Pathogens that infect plants and animals use a diverse arsenal of effector proteins to suppress the host immune system and promote infection. Identification of effectors in pathogen genomes is foundational to understanding mechanisms of pathogenesis, for monitoring field pathogen populations, and for breeding disease resistance. We identified candidate effectors from the lettuce downy mildew pathogen Bremia lactucae by searching the predicted proteome for the WY domain, a structural fold found in effectors that has been implicated in immune suppression as well as effector recognition by host resistance proteins. We predicted 55 WY domain containing proteins in the genome of B. lactucae and found substantial variation in both sequence and domain architecture. These candidate effectors exhibit several characteristics of pathogen effectors, including an N-terminal signal peptide, lineage specificity, and expression during infection. Unexpectedly, only a minority of B. lactucae WY effectors contain the canonical N-terminal RXLR motif, which is a conserved feature in the majority of cytoplasmic effectors reported in Phytophthora spp. Functional analysis of 21 effectors containing WY domains revealed 11 that elicited cell death on wild accessions and domesticated lettuce lines containing resistance genes, indicative of recognition of these effectors by the host immune system. Only two of the 11 recognized effectors contained the canonical RXLR motif, suggesting that there has been an evolutionary divergence in sequence motifs between genera; this has major consequences for robust effector prediction in oomycete pathogens.
The predictive receiver operating characteristic (PROC) curve differs from the morewell-known receiver operating characteristic (ROC) curve in that it provides a basis for theevaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the testrather than metrics defined conditionally on the actual disease status. Application of PROC curveanalysis may be hindered by the complex graphical patterns that are sometimes generated. Herewe present an information theoretic analysis that allows concurrent evaluation of PROC curves andROC curves together in a simple graphical format. The analysis is based on the observation thatmutual information may be viewed both as a function of ROC curve summary statistics (sensitivityand specificity) and prevalence, and as a function of predictive values and prevalence. Mutualinformation calculated from a 2 × 2 prediction-realization table for a specified risk score thresholdon an ROC curve is the same as the mutual information calculated at the same risk score thresholdon a corresponding PROC curve. Thus, for a given value of prevalence, the risk score threshold thatmaximizes mutual information is the same on both the ROC curve and the corresponding PROCcurve. Phytopathologists and clinicians who have previously relied solely on ROC curve summarystatistics when formulating risk thresholds for application in practical agricultural or clinicaldecision-making contexts are thus presented with a methodology that brings predictive valueswithin the scope of that formulation.
Pathogens infecting plants and animals use a diverse arsenal of effector proteins to suppress the host immune system and promote infection. Identification of effectors in pathogen genomes is foundational to understanding mechanisms of pathogenesis, for monitoring field pathogen populations, and for breeding disease resistance. We identified candidate effectors from the lettuce downy mildew pathogen, Bremia lactucae, using comparative genomics and bioinformatics to search for the WY domain. This conserved structural element is found in Phytophthora effectors and some other oomycete pathogens; it has been implicated in the immune-suppressing function of these effectors as well as their recognition by host resistance proteins. We predicted 54 WY domain containing proteins in isolate SF5 of B. lactucae that have substantial variation in both sequence and domain architecture. These candidate effectors exhibit several characteristics of pathogen effectors, including an N-terminal signal peptide, lineage specificity, and expression during infection. Unexpectedly, only a minority of B. lactucae WY effectors contain the canonical N-terminal RXLR motif, which is a conserved feature in the majority of cytoplasmic effectors reported in Phytophthora spp. Functional analysis effectors containing WY domains revealed eleven out of 21 that triggered necrosis, which is characteristic of the immune response on wild accessions and domesticated lettuce lines containing resistance genes. Only two of the eleven recognized effectors contained a canonical RXLR motif, suggesting that there has been an evolutionary divergence in sequence motifs between genera; this has major consequences for robust effector prediction in oomycete pathogens.Author SummaryThere is a microscopic battle that takes place at the molecular level during infection of plants and animals by pathogens. Some of the weapons that pathogens battle with are known as “effectors,” which are secreted proteins that enter host cells to alter physiology and suppress the immune system. Effectors can also be a liability for plant pathogens because plants have evolved ways to recognize these effectors, triggering a defense response leading to localized cell death, which prevents the spread of the pathogen. Here we used computer models to predict effectors from the genome of Bremia lactucae, the causal agent of lettuce downy mildew. Three effectors were demonstrated to suppress the basal immune system of lettuce. Eleven effectors were recognized by one or more resistant lines of lettuce. In addition to contributing to our understanding of the mechanisms of pathogenesis, this study of effectors is useful for breeding disease resistant lettuce, decreasing agricultural reliance on fungicides.
The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain “non-standard” data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.