Scotland Leman

The plant pathogen Pseudomonas syringae pv. tomato is genetically monomorphic and under strong selection to evade tomato immunity.

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 todays 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.

Observation-level interaction with statistical models for visual analytics

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.

Allelic variation in two distinct Pseudomonas syringae flagellin epitopes modulates the strength of plant immune responses but not bacterial motility

The bacterial flagellin (FliC) epitopes flg22 and flgII-28 are microbe-associated molecular patterns (MAMPs). Although 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 ∆fliC mutant complemented with different fliC alleles. The plant genotype and allelic diversity in flg22 and flgII-28 were found to significantly affect the plant immune response, but not bacterial motility. The recognition of flgII-28 is restricted to a number of solanaceous species. Although 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, the 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 an as yet unidentified receptor in the Solanaceae, although it has an FLS2-dependent virulence effect in Arabidopsis.

Nonagricultural reservoirs contribute to emergence and evolution of Pseudomonas syringae crop pathogens

While 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.

Genome Landscape and Evolutionary Plasticity of Chromosomes in Malaria Mosquitoes

Background Nonrandom distribution of rearrangements is a common feature of eukaryotic chromosomes that is not well understood in terms of genome organization and evolution. In the major African malaria vector Anopheles gambiae, polymorphic inversions are highly nonuniformly distributed among five chromosomal arms and are associated with epidemiologically important adaptations. However, it is not clear whether the genomic content of the chromosomal arms is associated with inversion polymorphism and fixation rates. Methodology/Principal Findings To better understand the evolutionary dynamics of chromosomal inversions, we created a physical map for an Asian malaria mosquito, Anopheles stephensi, and compared it with the genome of An. gambiae. We also developed and deployed novel Bayesian statistical models to analyze genome landscapes in individual chromosomal arms An. gambiae. Here, we demonstrate that, despite the paucity of inversion polymorphisms on the X chromosome, this chromosome has the fastest rate of inversion fixation and the highest density of transposable elements, simple DNA repeats, and GC content. The highly polymorphic and rapidly evolving autosomal 2R arm had overrepresentation of genes involved in cellular response to stress supporting the role of natural selection in maintaining adaptive polymorphic inversions. In addition, the 2R arm had the highest density of regions involved in segmental duplications that clustered in the breakpoint-rich zone of the arm. In contrast, the slower evolving 2L, 3R, and 3L, arms were enriched with matrix-attachment regions that potentially contribute to chromosome stability in the cell nucleus. Conclusions/Significance These results highlight fundamental differences in evolutionary dynamics of the sex chromosome and autosomes and revealed the strong association between characteristics of the genome landscape and rates of chromosomal evolution. We conclude that a unique combination of various classes of genes and repetitive DNA in each arm, rather than a single type of repetitive element, is likely responsible for arm-specific rates of rearrangements.

Likelihoods From Summary Statistics: Recent Divergence Between Species

We describe an importance-sampling method for approximating likelihoods of population parameters based on multiple summary statistics. In this first application, we address the demographic history of closely related members of the Drosophila pseudoobscura group. We base the maximum-likelihood estimation of the time since speciation and the effective population sizes of the extant and ancestral populations on the pattern of nucleotide variation at DPS2002, a noncoding region tightly linked to a paracentric inversion that strongly contributes to reproductive isolation. Consideration of summary statistics rather than entire nucleotide sequences permits a compact description of the genealogy of the sample. We use importance sampling first to propose a genealogical and mutational history consistent with the observed array of summary statistics and then to correct the likelihood with the exact probability of the history determined from a system of recursions. Analysis of a subset of the data, for which recursive computation of the exact likelihood was feasible, indicated close agreement between the approximate and exact likelihoods. Our results for the complete data set also compare well with those obtained through Metropolis-Hastings sampling of fully resolved genealogies of entire nucleotide sequences.

A System to Automatically Classify and Name Any Individual Genome-Sequenced Organism Independently of Current Biological Classification and Nomenclature

A broadly accepted and stable biological classification system is a prerequisite for biological sciences. It provides the means to describe and communicate about life without ambiguity. Current biological classification and nomenclature use the species as the basic unit and require lengthy and laborious species descriptions before newly discovered organisms can be assigned to a species and be named. The current system is thus inadequate to classify and name the immense genetic diversity within species that is now being revealed by genome sequencing on a daily basis. To address this lack of a general intra-species classification and naming system adequate for today’s speed of discovery of new diversity, we propose a classification and naming system that is exclusively based on genome similarity and that is suitable for automatic assignment of codes to any genome-sequenced organism without requiring any phenotypic or phylogenetic analysis. We provide examples demonstrating that genome similarity-based codes largely align with current taxonomic groups at many different levels in bacteria, animals, humans, plants, and viruses. Importantly, the proposed approach is only slightly affected by the order of code assignment and can thus provide codes that reflect similarity between organisms and that do not need to be revised upon discovery of new diversity. We envision genome similarity-based codes to complement current biological nomenclature and to provide a universal means to communicate unambiguously about any genome-sequenced organism in fields as diverse as biodiversity research, infectious disease control, human and microbial forensics, animal breed and plant cultivar certification, and human ancestry research.

The Multiset Sampler

We introduce the multiset sampler (MSS), a new Metropolis–Hastings algorithm for drawing samples from a posterior distribution. The MSS is designed to be effective when the posterior has the feature that the parameters can be divided into two sets, X, the parameters of interest and Y, the nuisance parameters. We contemplate a sampler that iterates between X moves and Y moves. We consider the case where either (a) Y is discrete and lives on a finite set or (b) Y is continuous and lives on a bounded set. After presenting some background, we define a multiset and show how to construct a distribution on one. The construction may seem artificial and pointless at first, but several small examples illustrate its value. Finally, we demonstrate the MSS in several realistic examples and compare it with alternatives.

Reconstructing host range evolution of bacterial plant pathogens using Pseudomonas syringae pv. tomato and its close relatives as a model.

Several lines of evidence suggest that highly virulent bacterial human pathogens evolved from less virulent wider host range animal pathogens since human migration out of Africa. To investigate evolution of host specificity of bacterial plant pathogens, here we report a molecular evolutionary analysis of the model plant pathogen Pseudomonas syringae pv. tomato DC3000 and of close relatives that are pathogens of a diverse set of crop plants. Extensive host range tests on five different plant species were performed. Combining phylogenetic data with host range data, a reconstruction of host range of all putative ancestors was performed. In particular, the hypothesis was tested that highly virulent narrow host range pathogens of todays crops grown in monoculture evolved from ancestors with wider host range that were adapted to natural mixed plant communities of pre-agricultural times. We found support for this hypothesis in individual clades. However, reconstruction of host range of the most recent common ancestor of all analyzed strains was not conclusive. Based on the obtained results we stress the importance of including pathogens from wild plants when reconstructing the evolution of plant pathogenic bacteria.

Bayesian visual analytics: BaVA

Leman et al. and Endert et al. develop an interactive data visualization framework called visual to parametric interaction V2PI. With V2PI, experts may explore data visually assess multiple data visualizations based on their judgments and an underlying data analytic method. Specifically, V2PI offers a deterministic procedure to quantify expert judgments and update analytical parameters to create new data visualizations. In this article, we explain V2PI from a probabilistic perspective and develop Bayesian visual analytics BaVA. We model data probabilistically, develop parallels between quantifying expert judgments and eliciting prior distributions from experts, and justify how we update parameters using Bayesian sequential updating. We apply BaVA using two linear projections methods to assess simulated and real-world datasets.