Recently, genome sequencing of many isolates of genetically monomorphic bacterial human pathogens has given new insights into pathogen microevolution and phylogeography. Here, we report a genome-based micro-evolutionary study of a bacterial plant pathogen, Pseudomonas syringae pv. tomato. Only 267 mutations were identified between five sequenced isolates in 3,543,009 nt of analyzed genome sequence, which suggests a recent evolutionary origin of this pathogen. Further analysis with genome-derived markers of 89 world-wide isolates showed that several genotypes exist in North America and in Europe indicating frequent pathogen movement between these world regions. Genome-derived markers and molecular analyses of key pathogen loci important for virulence and motility both suggest ongoing adaptation to the tomato host. A mutational hotspot was found in the type III-secreted effector gene hopM1. These mutations abolish the cell death triggering activity of the full-length protein indicating strong selection for loss of function of this effector, which was previously considered a virulence factor. Two non-synonymous mutations in the flagellin-encoding gene fliC allowed identifying a new microbe associated molecular pattern (MAMP) in a region distinct from the known MAMP flg22. Interestingly, the ancestral allele of this MAMP induces a stronger tomato immune response than the derived alleles. The ancestral allele has largely disappeared from today's Pto populations suggesting that flagellin-triggered immunity limits pathogen fitness even in highly virulent pathogens. An additional non-synonymous mutation was identified in flg22 in South American isolates. Therefore, MAMPs are more variable than expected differing even between otherwise almost identical isolates of the same pathogen strain.
Summary The bacterial flagellin (FliC) epitopes flg22 and flgII-28 are microbe-associated molecular patterns (MAMPs). While flg22 is recognized by many plant species via the pattern recognition receptor FLS2, neither the flgII-28 receptor nor the extent of flgII-28 recognition by different plant families is known.Here we tested the significance of flgII-28 as a MAMP and the importance of allelic diversity in flg22 and flgII-28 in plant–pathogen interactions using purified peptides and a Pseudomonas syringae ΔfliC mutant complemented with different fliC alleles.Plant genotype and allelic diversity in flg22 and flgII-28 were found to significantly affect the plant immune response but not bacterial motility. Recognition of flgII-28 is restricted to a number of Solanaceous species. While the flgII-28 peptide does not trigger any immune response in Arabidopsis, mutations in both flg22 and flgII-28 have FLS2-dependent effects on virulence. However, expression of a tomato allele of FLS2 does not confer to Nicotiana benthamiana the ability to detect flgII-28 and tomato plants silenced for FLS2 are not altered in flgII-28 recognition.Therefore, MAMP diversification is an effective pathogen virulence strategy and flgII-28 appears to be perceived by a yet unidentified receptor in the Solanaceae although it has an FLS2-dependent virulence effect in Arabidopsis.
Although there are adequate DNA sequence differences among plant-associated and plant-pathogenic bacteria to facilitate molecular approaches for their identification, identification at a taxonomic level that is predictive of their phenotype is a challenge. The problem is the absence of a taxonomy that describes genetic variation at a biologically relevant resolution and of a database containing reference strains for comparison. Moreover, molecular evolution, population genetics, ecology, and epidemiology of many plant-pathogenic and plant-associated bacteria are still poorly understood. To address these challenges, a database with web interface was specifically designed for plant-associated and plant-pathogenic microorganisms. The Plant-Associated Microbes Database (PAMDB) comprises, thus far, data from multilocus sequence typing and analysis (MLST/MLSA) studies of Acidovorax citrulli, Pseudomonas syringae, Ralstonia solanacearum, and Xanthomonas spp. Using data deposited in PAMDB, a robust phylogeny of Xanthomonas axonopodis and related bacteria has been inferred, and the diversity existing in the Xanthomonas genus and in described Xanthomonas spp. has been compared with the diversity in P. syringae and R. solanacearum. Moreover, we show how PAMDB makes it easy to distinguish between different pathogens that cause almost identical diseases. The scalable design of PAMDB will make it easy to add more plant pathogens in the future.
SummaryWhile the existence of environmental reservoirs of human pathogens is well established, less is known about the role of nonagricultural environments in emergence, evolution, and spread of crop pathogens.Here, we analyzed phylogeny, virulence genes, host range, and aggressiveness of Pseudomonas syringae strains closely related to the tomato pathogen P. syringae pv. tomato (Pto), including strains isolated from snowpack and streams.The population of Pto relatives in nonagricultural environments was estimated to be large and its diversity to be higher than that of the population of Pto and its relatives on crops. Ancestors of environmental strains, Pto, and other genetically monomorphic crop pathogens were inferred to have frequently recombined, suggesting an epidemic population structure for P. syringae. Some environmental strains have repertoires of type III-secreted effectors very similar to Pto, are almost as aggressive on tomato as Pto, but have a wider host range than typical Pto strains.We conclude that crop pathogens may have evolved through a small number of evolutionary events from a population of less aggressive ancestors with a wider host range present in nonagricultural environments.
In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus "observation") in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools.KEYWORDS: observation-level interaction, visual analytics, statistical models. INDEX TERMS: H.5.0 [Human-Computer Interaction] INTRODUCTIONVisual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces" [1]. The goal of visual analytics (VA) is to extract information, perform exploratory analyses, and validate hypotheses through an interactive exploration process known as sensemaking [2]. In this sensemaking loop, users proceed through a complex combination of proposing and evaluating hypotheses and schemas about their data, with the ultimate goal of gaining insight (i.e. "making sense of" the data). A wide variety of statistical models have been specifically designed for visualizations of this purpose. Thus, many visual analytic systems are fundamentally based on interaction with statistical models and algorithms, using visualization as the medium for the communication (i.e. where the interaction occurs). This communication is performed via direct interaction with the parameters of the model. For example, Interactive Principal Component Analysis, iPCA [3], allows the user to change the weight for each dimension in calculating the direction of projection using multiple sliders (one slider per dimension). Also, in an interactive visualization using MDS [4], the user can weight the dissimilarities in the calculation of the stress function through similar visual controls.In both instances, the model is made aware of the user input through a formal and direct modification of a parameter (i.e. parameter level interacti...
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.