Baunoffersen1874

Plants use different regulatory modules in response to changes in their surroundings. With the transcriptomic approaches governing all research areas, an integrative, fast, and sensitive approach toward validating genes of interest becomes a critical step prior to functional studies in planta. This chapter describes a detailed method for a quantitative analysis of transcriptional readouts of defense response genes using tobacco leaves as a transient system. The method uses Luciferase reporter assays to monitor activities of defense pathway promoters. Under normal conditions, the JASMONATE ZIM-DOMAIN (JAZ) proteins repress defense genes by preventing their expression. Here, we will provide a detailed protocol on the use of a dual-luciferase system to analyze activities of various defense response promoters simultaneously. We will use two well-characterized modules from the Jasmonic acid (JA) defense pathway; the JAZ3 repressor protein and the promoters of three of JA responsive genes, MYC2, 3 and 4. This assay revealed not only differences in promoter strength but also provided quantitative insights on the JAZ3 repression of MYCs in a quantitative manner.The system-wide complexity of genome regulation encoding the organism phenotypic diversity is well understood. However, a major challenge persists about the appropriate method to describe the systematic dynamic genome regulation event utilizing enormous multi-omics datasets. Here, we describe Interactive Dynamic Regulatory Events Miner (iDREM) which reconstructs gene-regulatory networks from temporal transcriptome, proteome, and epigenome datasets during stress to envisage "master" regulators by simulating cascades of temporal transcription-regulatory and interactome events. The iDREM is a Java-based software that integrates static and time-series transcriptomics and proteomics datasets, transcription factor (TF)-target interactions, microRNA (miRNA)-target interaction, and protein-protein interactions to reconstruct temporal regulatory network and identify significant regulators in an unsupervised manner. The hidden Markov model detects specialized manipulated pathways as well as genes to recognize statistically significant regulators (TFs/miRNAs) that diverge in temporal activity. Selleck DiR chemical This method can be translated to any biotic or abiotic stress in plants and animals to predict the master regulators from condition-specific multi-omics datasets including host-pathogen interactions for comprehensive understanding of manipulated biological pathways.Plant immunity is a highly dynamic process and requires dynamic modeling to capture the events of complexity mediated by the interaction between plant host and the attacking pathogen. The events of recognition are invoked by pathogen-based epitopes, while the subversion of host defenses are orchestrated by pathogen-originated effector molecules. The pathogen constitutes an immune signaling network inside the host cells. We model plant immune dynamics by using JIMENA-package, which is a java-based genetic regulatory network (GRN) simulation framework. It can efficiently compute network behavior and system states mediated by pathogenic perturbations. Here, we describe a step-by-step protocol to introduce the application of JIMENA-package to quantify immune dynamics in plant-pathogen interaction networks.With the advent of recent next-generation sequencing (NGS) technologies in genomics, transcriptomics, and epigenomics, profiling single-cell sequencing became possible. The single-cell RNA sequencing (scRNA-seq) is widely used to characterize diverse cell populations and ascertain cell type-specific regulatory mechanisms. The gene regulatory network (GRN) mainly consists of genes and their regulators-transcription factors (TF). Here, we describe the lightning-fast Python implementation of the SCENIC (Single-Cell reEgulatory Network Inference and Clustering) pipeline called pySCENIC. Using single-cell RNA-seq data, it maps TFs onto gene regulatory networks and integrates various cell types to infer cell-specific GRNs. There are two fast and efficient GRN inference algorithms, GRNBoost2 and GENIE3, optionally available with pySCENIC. The pipeline has three steps (1) identification of potential TF targets based on co-expression; (2) TF-motif enrichment analysis to identify the direct targets (regulons); and (3) scoring the activity of regulons (or other gene sets) on single cell types.Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions. DAP-seq and ATAC-seq have also been used to generate genome-scale maps of protein-DNA interactions and open chromatin regions in plants. In this protocol, we describe a multistep computational pipeline for the integration of single-cell RNAseq data with DAP-seq and ATAC-seq data to predict regulatory networks and key regulatory genes. Our approach utilizes machine learning methods including feature selection and stability selection to identify candidate regulatory genes. The network generated by this pipeline can be used to provide a putative annotation of gene regulatory modules and to identify candidate transcription factors that could play a key role in specific cell types.In this book chapter, we introduce a pipeline to mine significant biomedical entities (or bioentities) in biological networks. Our focus is on prioritizing both bioentities themselves and the associations between bioentities in order to reveal their biological functions. We will introduce three tools BEERE, WIPER, and PAGER 2.0 that can be used together for network analysis and function interpretation (1) BEERE is a network analysis tool for "Biomedical Entity Expansion, Ranking and Explorations," (2) WIPER is an entity-to-entity association ranking tool, and (3) PAGER 2.0 is a service for gene enrichment analysis.With the popularity of high-throughput transcriptomic techniques like RNAseq, models of gene regulatory networks have been important tools for understanding how genes are regulated. These transcriptomic datasets are usually assumed to reflect their associated proteins. This assumption, however, ignores post-transcriptional, translational, and post-translational regulatory mechanisms that regulate protein abundance but not transcript abundance. Here we describe a method to model cross-regulatory influences between the transcripts and proteins of a set of genes using abundance data collected from a series of transgenic experiments. The developed model can capture the effects of regulation that impacts transcription as well as regulatory mechanisms occurring after transcription. This approach uses a sparse maximum likelihood algorithm to determine relationships that influence transcript and protein abundance. An example of how to explore the network topology of this type of model is also presented. This model can be used to predict how the transcript and protein abundances will change in novel transgenic modification strategies.The cell expresses various genes in specific contexts with respect to internal and external perturbations to invoke appropriate responses. Transcription factors (TFs) orchestrate and define the expression level of genes by binding to their regulatory regions. Dysregulated expression of TFs often leads to aberrant expression changes of their target genes and is responsible for several diseases including cancers. In the last two decades, several studies experimentally identified target genes of several TFs. However, these studies are limited to a small fraction of the total TFs encoded by an organism, and only for those amenable to experimental settings. Experimental limitations lead to many computational techniques having been proposed to predict target genes of TFs. Linear modeling of gene expression is one of the most promising computational approaches, readily applicable to the thousands of expression datasets available in the public domain across diverse phenotypes. Linear models assume that the expression of a gene is the sum of expression of TFs regulating it. In this chapter, I introduce mathematical programming for the linear modeling of gene expression, which has certain advantages over the conventional statistical modeling approaches. It is fast, scalable to genome level and most importantly, allows mixed integer programming to tune the model outcome with prior knowledge on gene regulation.Diverse cellular phenotypes are determined by groups of transcription factors (TFs) and other regulators that influence each others' gene expression, forming transcriptional gene regulatory networks (GRNs). In many biological contexts, especially in development and associated diseases, the expression of the genes in GRNs is not static but evolves in time. Modeling the dynamics of GRN state is an important approach for understanding diverse cellular phenomena such as cell-fate specification, pluripotency and cell-fate reprogramming, oncogenesis, and tissue regeneration. In this protocol, we describe how to model GRNs using a data-driven dynamic modeling methodology, gene circuits. Gene circuits do not require knowledge of the GRN topology and connectivity but instead learn them from training data, making them very general and applicable to diverse biological contexts. We utilize the MATLAB-based gene circuit modeling software Fast Inference of Gene Regulation (FIGR) for training the model on quantitative gene expression data and simulating the GRN. We describe all the steps in the modeling life cycle, from formulating the model, training the model using FIGR, simulating the GRN, to analyzing and interpreting the model output. This protocol highlights these steps with the example of a dynamical model of the gap gene GRN involved in Drosophila segmentation and includes example MATLAB statements for each step.Gene expression data analysis and the prediction of causal relationships within gene regulatory networks (GRNs) have guided the identification of key regulatory factors and unraveled the dynamic properties of biological systems. However, drawing accurate and unbiased conclusions requires a comprehensive understanding of relevant tools, computational methods, and their workflows. The topics covered in this chapter encompass the entire workflow for GRN inference including (1) experimental design; (2) RNA sequencing data processing; (3) differentially expressed gene (DEG) selection; (4) clustering prior to inference; (5) network inference techniques; and (6) network visualization and analysis. Moreover, this chapter aims to present a workflow feasible and accessible for plant biologists without a bioinformatics or computer science background. To address this need, TuxNet, a user-friendly graphical user interface that integrates RNA sequencing data analysis with GRN inference, is chosen for the purpose of providing a detailed tutorial.Chromatin accessibility is directly linked with transcription in eukaryotes. Accessible regions associated with regulatory proteins are highly sensitive to DNase I digestion and are termed DNase I hypersensitive sites (DHSs). DHSs can be identified by DNase I digestion, followed by high-throughput DNA sequencing (DNase-seq). The single-base-pair resolution digestion patterns from DNase-seq allows identifying transcription factor (TF) footprints of local DNA protection that predict TF-DNA binding. The identification of differential footprinting between two conditions allows mapping relevant TF regulatory interactions. Here, we provide step-by-step instructions to build gene regulatory networks from DNase-seq data. Our pipeline includes steps for DHSs calling, identification of differential TF footprints between treatment and control conditions, and construction of gene regulatory networks. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be adapted to work with DNase-seq data from any organism with a sequenced genome.