Javascript must be enabled to continue!
Prediction and Characterization of RXLR Effectors inPythiumSpecies
View through CrossRef
AbstractBeing widely existed in oomycetes, the RXLR effector features conserved RXLR-dEER motifs in its N terminal. Every knownPhytophthoraorHyaloperonosporapathogen harbors hundreds of RXLRs. InPythiumspecies, however, none of the RXLR effectors has been characterized yet. Here, we developed a stringent method forde novoidentification of RXLRs and characterized 359 putative RXLR effectors from nine testedPythiumspecies. Phylogenetic analysis revealed a single superfamily formed by all oomycetous RXLRs, suggesting they descent from a common ancestor. RXLR effectors fromPythiumandPhytophthoraspecies exhibited similar sequence features, protein structures and genome locations. In particular, the mosquito biological agentP. guiyangensecontains a significantly larger RXLR repertoire than the other eightPythiumspecies examined, which may result from gene duplication and genome rearrangement events as indicated by synteny analysis. Expression pattern analysis of RXLR-encoding genes in the plant pathogenP. ultimumdetected transcripts from the vast majority of predictedRXLRswith some of them being induced at infection stages. One such RXLRs showed necrosis-inducing activity. Furthermore, all predictedRXLRswere cloned from two biocontrol agentsP. oligandrumandP. periplocum. Three of them were found to encode effectors inducing defense response inNicotiana benthamiana. Taken together, our findings represent the first complete synopsis ofPythiumRXLR effectors, which provides critical clues on their evolutionary patterns as well as the mechanisms of their interactions with diverse hosts.Author summaryPathogens from thePythiumgenus are widespread across multiple ecological niches. Most of them are soilborne plant pathogens whereas others cause infectious diseases in mammals. SomePythiumspecies can be used as biocontrol agents for plant diseases or mosquito management. Despite that phylogenetically close oomycete pathogens secrete RXLR effectors to enable infection, no RXLR protein was previously characterized in anyPythiumspecies. Here we developed a stringent method to predictPythiumRXLR effectors and compared them with known RXLRs from other species. All oomycetous RXLRs form a huge superfamily, which indicates they may share a common ancestor. Our sequence analysis results suggest that the expansion of RXLR repertoire results from gene duplication and genome recombination events. We further demonstrated that most predictedPythium RXLRscan be transcribed and some of them encode effectors exhibiting pathogenic or defense-inducing activities. This work expands our understanding of RXLR evolution in oomycetes in general, and provides novel insights into the molecular interactions betweenPythiumpathogens and their diverse hosts.
Cold Spring Harbor Laboratory
Title: Prediction and Characterization of RXLR Effectors inPythiumSpecies
Description:
AbstractBeing widely existed in oomycetes, the RXLR effector features conserved RXLR-dEER motifs in its N terminal.
Every knownPhytophthoraorHyaloperonosporapathogen harbors hundreds of RXLRs.
InPythiumspecies, however, none of the RXLR effectors has been characterized yet.
Here, we developed a stringent method forde novoidentification of RXLRs and characterized 359 putative RXLR effectors from nine testedPythiumspecies.
Phylogenetic analysis revealed a single superfamily formed by all oomycetous RXLRs, suggesting they descent from a common ancestor.
RXLR effectors fromPythiumandPhytophthoraspecies exhibited similar sequence features, protein structures and genome locations.
In particular, the mosquito biological agentP.
guiyangensecontains a significantly larger RXLR repertoire than the other eightPythiumspecies examined, which may result from gene duplication and genome rearrangement events as indicated by synteny analysis.
Expression pattern analysis of RXLR-encoding genes in the plant pathogenP.
ultimumdetected transcripts from the vast majority of predictedRXLRswith some of them being induced at infection stages.
One such RXLRs showed necrosis-inducing activity.
Furthermore, all predictedRXLRswere cloned from two biocontrol agentsP.
oligandrumandP.
periplocum.
Three of them were found to encode effectors inducing defense response inNicotiana benthamiana.
Taken together, our findings represent the first complete synopsis ofPythiumRXLR effectors, which provides critical clues on their evolutionary patterns as well as the mechanisms of their interactions with diverse hosts.
Author summaryPathogens from thePythiumgenus are widespread across multiple ecological niches.
Most of them are soilborne plant pathogens whereas others cause infectious diseases in mammals.
SomePythiumspecies can be used as biocontrol agents for plant diseases or mosquito management.
Despite that phylogenetically close oomycete pathogens secrete RXLR effectors to enable infection, no RXLR protein was previously characterized in anyPythiumspecies.
Here we developed a stringent method to predictPythiumRXLR effectors and compared them with known RXLRs from other species.
All oomycetous RXLRs form a huge superfamily, which indicates they may share a common ancestor.
Our sequence analysis results suggest that the expansion of RXLR repertoire results from gene duplication and genome recombination events.
We further demonstrated that most predictedPythium RXLRscan be transcribed and some of them encode effectors exhibiting pathogenic or defense-inducing activities.
This work expands our understanding of RXLR evolution in oomycetes in general, and provides novel insights into the molecular interactions betweenPythiumpathogens and their diverse hosts.
Related Results
An RXLR effector targets ER-Golgi interface to induce ER stress and necrotic cell death
An RXLR effector targets ER-Golgi interface to induce ER stress and necrotic cell death
AbstractTo achieve successful colonization, the pathogen secretes hundreds of effectors into host cells to manipulate the host’s immune response. Despite numerous studies, the mole...
Interpreting and evaluating digital soil mapping prediction uncertainties
Interpreting and evaluating digital soil mapping prediction uncertainties
There is an implicit quality associated with all soil maps which depends on a range of factors including the mapping algorithm, the extent and quality of the calibration data, and ...
State prediction of MR system by VMD-GRNN based on fractal dimension
State prediction of MR system by VMD-GRNN based on fractal dimension
Taking the test signals of magneto-rheological vibration system under different states as research objects, four Generalized Regression Neural Network (GRNN) prediction algorithms,...
P-180 Enhancing pregnancy prediction performance by combining pregnancy prediction AI with euploid prediction AI
P-180 Enhancing pregnancy prediction performance by combining pregnancy prediction AI with euploid prediction AI
Abstract
Study question
Can the combination of two different AI models, Life Whisperer “Viability” and Life Whisperer “Genetics”...
Characterization of the chorismate mutase effector (SsCm1) from Sclerotinia sclerotiorum
Characterization of the chorismate mutase effector (SsCm1) from Sclerotinia sclerotiorum
Sclerotinia sclerotiorum is a filamentous fungus (mold) that causes plant disease. It has an extremely wide range of hosts (>400 species) and causes considerable damage (annual ...
Multiple Machine Learning Methods for Runoff Prediction: Contrast and Improvement
Multiple Machine Learning Methods for Runoff Prediction: Contrast and Improvement
Abstract
Machine learning methods provide new alternative methods and ideas for runoff prediction. In order to improve the application of machine learning methods in the fi...
ARIMA-FSVR Hybrid Method for High-Speed Railway Passenger Traffic Forecasting
ARIMA-FSVR Hybrid Method for High-Speed Railway Passenger Traffic Forecasting
In order to improve the prediction accuracy of railway passenger traffic, an ARIMA model and FSVR are combined to propose a hybrid prediction method. The ARIMA prediction model is ...

