Project description:Expression profiles of 22 reference Arabidopsis immunity mutants were collected using the Arabidopsis Pathoarray 464_001 (GPL3638) in order to build a network model predicting the Arabidopsis immune signaling network. Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. Here, we demonstrate that use of mRNA profiling to collect and analyze detailed descriptions of changes in the network state resulting from specific network perturbations is a powerful and economical strategy to elucidate regulatory relationships among the components of a complex signaling network. Specifically, we studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with the Pto DC3000 AvrRpt2 and used as detailed descriptions of the network states resulting from specific genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting network model accurately predicted 22 of 23 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; (ii) negative regulatory relationships are common between signaling sectors. One case of a novel negative regulatory relationship, between the early microbe-associated molecular pattern (MAMP)-activated sector and the salicylic acid (SA)-mediated sector, was further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. Keywords: Responses of reference Arabidopsis immunity mutants to Pseudomonas syringae pv. tomato DC3000 carrying avrRpt2 This experiment consists of two (group00) or three (group01-04) biological replicates of each genotype (total 25 genotypes [3 are multiple mutants, which were removed for the network modeling, but were used for normalization]). For each genotype, two leaves per plant were pooled from three pots to prepare total RNA.
Project description:Although global analyses of transcription factor binding provide one view of potential transcriptional regulatory networks, regulation also occurs at levels distinct from transcription factor binding. Here, we use a genetic approach to identify targets of transcription factors in yeast and reconstruct a functional regulatory network. First, we profiled transcriptional responses in S. cerevisiae strains with individual deletions of 263 transcription factors. Then we used directed-weighted graph modeling and regulatory epistasis analysis to identify indirect regulatory relationships between these transcription factors, and from this we reconstructed a functional transcriptional regulatory network. The enrichment of promoter motifs and Gene Ontology annotations provide insight into the biological functions of the transcription factors. Data values are X scores and corresponding p-values derived using an error model. We profiled transcriptional responses upon the individual deletion of more than 260 TFs. We used a directed-weighted graph modelling approach and regulatory epistasis analysis to identify indirect regulatory relationships, and thus constructed a refined, functional transcriptional regulatory network.
Project description:Determining the gene regulatory network of an organism is fundamental to achieving a global understanding of cell behavior. In general, studies of transcription regulation are limited to the annotated transcription factors, not considering other non-canonical regulators. Here we describe the first systematic analysis of the DNA-interactome of a bacterium with a minimal proteome (Mycoplasma pneumoniae). We first determined by DNA affinity chromatography and intact chromatin isolation all potential DNA binding proteins. We then mapped the DNA binding of these factors by ChIP-seq, as well as their functionality by gain- and loss-of-function experiments, by transcriptomics and proteomics. This identified new DNA binding proteins and novel regulators with moonlighting properties like proteases and metabolic enzymes and allowed to reconstruct the gene regulatory network.
Project description:Th17 cells have critical roles in mucosal defense and are major contributors to inflammatory disease. Their differentiation requires the nuclear hormone receptor RORγt working with multiple other essential transcription factors (TFs). We have used an iterative systems approach, combining genome-wide TF occupancy, expression profiling of TF mutants, and expression time series to delineate the Th17 global transcriptional regulatory network. We find that cooperatively-bound BATF and IRF4 contribute to initial chromatin accessibility, and with STAT3 initiate a transcriptional program that is then globally tuned by the lineage-specifying TF RORγt, which plays a focal deterministic role at key loci. Integration of multiple datasets allowed inference of an accurate predictive model that we computationally and experimentally validated, identifying multiple new Th17 regulators, including Fosl2, a key determinant of cellular plasticity. This interconnected network can be used to investigate new therapeutic approaches to manipulate Th17 functions in the setting of inflammatory disease. 143 RNA-seq, 83 ChIP-seq, 65 ChIP-seq controls, and 16 FAIRE-seq
Project description:Determining the gene regulatory network of an organism is fundamental to achieving a global understanding of cell behavior. In general, studies of transcription regulation are limited to the annotated transcription factors, not considering other non-canonical regulators. Here we describe the first systematic analysis of the DNA-interactome of a bacterium with a minimal proteome (Mycoplasma pneumoniae). We first determined by DNA affinity chromatography and intact chromatin isolation all potential DNA binding proteins. We then mapped the DNA binding of these factors by ChIP-seq, as well as their functionality by gain- and loss-of-function experiments, by transcriptomics and proteomics. This identified new DNA binding proteins and novel regulators with moonlighting properties like proteases and metabolic enzymes and allowed to reconstruct the gene regulatory network.
Project description:Determining the gene regulatory network of an organism is fundamental to achieving a global understanding of cell behavior. In general, studies of transcription regulation are limited to the annotated transcription factors, not considering other non-canonical regulators. Here we describe the first systematic analysis of the DNA-interactome of a bacterium with a minimal proteome (Mycoplasma pneumoniae). We first determined by DNA affinity chromatography and intact chromatin isolation all potential DNA binding proteins. We then mapped the DNA binding of these factors by ChIP-seq, as well as their functionality by gain- and loss-of-function experiments, by transcriptomics and proteomics. This identified new DNA binding proteins and novel regulators with moonlighting properties like proteases and metabolic enzymes and allowed to reconstruct the gene regulatory network.