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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.

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The network model for the genes corresponding to the mutations.The difference profiles of the 22 Arabidopsis mutants at 6 hpi of Pto DC3000 AvrRpt2 were analyzed by RepEdLEGG to obtain this network model. Positive (A), negative (B), and both (C) regulatory relationships are graphically represented. See the color codes of the coefficients associated with the links in (C). The color codes for the vertices at the bottom of the figure show the signaling sector assignments for the genes corresponding to the mutations. The links represent the regulatory relationships between the genes. The color codes for the links show the coefficient values obtained in the RepEdLEGG procedure (x). A larger absolute value of x represents a stronger regulatory relationship.
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ppat-1001011-g002: The network model for the genes corresponding to the mutations.The difference profiles of the 22 Arabidopsis mutants at 6 hpi of Pto DC3000 AvrRpt2 were analyzed by RepEdLEGG to obtain this network model. Positive (A), negative (B), and both (C) regulatory relationships are graphically represented. See the color codes of the coefficients associated with the links in (C). The color codes for the vertices at the bottom of the figure show the signaling sector assignments for the genes corresponding to the mutations. The links represent the regulatory relationships between the genes. The color codes for the links show the coefficient values obtained in the RepEdLEGG procedure (x). A larger absolute value of x represents a stronger regulatory relationship.

Mentions: With the above procedure, we obtained a regulatory relationship model for 22 genes corresponding to the mutations with 67 undirected links, which we refer to as our network model (Figure 2). Our network model has a form of an undirected graph since a single time-point data set does not allow inference of the direction of relationships without an additional assumption. Forty-eight and 19 links represented positive and negative regulatory relationships, respectively (Figure 2A and 2B, Figure S2, Table S2). To evaluate the accuracy of the predicted regulatory relationships, the published literature was surveyed for supporting experimental data (Table S3). Twenty-five pairwise regulatory relationships between genes used in this study, that included information about the sign of the relationships, were found in published literature. Our network model correctly predicted 23 out of the 25 known regulatory relationships. One of the relationships not correctly inferred was the JIN1-MPK6 relationship: MPK6 was described as a negative regulator of JIN1 [47] whereas our model predicts a positive relationship between them. The other was that the model did not predict a direct relationship corresponding to negative regulation of SID2 by EIN3, described in Chen et al. [48]. However, when JAR1, which was connected positively and negatively with EIN3 and SID2, respectively, was removed from the input data set, the negative regulatory relationship between EIN3 and SID2 was inferred (Table S4). Under our experimental conditions, JA signaling could be strong due to coronatine and may have masked the effect of EIN3, which mediates ET signaling. Note that the known links were established with data from diverse experiments conducted using various Arabidopsis-pathogen interactions, performed by many different research groups. While such studies helped us to select useful mutants for our study, our network model was built based solely on mRNA profile data collected using a single experimental setup with a single time point. This fact demonstrates the richness of information in descriptions of the network state consisting of mRNA profiles and the high efficiency of network inference using mRNA profiles as detailed descriptions of network states. The high accuracy in prediction of previously known regulatory relationships suggests the accuracy of newly predicted regulatory relationships.


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 network model for the genes corresponding to the mutations.The difference profiles of the 22 Arabidopsis mutants at 6 hpi of Pto DC3000 AvrRpt2 were analyzed by RepEdLEGG to obtain this network model. Positive (A), negative (B), and both (C) regulatory relationships are graphically represented. See the color codes of the coefficients associated with the links in (C). The color codes for the vertices at the bottom of the figure show the signaling sector assignments for the genes corresponding to the mutations. The links represent the regulatory relationships between the genes. The color codes for the links show the coefficient values obtained in the RepEdLEGG procedure (x). A larger absolute value of x represents a stronger regulatory relationship.
© Copyright Policy
Related In: Results  -  Collection

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

ppat-1001011-g002: The network model for the genes corresponding to the mutations.The difference profiles of the 22 Arabidopsis mutants at 6 hpi of Pto DC3000 AvrRpt2 were analyzed by RepEdLEGG to obtain this network model. Positive (A), negative (B), and both (C) regulatory relationships are graphically represented. See the color codes of the coefficients associated with the links in (C). The color codes for the vertices at the bottom of the figure show the signaling sector assignments for the genes corresponding to the mutations. The links represent the regulatory relationships between the genes. The color codes for the links show the coefficient values obtained in the RepEdLEGG procedure (x). A larger absolute value of x represents a stronger regulatory relationship.
Mentions: With the above procedure, we obtained a regulatory relationship model for 22 genes corresponding to the mutations with 67 undirected links, which we refer to as our network model (Figure 2). Our network model has a form of an undirected graph since a single time-point data set does not allow inference of the direction of relationships without an additional assumption. Forty-eight and 19 links represented positive and negative regulatory relationships, respectively (Figure 2A and 2B, Figure S2, Table S2). To evaluate the accuracy of the predicted regulatory relationships, the published literature was surveyed for supporting experimental data (Table S3). Twenty-five pairwise regulatory relationships between genes used in this study, that included information about the sign of the relationships, were found in published literature. Our network model correctly predicted 23 out of the 25 known regulatory relationships. One of the relationships not correctly inferred was the JIN1-MPK6 relationship: MPK6 was described as a negative regulator of JIN1 [47] whereas our model predicts a positive relationship between them. The other was that the model did not predict a direct relationship corresponding to negative regulation of SID2 by EIN3, described in Chen et al. [48]. However, when JAR1, which was connected positively and negatively with EIN3 and SID2, respectively, was removed from the input data set, the negative regulatory relationship between EIN3 and SID2 was inferred (Table S4). Under our experimental conditions, JA signaling could be strong due to coronatine and may have masked the effect of EIN3, which mediates ET signaling. Note that the known links were established with data from diverse experiments conducted using various Arabidopsis-pathogen interactions, performed by many different research groups. While such studies helped us to select useful mutants for our study, our network model was built based solely on mRNA profile data collected using a single experimental setup with a single time point. This fact demonstrates the richness of information in descriptions of the network state consisting of mRNA profiles and the high efficiency of network inference using mRNA profiles as detailed descriptions of network states. The high accuracy in prediction of previously known regulatory relationships suggests the accuracy of newly predicted regulatory relationships.

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