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Maintenance of genetic variation in plants and pathogens involves complex networks of gene-for-gene interactions.

ABSTRACT: The RPP13 [recognition of Hyaloperonospora arabidopsidis (previously known as Peronospora parasitica)] resistance (R) gene in Arabidopsis thaliana exhibits the highest reported level of sequence diversity among known R genes. Consistent with a co-evolutionary model, the matching effector protein ATR13 (A. thaliana-recognized) from H. arabidopsidis reveals extreme levels of allelic diversity. We isolated 23 new RPP13 sequences from a UK metapopulation, giving a total of 47 when combined with previous studies. We used these in functional studies of the A. thaliana accessions for their resistance response to 16 isolates of H. arabidopsidis. We characterized the molecular basis of recognition by the expression of the corresponding ATR13 genes from these 16 isolates in these host accessions. This allowed the determination of which alleles of RPP13 were responsible for pathogen recognition and whether recognition was dependent on the RPP13/ATR13 combination. Linking our functional studies with phylogenetic analysis, we determined that: (i) the recognition of ATR13 is mediated by alleles in just a single RPP13 clade; (ii) RPP13 alleles in other clades have evolved the ability to detect other pathogen ATR protein(s); and (iii) at least one gene, unlinked to RPP13 in A. thaliana, detects a different subgroup of ATR13 alleles.

SUBMITTER: Hall SA

PROVIDER: S-EPMC6640458 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Maintenance of genetic variation in plants and pathogens involves complex networks of gene-for-gene interactions.

Hall Sharon A SA Allen Rebecca L RL Baumber Rachel E RE Baxter Laura A LA Fisher Kate K Bittner-Eddy Peter D PD Rose Laura E LE Holub Eric B EB Beynon Jim L JL

Molecular plant pathology 20090701 4

The RPP13 [recognition of Hyaloperonospora arabidopsidis (previously known as Peronospora parasitica)] resistance (R) gene in Arabidopsis thaliana exhibits the highest reported level of sequence diversity among known R genes. Consistent with a co-evolutionary model, the matching effector protein ATR13 (A. thaliana-recognized) from H. arabidopsidis reveals extreme levels of allelic diversity. We isolated 23 new RPP13 sequences from a UK metapopulation, giving a total of 47 when combined with prev ...[more]

PMID: 19523099

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Project description:BACKGROUND: In dynamical models with feedback and sigmoidal response functions, some or all variables have thresholds around which they regulate themselves or other variables. A mathematical analysis has shown that when the dose-response functions approach binary or on/off responses, any variable with an equilibrium value close to one of its thresholds is very robust to parameter perturbations of a homeostatic state. We denote this threshold robustness. To check the empirical relevance of this phenomenon with response function steepnesses ranging from a near on/off response down to Michaelis-Menten conditions, we have performed a simulation study to investigate the degree of threshold robustness in models for a three-gene system with one downstream gene, using several logical input gates, but excluding models with positive feedback to avoid multistationarity. Varying parameter values representing functional genetic variation, we have analysed the coefficient of variation (CV) of the gene product concentrations in the stable state for the regulating genes in absolute terms and compared to the CV for the unregulating downstream gene. The sigmoidal or binary dose-response functions in these models can be considered as phenomenological models of the aggregated effects on protein or mRNA expression rates of all cellular reactions involved in gene expression. RESULTS: For all the models, threshold robustness increases with increasing response steepness. The CVs of the regulating genes are significantly smaller than for the unregulating gene, in particular for steep responses. The effect becomes less prominent as steepnesses approach Michaelis-Menten conditions. If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost. Threshold robustness arises when a variable is an active regulator around its threshold, and this function is maintained by the feedback loop that the regulator necessarily takes part in and also is regulated by. In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state. CONCLUSION: Threshold robustness of a variable can be seen as its ability to maintain an active regulation around its threshold in a homeostatic state despite external perturbations. The feedback loop that the system necessarily possesses in this state, ensures that the robust variable is itself regulated and kept close to its threshold. Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback. Threshold robustness in gene regulatory networks illustrates that hidden genetic variation can be explained by systemic properties of the genotype-phenotype map.

Project description:BACKGROUND: The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified. RESULTS: We design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs. CONCLUSION: Our analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein interaction networks could plausibly be rich sources of information about epistatic effects among genes.

Dataset Information

Maintenance of genetic variation in plants and pathogens involves complex networks of gene-for-gene interactions.

Publications

Maintenance of genetic variation in plants and pathogens involves complex networks of gene-for-gene interactions.

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