Dynamics Of Regulatory Networks Research Articles

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.

Snapsense reveals the spatiotemporal dynamics of GTPase regulatory networks in live cells

Snapsense reveals the spatiotemporal dynamics of GTPase regulatory networks in live cells

DNA methylation remodeled amino acids biosynthesis regulates flower senescence in carnation (Dianthus caryophyllus).

Dynamic DNA methylation regulatory networks are involved in many biological processes. However, how DNA methylation patterns change during flower senescence and their relevance with gene expression and related molecular mechanism remain largely unknown. Here, we used whole genome bisulfite sequencing to reveal a significant increase of DNA methylation in the promoter region of genes during natural and ethylene-induced flower senescence in carnation (Dianthus caryophyllus L.), which was correlated with decreased expression of DNA demethylase gene DcROS1. Silencing of DcROS1 accelerated while overexpression of DcROS1 delayed carnation flower senescence. Moreover, among the hypermethylated differentially expressed genes during flower senescence, we identified two amino acid biosynthesis genes, DcCARA and DcDHAD, with increased DNA methylation and reduced expression in DcROS1 silenced petals, and decreased DNA methylation and increased expression in DcROS1 overexpression petals, accompanied by decreased or increased amino acids content. Silencing of DcCARA and DcDHAD accelerates carnation flower senescence. We further showed that adding corresponding amino acids could largely rescue the senescence phenotype of DcROS1, DcCARA and DcDHAD silenced plants. Our study not only demonstrates an essential role of DcROS1-mediated remodeling of DNA methylation in flower senescence but also unravels a novel epigenetic regulatory mechanism underlying DNA methylation and amino acid biosynthesis during flower senescence.

3D chromatin structure and massively parallel reporter assays in hiPSC‐derived microglia and human macrophages identifies novel putative Alzheimer’s Disease risk genes

AbstractBackgroundAlzheimer’s disease (AD) genome‐wide association studies (GWAS) identified 75 disease‐associated loci; however, interpretation of these results has proven difficult for two primary reasons. First, linkage disequilibrium between nearby variants makes it difficult to identify the causal variant(s) at each locus. Second, because the vast majority of OA risk variants are non‐coding and can regulate genes from distances of greater than one million base pairs, the genes impacted by AD risk variants are largely unknown. Recently developed genomic approaches can address these hurdles but must be applied to the correct cell types and biological contexts.MethodWe quantified the impact of 3,576 AD‐associated genetic variants on enhancer activity by performing massively parallel reporter assays (MPRAs) in resting and activated human macrophages. To map these variants to the genes they regulate we built regulatory networks by mapping 3D chromatin structure (Hi‐C), chromatin accessibility (ATAC), histone K27 acetylation (CUT&RUN), and gene expression (RNA‐seq) in both resting and activated iPSC‐derived microglia.ResultIn total we identified 20,992 chromatin loops across resting and activated iPSC‐derived microglia. Among these, 9,544 loops linked 14,576 enhancers to 11,487 genes. 58 loops connected 414 AD‐risk variants queried by our MPRA assays to 85 genes. 212, 19,256, and 2,790 of these loops, enhancers, and genes (respectively) changed in response to activation, providing further insight into their regulatory mechanisms and highlighting the need to study these events in the correct biological context.ConclusionOngoing analyses and data integration should reveal further mechanistic details and provide novel AD risk genes for further research and therapeutic development. By intersecting our MPRA results and dynamic regulatory networks with AD GWAS, we were able to identify novel putative genes regulated by non‐coding AD risk variants in a cell‐type and condition‐specific manner.

Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data.

Growth is regulated by gene expression variation at different developmental stages of biological processes such as cell differentiation, disease progression, or drug response. In cancer, a stage-specific regulatory model constructed to infer the dynamic expression changes in genes contributing to tissue growth or proliferation is referred as a dynamic growth regulatory network (dGRN). Over the past decade, gene expression data has been widely used for reconstructing dGRN by computing correlations between the differentially expressed genes (DEGs). A wide variety of pipelines are available to construct the GRNs using DEGs and the choice of a particular method or tool depends on the nature of the study. In this protocol, we have outlined a step-by-step guide for the analysis of DEGs using RNA-Seq data, beginning from data acquisition, pre-processing, mapping to reference genome, and construction of a correlation-based co-expression network to further downstream analysis. We have also outlined the steps for the inclusion of publicly available interaction/regulation information into the dGRN followed by relevant topological inferences. This tutorial has been designed in a way that early researchers can refer to for an easy and comprehensive glimpse of methodologies used in the inference of dGRN using transcriptomics data.

A digital twin reproducing gene regulatory network dynamics of early Ciona embryos indicates robust buffers in the network.

How gene regulatory networks (GRNs) encode gene expression dynamics and how GRNs evolve are not well understood, although these problems have been studied extensively. We created a digital twin that accurately reproduces expression dynamics of 13 genes that initiate expression in 32-cell ascidian embryos. We first showed that gene expression patterns can be manipulated according to predictions by this digital model. Next, to simulate GRN rewiring, we changed regulatory functions that represented their regulatory mechanisms in the digital twin, and found that in 55 of 100 cases, removal of a single regulator from a conjunctive clause of Boolean functions did not theoretically alter qualitative expression patterns of these genes. In other words, we found that more than half the regulators gave theoretically redundant temporal or spatial information to target genes. We experimentally substantiated that the expression pattern of Nodal was maintained without one of these factors, Zfpm, by changing the upstream regulatory sequence of Nodal. Such robust buffers of regulatory mechanisms may provide a basis of enabling developmental system drift, or rewiring of GRNs without changing expression patterns of downstream genes, during evolution.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

ScTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-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.