Dynamics Of Regulatory Networks Research Articles

The transcription factor GABPA is a master regulator of naïve pluripotency.

The establishment of naïve pluripotency is a continuous process starting with the generation of inner cell mass (ICM) which then differentiating into epiblast (EPI). Recent studies have revealed key transcription factors (TFs) for ICM formation, but which TFs initiate EPI specification remains unknown. Here, using a targeted rapid protein degradation system, we show that GABPA is not only a regulator of major ZGA, but also a master EPI specifier required for naïve pluripotency establishment by regulating 47% of EPI genes during E3.5 to E4.5 transition. Chromatin binding dynamics analysis suggests that GABPA controls EPI formation at least partly by binding to the ICM gene promoters occupied by the pluripotency regulators TFAP2C and SOX2 at E3.5 to establish naïve pluripotency at E4.5. Our study not only uncovers GABPA as a master pluripotency regulator, but also supports the notion that mammalian pluripotency establishment requires a dynamic and stepwise multi-TFs regulatory network.

The dynamic regulatory network of phosphatidic acid metabolism: a spotlight on substrate cycling between phosphatidic acid and diacylglycerol.

Mammalian cells utilize over 1000 different lipid species to maintain cell and organelle membrane properties, control cell signaling and processes, and store energy. Lipid synthesis and metabolism are mediated by highly interconnected and spatiotemporally regulated networks of lipid-metabolizing enzymes and supported by vesicle trafficking and lipid-transfer at membrane contact sites. However, the regulatory mechanisms that achieve lipid homeostasis are largely unknown. Phosphatidic acid (PA) serves as the central hub for phospholipid biosynthesis, acting as a key intermediate in both the Kennedy pathway and the CDP-DAG pathway. Additionally, PA is a potent signaling molecule involved in various cellular processes. This dual role of PA, both as a critical intermediate in lipid biosynthesis and as a significant signaling molecule, suggests that it is tightly regulated within cells. This minireview will summarize the functional diversity of PA molecules based on their acyl tail structures and subcellular localization, highlighting recent tools and findings that shed light on how the physical, chemical, and spatial properties of PA species contribute to their differential metabolic fates and functions. Dysfunctional effects of altered PA metabolism as well as the strategies cells employ to maintain PA regulation and homeostasis will also be discussed. Furthermore, this review will explore the differential regulation of PA metabolism across distinct subcellular membranes. Our recent proximity labeling studies highlight the possibility that substrate cycling between PA and DAG may be location-dependent and have functional significance in cell signaling and lipid homeostasis.

Dynamic transcriptomic and regulatory networks underpinning the transition from fetal primordial germ cells to spermatogonia in mice.

The transition from fetal primordial germ cells (PGCs) to spermatogonia (SPG) is critical for male germ cell development; however, the detailed transcriptomic dynamics and regulation underlying this transition remain poorly understood. Here by interrogating the comprehensive transcriptome atlas dataset of mouse male germ cells and gonadal cells development, we elucidated the regulatory networks underlying this transition. Our single-cell transcriptome analysis revealed that the transition from PGCs to SPG was characterized by global hypertranscription. A total of 315 highly active regulators were identified to be potentially involved in this transition, among which a non-transcription factor (TF) regulator TAGLN2 was validated to be essential for spermatogonial stem cells (SSCs) maintenance and differentiation. Metabolism profiling analysis also revealed dynamic changes in metabolism-related gene expression during PGC to SPG transition. Furthermore, we uncovered that intricate cell-cell communication exerted potential functions in the regulation of hypertranscription in germ cells by collaborating with stage-specific active regulators. Collectively, our work extends the understanding of molecular mechanisms underlying male germ cell development, offering insights into the recapitulation of germ cell generation in vitro.

The mathematical exploration for the mechanism of lung adenocarcinoma formation and progression.

Lung adenocarcinoma, a prevalent subtype of lung cancer, represents one of the most lethal human malignancies. Despite substantial efforts to elucidate its biological underpinnings, the underlying mechanisms governing lung adenocarcinoma remain enigmatic. Modeling and comprehending the dynamics of gene regulatory networks are crucial for unraveling the fundamental mechanisms of lung adenocarcinoma. Conventionally, the cancer is modeled as an equilibrium process based on a time-invariant gene regulatory network to investigate stable cell states. However, the cancer is a nonequilibrium process and the gene regulatory network should be regarded as time-varying in actual. Therefore, a feasible framework was developed to explore the formation and progression of lung adenocarcinoma. On the one hand, to delve into the underlying mechanisms of lung adenocarcinoma formation, the time-invariant gene regulatory network for lung adenocarcinoma was initially undertaken, and the composition of stable cell states was elucidated based on landscape theory. Furthermore, the plasticity of different states was quantified using energy landscape decomposition theory by incorporating cell proliferation. And transition probabilities between different states were defined to elucidate the transition between stable cell states. Additionally, the global sensitivity analysis was performed and a total of three genes and three regulations were identified to be more critical for the formation lung adenocarcinoma, offering a novel strategy for designing network-based therapies for its treatment. On the other hand, the time-invariant gene regulatory network is extended as time-varying to delve into the underlying mechanisms of lung adenocarcinoma progression. The lung adenocarcinoma progression was characterized as four different disease stages based on the mixed states of cell population and the evolutionary direction. And the progressionary mechanism of transition between stages was expounded by evaluating their dynamical transport, with the dynamical transport cost between different stages quantified using Wasserstein metrics.

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.

The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.

Machine learning reveals the transcriptional regulatory network and circadian dynamics of Synechococcus elongatus PCC 7942

Synechococcus elongatus is an important cyanobacterium that serves as a versatile and robust model for studying circadian biology and photosynthetic metabolism. Its transcriptional regulatory network (TRN) is of fundamental interest, as it orchestrates the cell's adaptation to the environment, including its response to sunlight. Despite the previous characterization of constituent parts of the S. elongatus TRN, a comprehensive layout of its topology remains to be established. Here, we decomposed a compendium of 300 high-quality RNA sequencing datasets of the model strain PCC 7942 using independent component analysis. We obtained 57 independently modulated gene sets, or iModulons, that explain 67% of the variance in the transcriptional response and 1) accurately reflect the activity of known transcriptional regulations, 2) capture functional components of photosynthesis, 3) provide hypotheses for regulon structures and functional annotations of poorly characterized genes, and 4) describe the transcriptional shifts under dynamic light conditions. This transcriptome-wide analysis of S. elongatus provides a quantitative reconstruction of the TRN and presents a knowledge base that can guide future investigations. Our systems-level analysis also provides a global TRN structure for S. elongatus PCC 7942.

Deciphering the dynamic single-cell transcriptional landscape in the ocular surface ectoderm differentiation system

Abstract The ocular surface ectoderm (OSE) is essential for the development of the ocular surface, yet the molecular mechanisms driving its differentiation are not fully understood. In this study, we used single-cell transcriptomic analysis to explore the dynamic cellular trajectories and regulatory networks during the in vitro differentiation of embryonic stem cells (ESCs) into the OSE lineage. We identified nine distinct cell subpopulations undergoing differentiation along three main developmental branches: neural crest, neuroectodermal, and surface ectodermal lineages. Key marker gene expression, transcription factor activity, and signaling pathway insights revealed stepwise transitions from undifferentiated ESCs to fate-specified cell types, including a PAX6 + TP63 + population indicative of OSE precursors. Comparative analysis with mouse embryonic development confirmed the model’s accuracy in mimicking in vivo epiblast-to-surface ectoderm dynamics. By integrating temporal dynamics of transcription factor activation and cell–cell communication, we constructed a comprehensive molecular atlas of the differentiation pathway from ESCs to distinct ectodermal lineages. This study provides new insights into the cellular heterogeneity and regulatory mechanisms of OSE development, aiding the understanding of ocular surface biology and the design of cell-based therapies for ocular surface disorders.

ERVcancer: a web resource designed for querying activation of human endogenous retroviruses across major cancer types

Human endogenous retroviruses (HERVs) comprise approximately 8% of the human genome, co-opted into the dynamic regulatory network of cellular potency in early embryonic development. In recent studies, resurgent HERVs' transcriptional activity has been frequently observed in many types of human cancers, suggesting their potential functions in the occurrence and progression of malignancy. However, a dedicated web resource for querying the relationship between the activation of HERVs and cancer development is lacking. Here, we construct a database to explore the sequence information, expression profiles, survival prognosis, and genetic interactions of HERVs in diverse cancer types. Our database currently contains RNA sequencing data of 580 HERVs across 16,246 samples, including that of 6478 tumoral and 634 normal tissues, 932 cancer cell lines, as well as 151 early embryonic and 8051 human adult tissues. The primary goal is to provide an easily accessible and user-friendly database for professionals in the fields of bioinformatics, pathology, pharmacology, and related areas, enabling them to efficiently screen the activity of HERVs of interest in normal and cancerous tissues and evaluate the clinical relevance. The ERVcancer database is available at http://kyuanlab.com/ervcancer/.

Integrative analyses of metabolome and transcriptome reveal the dynamic accumulation and regulatory network in rhizomes and fruits of Polygonatum cyrtonema Hua

BackgroundAccording to Chinese ancient books, both fruits and rhizomes of Polygonatum cyrtonema Hua have medicinal and edible values. Up to now, there is no report about the metabolite profiles and regulatory network in fruits and different year-old rhizomes of P. cyrtonema.ResultsIn this study, we performed integrative analyses of metabolome and transcriptome to reveal the dynamic accumulation and regulatory network of fruits and different year-old rhizomes in P. cyrtonema. The relative content of phenolic acids, lignans and coumarins, flavonoids and alkaloids increased with growth years, while steroids and lipids decreased with it. In addition, the relative content of nucleotides and derivatives, flavonoids, organic acids, steroids and lipids in fruits were higher than rhizomes. Genes that might relate to the biosynthesis of polysaccharides, flavonoids, triterpene saponins and alkaloids biosynthesis were further analyzed by transcriptome analysis, including sacA, GMPP, PMM, CCoAOMT, CHI, ANR, CHS, DXS, GGPS, ZEP, CYP72A219 and so on, for their expressions were positively correlated with the relative content of the metabolites. Additionally, the correlation network in sugar and aromatic amino acids metabolites were constructed to further illustrate the biosynthesis of polysaccharides, flavonoids and alkaloids in P. cyrtonema, and some transcription factors (TFs) were screened, such as C2C2, MYB, bZIP, GRAS and NAC.ConclusionsThis study can deepen our understanding of the accumulation patterns and molecular mechanism of the main compounds in P. cyrtonema, and provide reference for the standardize production of P. cyrtonema.

DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data.

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types.

Transcriptomic Insights into the Developmental Dynamics of Eimeria acervulina: A Comparative Study of a Precocious Line and the Wild Type.

Coccidiosis, a parasitic disease caused by single or multiple Eimeria species, leads to significant economic losses in the poultry industry. The Eimeria life cycle includes schizogony, gametogony, and sporogony. To investigate the dynamics of gene expression and regulatory networks during the development of Eimeria acervulina, we employed time-course transcriptomics to rigorously compare the gene expression patterns between a precocious line (PL) and the wild type (WT) of E. acervulina. The results revealed that the PL enters into gametogony 12 h earlier than the WT, and both the PL and WT exhibited distinct clustering patterns during the development phase. A weighted gene co-expression network analysis (WGCNA) identified genes specifically expressed at four distinct developmental stages, schizogony, gametogony, sporulated oocysts, and unsporulated oocysts, clarifying the key biological processes at each stage. This study used global transcriptome profiling to elucidate molecular variations throughout the E. acervulina life cycle, providing critical insights into molecular characterization and valuable resources for investigating other apicomplexan parasites of public health importance.

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.

Lattice structures that parameterize regulatory network dynamics

We consider two types of models of regulatory network dynamics: Boolean maps and systems of switching ordinary differential equations. Our goal is to construct all models in each category that are compatible with the directed signed graph that describe the network interactions. This leads to consideration of lattice of monotone Boolean functions (MBF), poset of non-degenerate MBFs, and a lattice of chains in these sets. We describe explicit inductive construction of these posets where the induction is on the number of inputs in MBF.Our results allow enumeration of potential dynamic behavior of the network for both model types, subject to practical limitation imposed by the size of the lattice of MBFs described by the Dedekind number.

Time-course swRNA-seq uncovers a hierarchical gene regulatory network in controlling the response-repair-remodeling after wounding

Wounding initiates intricate responses crucial for tissue repair and regeneration. Yet, the gene regulatory networks governing wound healing remain poorly understood. Here, employing single-worm RNA sequencing (swRNA-seq) across 12 time-points, we delineated a three-stage wound repair process in C. elegans: response, repair, and remodeling. Integrating diverse datasets, we constructed a dynamic regulatory network comprising 241 transcription regulators and their inferred targets. We identified potentially seven autoregulatory TFs and five cross-autoregulatory loops involving pqm-1 and jun-1. We revealed that TFs might interact with chromatin factors and form TF-TF combinatory modules via intrinsically disordered regions to enhance response robustness. We experimentally validated six regulators functioning in transcriptional and translocation-dependent manners. Notably, nhr-76, daf-16, nhr-84, and oef-1 are potentially required for efficient repair, while elt-2 may act as an inhibitor. These findings elucidate transcriptional responses and hierarchical regulatory networks during C. elegans wound repair, shedding light on mechanisms underlying tissue repair and regeneration.

ABCD4 is associated with mammary gland development in mammals.

Mammary gland development is a critical process in mammals, crucial for their reproductive success and offspring nourishment. However, the functional roles of key candidate genes associated with teat number, including ABCD4, VRTN, PROX2, and DLST, in this developmental process remain elusive. To address this gap in knowledge, we conducted an in-depth investigation into the dynamic expression patterns, functional implications, and regulatory networks of these candidate genes during mouse mammary gland development. In this study, the spatial and temporal patterns of key genes were characterized in mammary gland development. Using time-series single-cell data, we uncovered differences in the expression of A bcd4, Vrtn, Prox2, and Dlst in cell population of the mammary gland during embryonic and adult stages, while Vrtn was not detected in any cells. We found that only overexpression and knockdown of Abcd4 could inhibit proliferation and promote apoptosis of HC11 mammary epithelial cells, whereas Prox2 and Dlst had no significant effect on these cells. Using RNA-seq and qPCR, further analysis revealed that Abcd4 can induce widespread changes in the expression levels of genes involved in mammary gland development, such as Igfbp3, Ccl5, Tlr2, and Prlr, which were primarily associated with the MAPK, JAK-STAT, and PI3K-AKT pathways by functional enrichment. These findings revealed ABCD4 as a candidate gene pivotal for regulating mammary gland development and lactation during pregnancy by influencing PRLR expression.

A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks

Regulatory networks depict promoting or inhibiting interactions between molecules in a biochemical system. We introduce a category-theoretic formalism for regulatory networks, using signed graphs to model the networks and signed functors to describe occurrences of one network in another, especially occurrences of network motifs. With this foundation, we establish functorial mappings between regulatory networks and other mathematical models in biochemistry. We construct a functor from reaction networks, modeled as Petri nets with signed links, to regulatory networks, enabling us to precisely define when a reaction network could be a physical mechanism underlying a regulatory network. Turning to quantitative models, we associate a regulatory network with a Lotka-Volterra system of differential equations, defining a functor from the category of signed graphs to a category of parameterized dynamical systems. We extend this result from closed to open systems, demonstrating that Lotka-Volterra dynamics respects not only inclusions and collapsings of regulatory networks, but also the process of building up complex regulatory networks by gluing together simpler pieces. Formally, we use the theory of structured cospans to produce a lax double functor from the double category of open signed graphs to that of open parameterized dynamical systems. Throughout the paper, we ground the categorical formalism in examples inspired by systems biology.

Functions of N6-methyladenosine (m6A) RNA modifications in acute myeloid leukemia.

N6-methyladenosine (m6A) is the most common modification of eukaryotic RNA. m6A participates in RNA splicing, nuclear export, translation, and degradation through regulation by methyltransferases, methylation readers, and demethylases, affecting mRNA stability and translation efficiency. Through the dynamic and reversible regulatory network composed of " Writers-Erasers-Readers", m6A modification plays a unique role in the process of hematopoiesis. Acute myeloid leukemia (AML) is a heterogeneous disease characterized by malignant proliferation of hematopoietic stem cells/progenitor cells. Many studies have shown that m6A-related proteins are abnormally expressed in AML and play an important role in the occurrence and development of AML, acting as carcinogenic or anticancer factors. Here, we describe the mechanisms of action of reversing m6A modification in hematopoiesis and AML occurrence and progression to provide a basis for further research on the role of m6A methylation and its regulatory factors in normal hematopoiesis and AML, to ultimately estimate its potential clinical value.

Integrative metabolomic and transcriptomic analyses reveals the accumulation patterns of key metabolites associated with flavonoids and terpenoids of Gynostemma pentaphyllum (Thunb.) Makino.

Gynostemma pentaphyllum (Thunb.) Makino (G. pentaphyllum) is a medicinal and edible plant with multiple functions of liver protection, anti-tumor, anti-inflammation, balancing blood sugar and blood lipids. The nutritional value of the G. pentaphyllum plant is mainly due to its rich variety of biologically active substances, such as flavonoids, terpenes and polysaccharides. In this study, we performed a comprehensive analysis combining metabolomics and root, stem and leaf transcriptomic data of G. pentaphyllum. We used transcriptomics and metabolomics data to construct a dynamic regulatory network diagram of G. pentaphyllum flavonoids and terpenoids, and screened the transcription factors involved in flavonoids and terpenoids, including basic helix-loop-helix (bHLH), myb-related, WRKY, AP2/ERF. Transcriptome analysis results showed that among the DEGs related to the synthesis of flavonoids and terpenoids, dihydroflavonol 4-reductase (DFR) and geranylgeranyl diphosphate synthases (GGPPS) were core genes. This study presents a dynamic image of gene expression in different tissues of G. pentaphyllum, elucidating the key genes and metabolites of flavonoids and terpenoids. This study is beneficial to a deeper understanding of the medicinal plants of G. pentaphyllum, and also provides a scientific basis for further regulatory mechanisms of plant natural product synthesis pathways and drug development.

An integrated multi-omics analysis reveals osteokines involved in global regulation

Bone secretory proteins, termed osteokines, regulate bone metabolism and whole-body homeostasis. However, fundamental questions as to what the bona fide osteokines and their cellular sources are and how they are regulated remain unclear. In this study, we analyzed bone and extraskeletal tissues, osteoblast (OB) conditioned media, bone marrow supernatant (BMS), and serum, for basal osteokines and those responsive to aging and mechanical loading/unloading. We identified 375 candidate osteokines and their changes in response to aging and mechanical dynamics by integrating data from RNA-seq, scRNA-seq, and proteomic approaches. Furthermore, we analyzed their cellular sources in the bone and inter-organ communication facilitated by them (bone-brain, liver, and aorta). Notably, we discovered that senescent OBs secrete fatty-acid-binding protein 3 to propagate senescence toward vascular smooth muscle cells (VSMCs). Taken together, we identified previously unknown candidate osteokines and established a dynamic regulatory network among them, thus providing valuable resources to further investigate their systemic roles.

Dynamics of plant nutrient signaling through compost

Nutrient signaling is a fundamental aspect of plant growth and development, encompassing intricate pathways that govern nutrient uptake, allocation, and utilization. Plant growth hormones, root exudates, soil microbes intricately coordinate these processes, responding to nutrient availability and environmental cues. These hormones regulate the expression of transporters, enzymes, and ion channels involved in nutrient acquisition and distribution. Furthermore, plant nutrient signaling pathways interface with hormonal pathways, forming a dynamic regulatory network that allows plants to adapt to changing nutrient conditions and stressors. Additionally, these pathways impact secondary metabolism, root architecture, and nutrient homeostasis, influencing crop yield and quality. Complementing nutrient signaling, through compost application enriches soils, enhancing nutrient availability and soil microbial communities, ultimately optimizing nutrient mobilization and uptake. Understanding these mechanisms is vital for sustainable agriculture and improved crop management, promising increased plant growth, yield, and nutrient utilization efficiency.