Limits...
Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

Sato M, Tsuda K, Wang L, Coller J, Watanabe Y, Glazebrook J, Katagiri F - PLoS Pathog. (2010)

Bottom Line: The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate.Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, Minnesota, United States of America.

ABSTRACT
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. 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 for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the 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 static network model accurately predicted 23 of 25 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; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were 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.

Show MeSH

Related in: MedlinePlus

The workflow for network inference.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2908620&req=5

ppat-1001011-g001: The workflow for network inference.

Mentions: The mRNA profiles were collected in multiple experiment groups and combined into a single data set using mixed linear models (MATERIALS AND METHODS, Table S1). The overall experimental design regarding the experiment group was not symmetric, and the overlapping genotypes in any particular combination of experiment groups were limited. These features may have introduced some biases in the data set. To compare mutation effects, log2-transformed expression values of genes in the wild type mRNA profile were subtracted from log2-transformed expression values of genes in each mutant mRNA profile, and the obtained log-transformed mRNA profile change was scaled across the genes, but not centered, to preserve the signs of the values (which is called a difference profile hereafter). Linear dimensionality reduction was applied locally (Locally Linear Embedding, LLE; [33]), so that the same types of mRNA profile changes do not make redundant links. Although the above procedure is in principle the same as used in our previous studies [34], [35], [36], we implemented an additional concept in the current study. In the previous procedure, mutant difference profiles that are local to a particular mutant difference profile are defined based on the global distance in the difference profile space. However, mutants that have a weak regulatory relationship, such as one corresponding to weak cross-talk, may not be detected as their difference profiles may not be located closely in the global space. In the new procedure, named Repetitive Euclidean-distance Locally linear Embedded Graph Generator (RepEdLEGG), the residual from the first round of LLE was subjected to another round of LLE. This recursive application of LLE enabled detection of such weak regulatory relationships (Figure S1). The overall workflow of the network inference procedure is summarized in Figure 1.


Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

Sato M, Tsuda K, Wang L, Coller J, Watanabe Y, Glazebrook J, Katagiri F - PLoS Pathog. (2010)

The workflow for network inference.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2908620&req=5

ppat-1001011-g001: The workflow for network inference.
Mentions: The mRNA profiles were collected in multiple experiment groups and combined into a single data set using mixed linear models (MATERIALS AND METHODS, Table S1). The overall experimental design regarding the experiment group was not symmetric, and the overlapping genotypes in any particular combination of experiment groups were limited. These features may have introduced some biases in the data set. To compare mutation effects, log2-transformed expression values of genes in the wild type mRNA profile were subtracted from log2-transformed expression values of genes in each mutant mRNA profile, and the obtained log-transformed mRNA profile change was scaled across the genes, but not centered, to preserve the signs of the values (which is called a difference profile hereafter). Linear dimensionality reduction was applied locally (Locally Linear Embedding, LLE; [33]), so that the same types of mRNA profile changes do not make redundant links. Although the above procedure is in principle the same as used in our previous studies [34], [35], [36], we implemented an additional concept in the current study. In the previous procedure, mutant difference profiles that are local to a particular mutant difference profile are defined based on the global distance in the difference profile space. However, mutants that have a weak regulatory relationship, such as one corresponding to weak cross-talk, may not be detected as their difference profiles may not be located closely in the global space. In the new procedure, named Repetitive Euclidean-distance Locally linear Embedded Graph Generator (RepEdLEGG), the residual from the first round of LLE was subjected to another round of LLE. This recursive application of LLE enabled detection of such weak regulatory relationships (Figure S1). The overall workflow of the network inference procedure is summarized in Figure 1.

Bottom Line: The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate.Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, Minnesota, United States of America.

ABSTRACT
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. 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 for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the 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 static network model accurately predicted 23 of 25 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; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were 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.

Show MeSH
Related in: MedlinePlus