target short gastrulation ( sog) in ventral regions. Surprisingly, the mispatterning elicited by this transient change in DL is detectable at the level of single cell transcriptional bursting kinetics, specifically affecting long inter-burst durations. Our approach of using temporally-resolved and reversible modulation of a morphogen in vivo , combined with mathematical modeling, establishes a framework for understanding the stimulus-response relationships that govern embryonic patterning.

Recurrent hyper-motif circuits in developmental programs

During embryogenesis, homogenous groups of cells self-organize into stereotypic spatial and temporal patterns that make up tissues and organs. These emergent patterns are controlled by transcription factors and secreted signals that regulate cellular fate and behaviors through intracellular regulatory circuits and cell-cell communication circuits. However, the principles of these circuits and how their properties are combined to provide the spatio-temporal properties of tissues remain unclear. Here we develop a framework to explore building-block circuits of developmental programs. We use single-cell gene expression data across developmental stages of the human intestine to infer the key intra- and inter-cellular circuits that control developmental programs. We study how these circuits are joined into higher-level hyper-motif circuits and explore their emergent dynamical properties. This framework uncovers design principles of developmental programs and reveals the rules that allow cells to develop robust and diverse patterns.

Characterization of Single-Cell Cis-regulatory Elements Informs Implications for Cell Differentiation

Cis-regulatory elements govern the specific patterns and dynamics of gene expression in cells during development, which are the fundamental mechanisms behind cell differentiation. However, the genomic characteristics of single-cell cis-regulatory elements closely linked to cell differentiation during development remain unclear. To explore this, we systematically analyzed ∼250,000 putative single-cell cis-regulatory elements obtained from snATAC-seq analysis of the developing mouse cerebellum. We found that over 80% of these single-cell cis-regulatory elements show pleiotropic effects, being active in 2 or more cell types. The pleiotropic degrees of proximal and distal single-cell cis-regulatory elements are positively correlated with the density and diversity of transcription factor binding motifs and GC content. There is a negative correlation between the pleiotropic degrees of single-cell cis-regulatory elements and their distances to the nearest transcription start sites, and proximal single-cell cis-regulatory elements display higher relevance strengths than distal ones. Furthermore, both proximal and distal single-cell cis-regulatory elements related to cell differentiation exhibit enhanced sequence-level evolutionary conservation, increased density and diversity of transcription factor binding motifs, elevated GC content, and greater distances from their nearest genes. Together, our findings reveal the general genomic characteristics of putative single-cell cis-regulatory elements and provide insights into the genomic and evolutionary mechanisms by which single-cell cis-regulatory elements regulate cell differentiation during development.

Gene Expression Shifts in Emperor Penguin Adaptation to the Extreme Antarctic Environment

Gene expression can accelerate ecological divergence by rapidly tweaking the response of an organism to novel environments, with more divergent environments exerting stronger selection and supposedly, requiring faster adaptive responses. Organisms adapted to extreme environments provide ideal systems to test this hypothesis, particularly when compared to related species with milder ecological niches. The Emperor penguin (Aptenodytes forsteri) is the only endothermic vertebrate breeding in the harsh Antarctic winter, in stark contrast with the less cold-adapted sister species, the King penguin (A. patagonicus). Assembling the first de novo transcriptomes and analysing multi-tissue (brain, kidney, liver, muscle, skin) RNA-Seq data from natural populations of both species, we quantified the shifts in tissue-enhanced genes, co-expression gene networks, and differentially expressed genes characterising Emperor penguin adaptation to the extreme Antarctic. Our analyses revealed the crucial role played by muscle and liver in temperature homeostasis, fasting, and whole-body energy metabolism (glucose/insulin regulation, lipid metabolism, fatty acid beta-oxidation, and blood coagulation). Repatterning at the regulatory level appears as more important in the brain of the Emperor penguin, showing the lowest signature of differential gene expression, but the largest co-expression gene network shift. Nevertheless, over-expressed genes related to mTOR signalling in the brain and the liver support their central role in cold and fasting responses. Besides contributing to understanding the genetics underlying complex traits, like body energy reservoir management, our results provide a first insight into the role of gene expression in adaptation to one of the most extreme environmental conditions endured by an endotherm.

Core genes of biomineralization and cis-regulatory long non-coding RNA regulate shell growth in bivalves

INTRODUCTION

Bivalve molluscs are abundant in marine and freshwater systems and contribute essential ecosystem services. They are characterized by an exuberant diversity of biomineralized shells and typically have two symmetric valves (a.k.a shells), but oysters (Ostreidae), some clams (Anomiidae and Chamidae) and scallops (Pectinida) have two asymmetrical valves. Predicting and modelling the likely consequences of ocean acidification on bivalve survival, biodiversity and aquaculture makes understanding shell biomineralization and its regulation a priority.

OBJECTIVES

This study aimed to a) exploit the atypical asymmetric shell growth of some bivalves and through comparative analysis of the genome and transcriptome pinpoint candidate biomineralization-related genes and regulatory long non-coding RNAs (LncRNAs) and b) demonstrate their roles in regulating shell biomineralization/growth.

METHODS

Meta-analysis of genomes, de novo generated mantle transcriptomes or transcriptomes and proteomes from public databases for six asymmetric to symmetric bivalve species was used to identify biomineralization-related genes. Bioinformatics filtering uncovered genes and regulatory modules characteristic of bivalves with asymmetric shells and identified candidate biomineralization-related genes and lncRNAs with a biased expression in asymmetric valves. A shell regrowth model in oyster and gene silencing experiments, were used to characterize candidate gene function.

RESULTS

Shell matrix genes with asymmetric expression in the mantle of the two valves were identified and unique cis-regulatory lncRNA modules characterized in Ostreidae. LncRNAs that regulate the expression of the tissue inhibitor of metalloproteinases gene family (TIMPDR) and of the shell matrix protein domain family (SMPDR) were identified. In vitro and in vivo silencing experiments revealed the candidate genes and lncRNA were associated with divergent shell growth rates and modified the microstructure of calcium carbonate (CaCO3) crystals.

CONCLUSION

LncRNAs are putative regulatory factors of the bivalve biomineralization toolbox. In the Ostreidae family of bivalves biomineralization-related genes are cis-regulated by lncRNA and modify the planar growth rate and spatial orientation of crystals in the shell.

Dissecting the regulatory logic of specification and differentiation during vertebrate embryogenesis

The interplay between transcription factors and chromatin accessibility regulates cell type diversification during vertebrate embryogenesis. To systematically decipher the gene regulatory logic guiding this process, we generated a single-cell multi-omics atlas of RNA expression and chromatin accessibility during early zebrafish embryogenesis. We developed a deep learning model to predict chromatin accessibility based on DNA sequence and found that a small number of transcription factors underlie cell-type-specific chromatin landscapes. While Nanog is well-established in promoting pluripotency, we discovered a new function in priming the enhancer accessibility of mesendodermal genes. In addition to the classical stepwise mode of differentiation, we describe instant differentiation, where pluripotent cells skip intermediate fate transitions and terminally differentiate. Reconstruction of gene regulatory interactions reveals that this process is driven by a shallow network in which maternally deposited regulators activate a small set of transcription factors that co-regulate hundreds of differentiation genes. Notably, misexpression of these transcription factors in pluripotent cells is sufficient to ectopically activate their targets. This study provides a rich resource for analyzing embryonic gene regulation and reveals the regulatory logic of instant differentiation.

Cell cycle-driven transcriptome maturation confers multilineage competence to cardiopharyngeal progenitors

During development, stem and progenitor cells divide and transition through germ layer- and lineage-specific multipotent states to generate the diverse cell types that compose an animal. Defined changes in biomolecular composition underlie the progressive loss of potency and acquisition of lineage-specific characteristics. For example, multipotent cardiopharyngeal progenitors display multilineage transcriptional priming, whereby both the cardiac and pharyngeal muscle programs are partially active and coexist in the same progenitor cells, while their daughter cells engage in a cardiac or pharyngeal muscle differentiation path only after cell division. Here, using the tunicate Ciona, we studied the acquisition of multilineage competence and the coupling between fate decisions and cell cycle progression. We showed that multipotent cardiopharyngeal progenitors acquire the competence to produce distinct Tbx1/10(+) and (-) daughter cells shortly before mitosis, which is necessary for Tbx1/10 activation. By combining transgene-based sample barcoding with single cell RNA-seq (scRNA-seq), we uncovered transcriptome-wide dynamics in migrating cardiopharyngeal progenitors as cells progress through G1, S and G2 phases. We termed this process "transcriptome maturation", and identified candidate "mature genes", including the Rho GAP-coding gene Depdc1, which peak in late G2. Functional assays indicated that transcriptome maturation fosters cardiopharyngeal competence, in part through multilineage priming and proper oriented and asymmetric division that influences subsequent fate decisions, illustrating the concept of "behavioral competence". Both classic feedforward circuits and coupling with cell cycle progression drive transcriptome maturation, uncovering distinct levels of coupling between cell cycle progression and fateful molecular transitions. We propose that coupling competence and fate decision with the G2 and G1 phases, respectively, ensures the timely deployment of lineage-specific programs.

A hierarchy index for networks in the brain reveals a complex entangled organizational structure

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.

SAILoR: Structure-Aware Inference of Logic Rules

Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.

Establishing a conceptual framework for holistic cell states and state transitions

Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.

Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics

The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.

Genomic context- and H2AK119 ubiquitination-dependent inheritance of human Polycomb silencing

Polycomb repressive complexes 1 and 2 (PRC1 and 2) are required for heritable repression of developmental genes. The cis- and trans-acting factors that contribute to epigenetic inheritance of mammalian Polycomb repression are not fully understood. Here, we show that, in human cells, ectopically induced Polycomb silencing at initially active developmental genes, but not near ubiquitously expressed housekeeping genes, is inherited for many cell divisions. Unexpectedly, silencing is heritable in cells with mutations in the H3K27me3 binding pocket of the Embryonic Ectoderm Development (EED) subunit of PRC2, which are known to disrupt H3K27me3 recognition and lead to loss of H3K27me3. This mode of inheritance is less stable and requires intact PRC2 and recognition of H2AK119ub1 by PRC1. Our findings suggest that maintenance of Polycomb silencing is sensitive to local genomic context and can be mediated by PRC1-dependent H2AK119ub1 and PRC2 independently of H3K27me3 recognition.

Single-cell transcriptomics reveals evolutionary reconfiguration of embryonic cell fate specification in the sea urchin Heliocidaris erythrogramma

Altered regulatory interactions during development likely underlie a large fraction of phenotypic diversity within and between species, yet identifying specific evolutionary changes remains challenging. Analysis of single-cell developmental transcriptomes from multiple species provides a powerful framework for unbiased identification of evolutionary changes in developmental mechanisms. Here, we leverage a "natural experiment" in developmental evolution in sea urchins, where a major life history switch recently evolved in the lineage leading to Heliocidaris erythrogramma, precipitating extensive changes in early development. Comparative analyses of scRNA-seq developmental time courses from H. erythrogramma and Lytechinus variegatus (representing the derived and ancestral states respectively) reveals numerous evolutionary changes in embryonic patterning. The earliest cell fate specification events, and the primary signaling center are co-localized in the ancestral dGRN but remarkably, in H. erythrogramma they are spatially and temporally separate. Fate specification and differentiation are delayed in most embryonic cell lineages, although in some cases, these processes are conserved or even accelerated. Comparative analysis of regulator-target gene co-expression is consistent with many specific interactions being preserved but delayed in H. erythrogramma, while some otherwise widely conserved interactions have likely been lost. Finally, specific patterning events are directly correlated with evolutionary changes in larval morphology, suggesting that they are directly tied to the life history shift. Together, these findings demonstrate that comparative scRNA-seq developmental time courses can reveal a diverse set of evolutionary changes in embryonic patterning and provide an efficient way to identify likely candidate regulatory interactions for subsequent experimental validation.

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation

BACKGROUND

Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging.

RESULTS

Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters.

CONCLUSION

Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

BloodChIP Xtra: an expanded database of comparative genome-wide transcription factor binding and gene-expression profiles in healthy human stem/progenitor subsets and leukemic cells

The BloodChIP Xtra database (http://bloodchipXtra.vafaeelab.com/) facilitates genome-wide exploration and visualization of transcription factor (TF) occupancy and chromatin configuration in rare primary human hematopoietic stem (HSC-MPP) and progenitor (CMP, GMP, MEP) cells and acute myeloid leukemia (AML) cell lines (KG-1, ME-1, Kasumi1, TSU-1621-MT), along with chromatin accessibility and gene expression data from these and primary patient AMLs. BloodChIP Xtra features significantly more datasets than our earlier database BloodChIP (two primary cell types and two cell lines). Improved methodologies for determining TF occupancy and chromatin accessibility have led to increased availability of data for rare primary cell types across the spectrum of healthy and AML hematopoiesis. However, there is a continuing need for these data to be integrated in an easily accessible manner for gene-based queries and use in downstream applications. Here, we provide a user-friendly database based around genome-wide binding profiles of key hematopoietic TFs and histone marks in healthy stem/progenitor cell types. These are compared with binding profiles and chromatin accessibility derived from primary and cell line AML and integrated with expression data from corresponding cell types. All queries can be exported to construct TF-gene and protein-protein networks and evaluate the association of genes with specific cellular processes.

Lhx3/4 initiates a cardiopharyngeal-specific transcriptional program in response to widespread FGF signaling

Individual signaling pathways, such as fibroblast growth factors (FGFs), can regulate a plethora of inductive events. According to current paradigms, signal-dependent transcription factors (TFs), such as FGF/MapK-activated Ets family factors, partner with lineage-determining factors to achieve regulatory specificity. However, many aspects of this model have not been rigorously investigated. One key question relates to whether lineage-determining factors dictate lineage-specific responses to inductive signals or facilitate these responses in collaboration with other inputs. We utilize the chordate model Ciona robusta to investigate mechanisms generating lineage-specific induction. Previous studies in C. robusta have shown that cardiopharyngeal progenitor cells are specified through the combined activity of FGF-activated Ets1/2.b and an inferred ATTA-binding transcriptional cofactor. Here, we show that the homeobox TF Lhx3/4 serves as the lineage-determining TF that dictates cardiopharyngeal-specific transcription in response to pleiotropic FGF signaling. Targeted knockdown of Lhx3/4 leads to loss of cardiopharyngeal gene expression. Strikingly, ectopic expression of Lhx3/4 in a neuroectodermal lineage subject to FGF-dependent specification leads to ectopic cardiopharyngeal gene expression in this lineage. Furthermore, ectopic Lhx3/4 expression disrupts neural plate morphogenesis, generating aberrant cell behaviors associated with execution of incompatible morphogenetic programs. Based on these findings, we propose that combinatorial regulation by signal-dependent and lineage-determinant factors represents a generalizable, previously uncategorized regulatory subcircuit we term "cofactor-dependent induction." Integration of this subcircuit into theoretical models will facilitate accurate predictions regarding the impact of gene regulatory network rewiring on evolutionary diversification and disease ontogeny.

Engineering of a Self-Regulatory Bidirectional DNA Assembly Circuit for Amplified MicroRNA Imaging

The engineering of catalytic hybridization DNA circuits represents versatile ways to orchestrate a complex flux of molecular information at the nanoscale, with potential applications in DNA-encoded biosensing, drug discovery, and therapeutics. However, the diffusive escape of intermediates and unintentional binding interactions remain an unsolved challenge. Herein, we developed a compact, yet efficient, self-regulatory assembly circuit (SAC) for achieving robust microRNA (miRNA) imaging in live cells through DNA-templated guaranteed catalytic hybridization. By integrating the toehold strand with a preblocked palindromic fragment in the stem domain, the proposed miniature SAC system allows the reactant-to-template-controlled proximal hybridization, thus facilitating the bidirectional-sustained assembly and the localization-intensified signal amplification without undesired crosstalk. With condensed components and low reactant complexity, the SAC amplifier realized high-contrast intracellular miRNA imaging. We anticipate that this simple and template-controlled design can enrich the clinical diagnosis and prognosis toolbox.

From signalling to form: the coordination of neural tube patterning

The development of the vertebrate spinal cord involves the formation of the neural tube and the generation of multiple distinct cell types. The process starts during gastrulation, combining axial elongation with specification of neural cells and the formation of the neuroepithelium. Tissue movements produce the neural tube which is then exposed to signals that provide patterning information to neural progenitors. The intracellular response to these signals, via a gene regulatory network, governs the spatial and temporal differentiation of progenitors into specific cell types, facilitating the assembly of functional neuronal circuits. The interplay between the gene regulatory network, cell movement, and tissue mechanics generates the conserved neural tube pattern observed across species. In this review we offer an overview of the molecular and cellular processes governing the formation and patterning of the neural tube, highlighting how the remarkable complexity and precision of vertebrate nervous system arises. We argue that a multidisciplinary and multiscale understanding of the neural tube development, paired with the study of species-specific strategies, will be crucial to tackle the open questions.

Plasticity-led evolution as an intrinsic property of developmental gene regulatory networks

The modern evolutionary synthesis seemingly fails to explain how a population can survive a large environmental change: the pre-existence of heritable variants adapted to the novel environment is too opportunistic, whereas the search for new adaptive mutations after the environmental change is so slow that the population may go extinct. Plasticity-led evolution, the initial environmental induction of a novel adaptive phenotype followed by genetic accommodation, has been proposed to solve this problem. However, the mechanism enabling plasticity-led evolution remains unclear. Here, we present computational models that exhibit behaviors compatible with plasticity-led evolution by extending the Wagner model of gene regulatory networks. The models show adaptive plastic response and the uncovering of cryptic mutations under large environmental changes, followed by genetic accommodation. Moreover, these behaviors are consistently observed over distinct novel environments. We further show that environmental cues, developmental processes, and hierarchical regulation cooperatively amplify the above behaviors and accelerate evolution. These observations suggest plasticity-led evolution is a universal property of complex developmental systems independent of particular mutations.

Genome-wide transcription factor-binding maps reveal cell-specific changes in the regulatory architecture of human HSPCs

Hematopoietic stem and progenitor cells (HSPCs) rely on a complex interplay among transcription factors (TFs) to regulate differentiation into mature blood cells. A heptad of TFs (FLI1, ERG, GATA2, RUNX1, TAL1, LYL1, LMO2) bind regulatory elements in bulk CD34+ HSPCs. However, whether specific heptad-TF combinations have distinct roles in regulating hematopoietic differentiation remains unknown. We mapped genome-wide chromatin contacts (HiC, H3K27ac, HiChIP), chromatin modifications (H3K4me3, H3K27ac, H3K27me3) and 10 TF binding profiles (heptad, PU.1, CTCF, STAG2) in HSPC subsets (stem/multipotent progenitors plus common myeloid, granulocyte macrophage, and megakaryocyte erythrocyte progenitors) and found TF occupancy and enhancer-promoter interactions varied significantly across cell types and were associated with cell-type-specific gene expression. Distinct regulatory elements were enriched with specific heptad-TF combinations, including stem-cell-specific elements with ERG, and myeloid- and erythroid-specific elements with combinations of FLI1, RUNX1, GATA2, TAL1, LYL1, and LMO2. Furthermore, heptad-occupied regions in HSPCs were subsequently bound by lineage-defining TFs, including PU.1 and GATA1, suggesting that heptad factors may prime regulatory elements for use in mature cell types. We also found that enhancers with cell-type-specific heptad occupancy shared a common grammar with respect to TF binding motifs, suggesting that combinatorial binding of TF complexes was at least partially regulated by features encoded in DNA sequence motifs. Taken together, this study comprehensively characterizes the gene regulatory landscape in rare subpopulations of human HSPCs. The accompanying data sets should serve as a valuable resource for understanding adult hematopoiesis and a framework for analyzing aberrant regulatory networks in leukemic cells.

Dissecting and improving gene regulatory network inference using single-cell transcriptome data

Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. Although a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell data sets, we showed that target genes' mature mRNA levels often fail to accurately report upstream regulatory activities because of gene-level and network-level factors, which can be improved by using pre-mRNA levels. We tested this finding on public single-cell RNA-seq data sets using intronic reads as proxies of pre-mRNA levels and can indeed achieve a higher inference accuracy compared to using exonic reads (corresponding to mature mRNAs). Using experimental data sets, we further validated findings from the simulated data sets and identified factors such as transcription factor activity dynamics influencing the accuracy of pre-mRNA-based inference. This work delineates the fundamental limitations of gene regulatory inference and helps improve GRN inference using single-cell RNA-seq data.

Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.

Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.

An atlas of gene regulatory networks for memory CD4 + T cells in youth and old age

Aging profoundly affects immune-system function, promoting susceptibility to pathogens, cancers and chronic inflammation. We previously identified a population of IL-10-producing, T follicular helper-like cells (" Tfh10 "), linked to suppressed vaccine responses in aged mice. Here, we integrate single-cell ( sc )RNA-seq, scATAC-seq and genome-scale modeling to characterize Tfh10 - and the full CD4 + memory T cell ( CD4 + TM ) compartment - in young and old mice. We identified 13 CD4 + TM populations, which we validated through cross-comparison to prior scRNA-seq studies. We built gene regulatory networks ( GRNs ) that predict transcription-factor control of gene expression in each T-cell population and how these circuits change with age. Through integration with pan-cell aging atlases, we identified intercellular-signaling networks driving age-dependent changes in CD4 + TM. Our atlas of finely resolved CD4 + TM subsets, GRNs and cell-cell communication networks is a comprehensive resource of predicted regulatory mechanisms operative in memory T cells, presenting new opportunities to improve immune responses in the elderly.

Leukemia core transcriptional circuitry is a sparsely interconnected hierarchy stabilized by incoherent feed-forward loops

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.

Altered transcriptome-proteome coupling indicates aberrant proteostasis in Parkinson's disease

Aberrant proteostasis is thought to be implicated in Parkinson's disease (PD), but patient-derived evidence is scant. We hypothesized that impaired proteostasis is reflected as altered transcriptome-proteome correlation in the PD brain. We integrated transcriptomic and proteomic data from prefrontal cortex of PD patients and young and aged controls to assess RNA-protein correlations across samples. The aged brain showed a genome-wide decrease in mRNA-protein correlation. Genes encoding synaptic vesicle proteins showed negative correlations, likely reflecting spatial separation of mRNA and protein into soma and synapses. PD showed a broader transcriptome-proteome decoupling, consistent with a proteome-wide decline in proteostasis. Genes showing negative correlation in PD were enriched for proteasome subunits, indicating accentuated spatial separation of transcript and protein in PD neurons. In addition, PD showed positive correlations for mitochondrial respiratory chain genes, suggesting a tighter regulation in the face of mitochondrial dysfunction. Our results support the hypothesis that aberrant proteasomal function is implicated in PD pathogenesis.

Multi-modular structure of the gene regulatory network for specification and commitment of murine T cells

T cells develop from multipotent progenitors by a gradual process dependent on intrathymic Notch signaling and coupled with extensive proliferation. The stages leading them to T-cell lineage commitment are well characterized by single-cell and bulk RNA analyses of sorted populations and by direct measurements of precursor-product relationships. This process depends not only on Notch signaling but also on multiple transcription factors, some associated with stemness and multipotency, some with alternative lineages, and others associated with T-cell fate. These factors interact in opposing or semi-independent T cell gene regulatory network (GRN) subcircuits that are increasingly well defined. A newly comprehensive picture of this network has emerged. Importantly, because key factors in the GRN can bind to markedly different genomic sites at one stage than they do at other stages, the genes they significantly regulate are also stage-specific. Global transcriptome analyses of perturbations have revealed an underlying modular structure to the T-cell commitment GRN, separating decisions to lose "stem-ness" from decisions to block alternative fates. Finally, the updated network sheds light on the intimate relationship between the T-cell program, which depends on the thymus, and the innate lymphoid cell (ILC) program, which does not.

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built "maxATAC", a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

Learning in Transcriptional Network Models: Computational Discovery of Pathway-Level Memory and Effective Interventions

Trainability, in any substrate, refers to the ability to change future behavior based on past experiences. An understanding of such capacity within biological cells and tissues would enable a particularly powerful set of methods for prediction and control of their behavior through specific patterns of stimuli. This top-down mode of control (as an alternative to bottom-up modification of hardware) has been extensively exploited by computer science and the behavioral sciences; in biology however, it is usually reserved for organism-level behavior in animals with brains, such as training animals towards a desired response. Exciting work in the field of basal cognition has begun to reveal degrees and forms of unconventional memory in non-neural tissues and even in subcellular biochemical dynamics. Here, we characterize biological gene regulatory circuit models and protein pathways and find them capable of several different kinds of memory. We extend prior results on learning in binary transcriptional networks to continuous models and identify specific interventions (regimes of stimulation, as opposed to network rewiring) that abolish undesirable network behavior such as drug pharmacoresistance and drug sensitization. We also explore the stability of created memories by assessing their long-term behavior and find that most memories do not decay over long time periods. Additionally, we find that the memory properties are quite robust to noise; surprisingly, in many cases noise actually increases memory potential. We examine various network properties associated with these behaviors and find that no one network property is indicative of memory. Random networks do not show similar memory behavior as models of biological processes, indicating that generic network dynamics are not solely responsible for trainability. Rational control of dynamic pathway function using stimuli derived from computational models opens the door to empirical studies of proto-cognitive capacities in unconventional embodiments and suggests numerous possible applications in biomedicine, where behavior shaping of pathway responses stand as a potential alternative to gene therapy.

Alternative mammalian strategies leading towards gastrulation: losing polar trophoblast (Rauber's layer) or gaining an epiblast cavity

Using embryological data from 14 mammalian orders, the hypothesis is presented that in placental mammals, epiblast cavitation and polar trophoblast loss are alternative developmental solutions to shield the central epiblast from extraembryonic signalling. It is argued that such reciprocal signalling between the edge of the epiblast and the adjoining polar trophoblast or edge of the mural trophoblast or with the amniotic ectoderm is necessary for the induction of gastrulation. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.

Fishing for Developmental Regulatory Regions: Zebrafish Tissue-Specific ATAC-seq

Interactions between transcription factors and regulatory DNA can be described by gene regulatory networks. These networks provide a systems-level view of embryonic tissue development. Here, we describe a protocol for the isolation, identification, and experimental manipulation of tissue-specific cis-regulatory elements during zebrafish embryonic development using low-input ATAC-seq. With the methods described, genome-wide assessments of regulatory DNA in small populations of developing tissues can be identified, allowing for the construction of gene regulatory networks.

Cell fate decisions, transcription factors and signaling during early retinal development

The development of the vertebrate eyes is a complex process starting from anterior-posterior and dorso-ventral patterning of the anterior neural tube, resulting in the formation of the eye field. Symmetrical separation of the eye field at the anterior neural plate is followed by two symmetrical evaginations to generate a pair of optic vesicles. Next, reciprocal invagination of the optic vesicles with surface ectoderm-derived lens placodes generates double-layered optic cups. The inner and outer layers of the optic cups develop into the neural retina and retinal pigment epithelium (RPE), respectively. In vitro produced retinal tissues, called retinal organoids, are formed from human pluripotent stem cells, mimicking major steps of retinal differentiation in vivo. This review article summarizes recent progress in our understanding of early eye development, focusing on the formation the eye field, optic vesicles, and early optic cups. Recent single-cell transcriptomic studies are integrated with classical in vivo genetic and functional studies to uncover a range of cellular mechanisms underlying early eye development. The functions of signal transduction pathways and lineage-specific DNA-binding transcription factors are dissected to explain cell-specific regulatory mechanisms underlying cell fate determination during early eye development. The functions of homeodomain (HD) transcription factors Otx2, Pax6, Lhx2, Six3 and Six6, which are required for early eye development, are discussed in detail. Comprehensive understanding of the mechanisms of early eye development provides insight into the molecular and cellular basis of developmental ocular anomalies, such as optic cup coloboma. Lastly, modeling human development and inherited retinal diseases using stem cell-derived retinal organoids generates opportunities to discover novel therapies for retinal diseases.

Decoding the IGF1 signaling gene regulatory network behind alveologenesis from a mouse model of bronchopulmonary dysplasia

Lung development is precisely controlled by underlying gene regulatory networks (GRN). Disruption of genes in the network can interrupt normal development and cause diseases such as bronchopulmonary dysplasia (BPD) - a chronic lung disease in preterm infants with morbid and sometimes lethal consequences characterized by lung immaturity and reduced alveolarization. Here, we generated a transgenic mouse exhibiting a moderate severity BPD phenotype by blocking IGF1 signaling in secondary crest myofibroblasts (SCMF) at the onset of alveologenesis. Using approaches mirroring the construction of the model GRN in sea urchin's development, we constructed the IGF1 signaling network underlying alveologenesis using this mouse model that phenocopies BPD. The constructed GRN, consisting of 43 genes, provides a bird's eye view of how the genes downstream of IGF1 are regulatorily connected. The GRN also reveals a mechanistic interpretation of how the effects of IGF1 signaling are transduced within SCMF from its specification genes to its effector genes and then from SCMF to its neighboring alveolar epithelial cells with WNT5A and FGF10 signaling as the bridge. Consistently, blocking WNT5A signaling in mice phenocopies BPD as inferred by the network. A comparative study on human samples suggests that a GRN of similar components and wiring underlies human BPD. Our network view of alveologenesis is transforming our perspective to understand and treat BPD. This new perspective calls for the construction of the full signaling GRN underlying alveologenesis, upon which targeted therapies for this neonatal chronic lung disease can be viably developed.