Study Of Gene Regulatory Networks Research Articles

Comparative analysis of gene regulatory networks identifies conserved regulators in seed plants

Abstract Gene regulatory networks (GRNs) based on transcription factors (TFs) control development and environmental responses. In this study, GRNs were inferred computationally using random forest decision tree-based regression. Networks were constructed for the grasses barley, maize, wheat, brachypodium, sorghum and rice. When compared with Arabidopsis thaliana and alga networks, they show substantial conservation. The degree of conservation depends on phylogenetic closeness. The processes are conserved between all species include basic cellular functions while the processes conserved only in the grasses are annotated with more specific gene ontology terms. In the three species with a carbon concentration mechanism, photorespiration is partially dissociated from photosynthetic regulation. In contrast, in the C4 species, the regulation of C4 genes is associated with photosynthetic regulation. The comparative analyses reveal conserved TFs that control photosynthesis in seed plants but not in the alga. The analyses resulted in a pipeline for the general transfer of information between the small dicot A. thaliana and the commercially relevant grasses.

Positivity and Stability of Caputo Fractional Order Gene Regulatory Networks: The System Comparison Method

As well known, the positivity is an essential topic when studying gene regulatory networks since the variables involved, e.g., the concentrations of mRNA and proteins, can never be negative. However, the positivity of Caputo fractional order models has been a longstanding problem due to the nonlocality of Caputo fractional derivatives (CFD). In this paper, we present the system comparison method to prove the positivity of Caputo fractional order gene regulatory networks (CFOGRNs) only under positive initial conditions. Moreover, it is found that the positivity results can make it feasible to give proper comparison systems for CFOGRNs, in which the upper and lower estimations can be used to guarantee the stability of the objective CFOGRNs. Thus, the system comparison method for the stability of CFOGRNs is also provided here. Compared to the existing Lyapunov direct method, the proposed system comparison method affords an alternative method for stability analysis and different insights in stability conditions. Finally, these theoretical derivations are illustrated and validated by an example with numerical simulations.

Unravelling microRNA regulation and miRNA-mRNA regulatory networks in osteogenesis driven by 3D nanotopographical cues.

Three-dimensional (3D) culturing of cells is being adopted for developing tissues for various applications such as mechanistic studies, drug testing, tissue regeneration, and animal-free meat. These approaches often involve cost-effective differentiation of stem or progenitor cells. One approach is to exploit architectural cues on a 3D substrate to drive cellular differentiation, which has been shown to be effective in various studies. Although extensive gene expression data from such studies have shown that gene expression patterns might differ, the gene regulatory networks controlling the expression of genes are rarely studied. In this study, we profiled genes and microRNAs (miRNAs) via next-generation sequencing (NGS) in human mesenchymal stem cells (hMSCs) driven toward osteogenesis via architectural cues in 3D matrices (3D conditions) and compared with cells in two-dimensional (2D) culture driven toward osteogenesis via soluble osteoinductive factors (OF conditions). The total number of differentially expressed genes was smaller in 3D compared to OF conditions. A distinct set of genes was observed under these conditions that have been shown to control osteogenic differentiation via different pathways. Small RNA sequencing revealed a core set of miRNAs to be differentially expressed under these conditions, similar to those that have been previously implicated in osteogenesis. We also observed a distinct regulation of miRNAs in these samples that can modulate gene expression, suggesting supplementary gene regulatory networks operative under different stimuli. This study provides insights into studying gene regulatory networks for identifying critical nodes to target for enhanced cellular differentiation and reveal the differences in physical and biochemical cues to drive cell fates.

Quasi-periodic Solutions for a Three-dimensional System in Gene Regulatory Network

This work introduces a three-dimensional system with quasi-periodic solutions for special values of parameters. The equations model the interactions between genes and their products. In gene regulatory networks, quasi-periodic solutions refer to a specific type of temporal behavior observed in the system. We show the dynamics of Lyapunov exponents. Visualizations are provided. It is important to note that the study of gene regulatory networks is a complex interdisciplinary field that combines biology, mathematics, and computer science.

Bifurcation control of a novel fractional-order gene regulatory network with incommensurate order and time delay

Gene regulatory networks play an extremely important role in human life activities. This paper presents a novel fractional-order gene regulatory network model (NFGRNM). The model employs incommensurate fractional orders and introduces time delay arising from the binding of miRNAs to RNA. Time delay often triggers instability such as oscillation and bifurcation, so in this paper we introduce a generalized hybrid control to control the unstable dynamical behavior induced by time delay. First we derive the NFGRNM using fractional order theory, after which the equilibrium point of the network model is calculated. Next, a systematic dynamics analysis of the NFGRNM is performed to derive sufficient conditions to generate the Hopf bifurcation. Most importantly, we introduce the extended hybrid control for the periodic oscillations generated by the time delay in the NFGRNM. Finally, numerical simulation results show that the order of the fractional order can have a great impact on the dynamical behavior of the network model and that the hybrid control has a significant role in delaying (or advancing) the Hopf bifurcation. Since infectious diseases such as SARS-Cov-2, influenza A, and HIV are still prevalent today, the study of gene regulatory networks not only promotes the study of the dynamics of complex networks but also has profound implications for the study of virus transmission in the host and various life activities in humans.

Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA Interactions Over Time in Cells and Tissues.

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon "Calling Cards" into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Preparation and delivery of Calling Cards reagents Support Protocol 1: Next-generation sequencing quantification of barcode distribution within self-reporting transposon plasmid pool and adeno-associated virus genome Basic Protocol 2: Sample collection and RNA purification Support Protocol 2: Library density quantitative PCR Basic Protocol 3: Sequencing library preparation Basic Protocol 4: Library pooling and sequencing Basic Protocol 5: Data analysis.

Gene regulatory network inference in the era of single-cell multi-omics.

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

Current technical advancements in plant epitranscriptomic studies.

The growth and development of plants are the result of the interplay between the internal developmental programming and plant-environment interactions. Gene expression regulations in plants are made up of multi-level networks. In the past few years, many studies were carried out on co- and post-transcriptional RNA modifications, which, together with the RNA community, are collectively known as the "epitranscriptome." The epitranscriptomic machineries were identified and their functional impacts characterized in a broad range of physiological processes in diverse plant species. There is mounting evidence to suggest that the epitranscriptome provides an additional layer in the gene regulatory network for plant development and stress responses. In the present review, we summarized the epitranscriptomic modifications found so far in plants, including chemical modifications, RNA editing, and transcript isoforms. The various approaches to RNA modification detection were described, with special emphasis on the recent development and application potential of third-generation sequencing. The roles of epitranscriptomic changes in gene regulation during plant-environment interactions were discussed in case studies. This review aims to highlight the importance of epitranscriptomics in the study of gene regulatory networks in plants and to encourage multi-omics investigations using the recent technical advancements.

Mechanisms of robustness in gene regulatory networks involved in neural development.

The functions of living organisms are affected by different kinds of perturbation, both internal and external, which in many cases have functional effects and phenotypic impact. The effects of these perturbations become particularly relevant for multicellular organisms with complex body patterns and cell type heterogeneity, where transcriptional programs controlled by gene regulatory networks determine, for example, the cell fate during embryonic development. Therefore, an essential aspect of development in these organisms is the ability to maintain the functionality of their genetic developmental programs even in the presence of genetic variation, changing environmental conditions and biochemical noise, a property commonly termed robustness. We discuss the implication of different molecular mechanisms of robustness involved in neurodevelopment, which is characterized by the interplay of many developmental programs at a molecular, cellular and systemic level. We specifically focus on processes affecting the function of gene regulatory networks, encompassing transcriptional regulatory elements and post-transcriptional processes such as miRNA-based regulation, but also higher order regulatory organization, such as gene network topology. We also present cases where impairment of robustness mechanisms can be associated with neurodevelopmental disorders, as well as reasons why understanding these mechanisms should represent an important part of the study of gene regulatory networks driving neural development.

Mining novel cis-regulatory elements from the emergent host Rhodosporidium toruloides using transcriptomic data.

The demand for robust microbial cell factories that produce valuable biomaterials while resisting stresses imposed by current bioprocesses is rapidly growing. Rhodosporidium toruloides is an emerging host that presents desirable features for bioproduction, since it can grow in a wide range of substrates and tolerate a variety of toxic compounds. To explore R. toruloides suitability for application as a cell factory in biorefineries, we sought to understand the transcriptional responses of this yeast when growing under experimental settings that simulated those used in biofuels-related industries. Thus, we performed RNA sequencing of the oleaginous, carotenogenic yeast in different contexts. The first ones were stress-related: two conditions of high temperature (37 and 42°C) and two ethanol concentrations (2 and 4%), while the other used the inexpensive and abundant sugarcane juice as substrate. Differential expression and functional analysis were implemented using transcriptomic data to select differentially expressed genes and enriched pathways from each set-up. A reproducible bioinformatics workflow was developed for mining new regulatory elements. We then predicted, for the first time in this yeast, binding motifs for several transcription factors, including HAC1, ARG80, RPN4, ADR1, and DAL81. Most putative transcription factors uncovered here were involved in stress responses and found in the yeast genome. Our method for motif discovery provides a new realm of possibilities in studying gene regulatory networks, not only for the emerging host R. toruloides, but for other organisms of biotechnological importance.

Inducible, Tissue-Specific Gene Expression in Arabidopsis Using GR-LhG4-Mediated Trans-Activation.

Inducible, tissue-specific gene expression is a potent tool to study gene regulatory networks as it allows spatially and temporally controlled genetic perturbations. To this end, we generated a toolkit that covers many cell types in the three main meristems: the root apical meristem, the shoot apical meristem, and the vascular cambium. The system is based on an extensive set of driver lines expressing a synthetic transcription factor under cell type-specific promoters. Induction leads to nuclear translocation of the transcription factor and expression of response elements under control of a cognate synthetic promoter. In addition, a fluorescent reporter incorporated in driver lines allows to monitor induction. All previously generated driver lines are available from the Nottingham Arabidopsis Stock Center. This protocol describes how users can create their own constructs compatible with the existing set of lines and as well as induction and imaging procedures.

Development of a transgenic tissue visualization system in representatives of <i>Fabaceae</i>

Plant organisms are the objects of green biotechnology, which have been used by humans in various areas of life. The family of legumes (Fabaceae) that is being studied in this work is not an exception here. It is a very diverse family widespread throughout the globe.
 Barrel medic (Medicago truncatula) and common pea (Pisum sativum), members of the legume family, were selected in this study for the development of an vital imaging system for transgenic tissues. As a part of the work, we tested an imaging system with post-mortem staining based on the GUS reporter, as well as a system for vital detection of transgenic tissue with fluorescent proteins GFP and DsRed. At this stage, vital imaging systems based on betalain and anthocyanin staining of transgenic tissues are designed and currently being adapted to the model objects under study.
 The results of this study may be useful for subsequent attempts to solve the problem of the low efficiency of transformation of P. sativum. Moreover, such a system should be useful in the study of gene regulatory networks and factors that regulate the process of induction and subsequent development of somatic embryos. In addition, it can be used to track the dynamics of expression of genes of interest on living objects, while studying other fundamental and biotechnological processes.
 The study is supported by the Russian Foundation for Basic Research (20-016-00124).

An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation.

The development of synthetic biology has enabled massive progress in biotechnology and in approaching research questions from a brand-new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo, and in silico have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level. Mathematical models are increasingly used to predict the behaviours of molecular circuits. Fernando’s model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture. In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values. We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando’s model. Our work can be readily used as reference for synthetic biologists who consider implementing circuits of this kind in biological systems.

In Vitro Transcription Factor Binding Site Predictions Using Support Vector Machine Classification

Transcription factors (TFs) are sequence‐specific DNA‐binding proteins essential in regulating gene expression. Determining TF DNA‐binding specificity can help to study gene regulatory networks within cells and how genetic variation can disrupt normal gene expression. One method for characterizing TF specificity is through Support Vector Machines (SVMs) by analyzing chromatin immunoprecipitation followed by DNA‐sequencing (ChIP‐seq) data. However, this can also be achieved using Systematic Evolution of Ligands by Exponential Enrichment (SELEX) data, a method that also aids in determining TF‐DNA preferences. During this project, I implemented a gapped kmer SVM to study TF‐DNA binding preferences by using data from SELEX‐seq. I used a large scale‐gapped kmer, a sequence‐based SVM for analyzing TF specificity. It works by creating a predictive model that is trained with bound and unbound sequences from SELEX data. For purposes of this project, we used the T‐box transcription factor 5 (TBX5). After training the model for TBX5 and testing its performance, it had an AUROC value of 0.8248, indicating a significant degree of reliability. Likewise, the sequences with highest scores contained motifs for the TBX5. Given these results, we concluded that SVM was successfully implemented. In addition, SELEX data had not been previously used to train SVM based predictive models, meaning SELEX data is compatible and useful for developing predictive models.

Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

BackgroundNetwork inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications.MethodsWe performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation.ResultsThe structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context.ConclusionsThe combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.

A semi-parametric statistical test to compare complex networks

AbstractThe modelling of real-world data as complex networks is ubiquitous in several scientific fields, for example, in molecular biology, we study gene regulatory networks and protein–protein interaction (PPI)_networks; in neuroscience, we study functional brain networks; and in social science, we analyse social networks. In contrast to theoretical graphs, real-world networks are better modelled as realizations of a random process. Therefore, analyses using methods based on deterministic graphs may be inappropriate. For example, verifying the isomorphism between two graphs is of limited use to decide whether two (or more) real-world networks are generated from the same random process. To overcome this problem, in this article, we introduce a semi-parametric approach similar to the analysis of variance to test the equality of generative models of two or more complex networks. We measure the performance of the proposed statistic using Monte Carlo simulations and illustrate its usefulness by comparing PPI networks of six enteric pathogens.

RNAi Transfection Optimized in Primary Naïve B Cells for the Targeted Analysis of Human Plasma Cell Differentiation.

Upon antigen recognition, naïve B cells undergo rapid proliferation followed by differentiation to specialized antibody secreting cells (ASCs), called plasma cells. Increased circulating plasma cells are reported in patients with B cell-associated malignancies, chronic graft-vs.-host disease, and autoimmune disorders. Our aim was to optimize an RNAi-based method that efficiently and reproducibly knocks-down genes of interest in human primary peripheral B cells for the targeted analysis of ASC differentiation. The unique contributions of transcriptional diversity in species-specific regulatory networks and the mechanisms of gene function need to be approached directly in human B cells with tools to hone our basic inferences from animal models to human biology. To date, methods for gene knockdown in human primary B cells, which tend to be more refractory to transfection than immortalized B cell lines, have been limited by losses in cell viability and ineffective penetrance. Our single-step siRNA nucleofector-based approach for human primary naïve B cells demonstrates reproducible knockdown efficiency (~40–60%). We focused on genes already known to play key roles in murine ASC differentiation, such as interferon regulatory factor 4 (IRF4) and AID. This study reports a validated non-viral method of siRNA delivery into human primary B cells that can be applied to study gene regulatory networks that control human ASC differentiation.

DNA Motif Finding Method Without Protection Can Leak User Privacy

DNA sequence analysis plays an important role in the study of gene regulatory networks. DNA motif finding has become a key discipline in the post-gene era and gradually become a research hotspot by mining key gene sequences corresponding to disease mechanism and important biological functions. However, the research of DNA motif finding is faced with a huge problem of privacy disclosure. DNA motif finding technology cannot manage and use data well under controllable conditions, and the mining process of DNA motif finding itself is prone to reveal private information such as individual traits, characteristics and disease defects. In this paper, we presented an overview of the privacy breaching of DNA motif finding, summarized the main methods and tools of the current DNA motif finding, analyzed its privacy risks, and used two case studies to verify that the DNA motif finding may identify individual privacy information. Finally, we discussed the privacy protection methods for motif finding and proposed the privacy protection solutions.

Procuring animals and culturing of eggs and embryos.

Echinoderms and especially echinoids have a rich history as model systems for the study of oogenesis, fertilization, and early embryogenesis. The ease of collecting and maintaining adults, as well as in obtaining gametes and culturing large quantities of synchronous embryos, is complemented by the ability to do biochemistry, reverse genetics, embryo manipulations and study gene regulatory networks. The diversity of species and developmental modes as well as unparalleled transparency in early developmental stages also makes echinoderms an excellent system in which to study evolutionary aspects of developmental biology. This chapter provides a practical guide to experimental methods for procuring adults and gametes, achieving synchronous in vitro fertilization, and culturing embryos through early larval stages for several echinoderm species representing four classes (Echinoidea, Asteroidea, Ophiuroidea, and Holothuroidea). We provide specific examples of protocols for obtaining adults and gametes and for culturing embryos of a selected number of species for experimental analysis of their development. The species were chosen to provide breadth across the phylum Echinodermata, as well as to provide practical guidelines for handling some of the more commonly studied species. For each species, we highlight specific advantages, and special note is made of key issues to consider when handling adults, collecting gametes, or setting and maintaining embryo cultures. Finally, information regarding interspecific crosses is provided.

Target Gene Prediction of Transcription Factor Using a New Neighborhood-regularized Tri-factorization One-class Collaborative Filtering Algorithm.

Identifying the target genes of transcription factors (TFs) is one of the key factors to understand transcriptional regulation. However, our understanding of genome-wide TF targeting profile is limited due to the cost of large scale experiments and intrinsic complexity. Thus, computational prediction methods are useful to predict the unobserved associations. Here, we developed a new one-class collaborative filtering algorithm tREMAP that is based on regularized, weighted nonnegative matrix tri-factorization. The algorithm predicts unobserved target genes for TFs using known gene-TF associations and protein-protein interaction network. Our benchmark study shows that tREMAP significantly outperforms its counterpart REMAP, a bi-factorization-based algorithm, for transcription factor target gene prediction in all four performance metrics AUC, MAP, MPR, and HLU. When evaluated by independent data sets, the prediction accuracy is 37.8% on the top 495 predicted associations, an enrichment factor of 4.19 compared with the random guess. Furthermore, many of the predicted novel associations by tREMAP are supported by evidence from literature. Although we only use canonical TF-target gene interaction data in this study, tREMAP can be directly applied to tissue-specific data sets. tREMAP provides a framework to integrate multiple omics data for the further improvement of TF target gene prediction. Thus, tREMAP is a potentially useful tool in studying gene regulatory networks. The benchmark data set and the source code of tREMAP are freely available at https://github.com/hansaimlim/REMAP/tree/master/TriFacREMAP.