Plant disease symptoms exhibit complex spatial and temporal patterns that are challenging to quantify. Image-based phenotyping approaches enable multidimensional characterization of host-microbe interactions and are well suited to capture spatial and temporal data that are key to understanding disease progression. We applied image-based methods to investigate cassava bacterial blight, which is caused by the pathogen Xanthomonas axonopodis pv. manihotis (Xam). We generated Xam strains in which individual predicted type III effector (T3E) genes were mutated and applied multiple imaging approaches to investigate the role of these proteins in bacterial virulence. Specifically, we quantified bacterial populations, water-soaking disease symptoms, and pathogen spread from the site of inoculation over time for strains with mutations in avrBs2, xopX, and xopK as compared to wild-type Xam. ΔavrBs2 and ΔxopX both showed reduced growth in planta and delayed spread through the vasculature system of cassava. ΔavrBs2 exhibited reduced water-soaking symptoms at the site of inoculation. In contrast, ΔxopK exhibited enhanced induction of disease symptoms at the site of inoculation but reduced spread through the vasculature. Our results highlight the importance of adopting a multipronged approach to plant disease phenotyping to more fully understand the roles of T3Es in virulence. Finally, we demonstrate that the approaches used in this study can be extended to many host-microbe systems and increase the dimensions of phenotype that can be explored.

Physalis is an economically important and morphologically diverse genus of plants with solitary flowers and fruits that are enveloped by an inflated fruiting calyx. Although work to resolve phylogenetic relationships in this clade is ongoing, Physalis remains a taxonomically complex genus with multiple nomenclatural problems. Here, we review 28 species from the United States and their synonyms as well as clarification on the status of their types. We propose 53 typifications. We select a lectotype for 49 names and a neotype for three names (P. ixocarpa, P. linkiana, P. ramosissima). We additionally designate an epitype for P. longifolia.

Plant disease symptoms exhibit complex spatial and temporal patterns that are challenging to quantify. Image-based phenotyping approaches enable multi-dimensional characterization of host-microbe interactions and are well suited to capture spatial and temporal data that are key to understanding disease progression. We applied image-based methods to investigate cassava bacterial blight, which is caused by the pathogen Xanthomonas axonopodis pv. manihotis (Xam). We generated Xam strains in which individual predicted type III effector (T3E) genes were mutated and applied multiple imaging approaches to investigate the role of these proteins in bacterial virulence. Specifically, we quantified bacterial populations, water-soaking disease symptoms, and pathogen spread from the site of inoculation over time for strains with mutations in avrBs2, xopX, and xopK as compared to wild-type Xam. ΔavrBs2 and ΔxopX both showed reduced growth in planta and delayed spread through the vasculature system of cassava. ΔavrBs2 exhibited reduced water-soaking symptoms at the site of inoculation. In contrast, ΔxopK exhibited enhanced induction of disease symptoms at the site of inoculation but reduced spread through the vasculature. Our results highlight the importance of adopting a multi-pronged approach to plant disease phenotyping to more fully understand the roles of T3Es in virulence. Finally, we demonstrate that the approaches used in this study can be extended to many host-microbe systems and increase the dimensions of phenotype that can be explored.Summary:Novel, image-based phenotyping methods enhance characterization of plant-pathogen interactions.

Variation in mating systems is prevalent throughout angiosperms, with many transitions between outcrossing and selfing above and below the species level. This study documents a new case of an intraspecific breakdown of self-incompatibility in a wild relative of tomatillo, Physalis acutifolia. We used controlled greenhouse crosses to identify self-incompatible (SI) and self-compatible (SC) individuals grown from seed sampled across seven sites across Arizona and New Mexico. We measured fourteen flower and fruit traits to test for trait variation associated with mating system. We also quantified pollen tube growth in vivo and tested for the presence of the S-RNase proteins in SI and SC styles. We found that seed from six of the seven sites produced SI individuals that terminated self pollen tubes in the style and showed detectable S-RNase expression. By contrast, seed from one Arizona site produced SC individuals with no S-RNase expression. These SC individuals displayed typical selfing syndrome traits such as smaller corollas, reduced sigma-anther distances, and a smaller pollen-ovule ratio. We also found plasticity in self-incompatibility as most of the SI individuals became SC and lost S-RNase expression roughly after six months in the greenhouse. While fixed differences in mating systems are known among the self-incompatible wild species and the often self-compatible domesticated tomatillos, our study is the first to demonstrate intraspecific variation in natural populations as well as variation in SI over an individual’s lifespan.

Syndromes, wherein multiple traits evolve convergently in response to a shared selective driver, form a central concept in ecology and evolution. Recent work has questioned the utility and indeed the existence of some of the classic syndromes, such as pollination and seed dispersal syndromes. Here, we discuss some of the major issues that have plagued research into syndromes in macroevolution. First, observation of co-evolving traits (sometimes called "trait syndromes'') is often used as evidence of adaptation to a particular driver, even when the link between traits and adaptation is not well-tested. Second, the study of syndromes often uses a biased sampling approach, focusing on the most extreme examples, which may obscure significant continuous variation between traits. Finally, researchers often focus on the traits that are easiest to measure even though these may not be the most directly relevant to adaptive hypotheses. We argue that these issues can be avoided by combining macroevolutionary studies of trait variation across entire clades with explicit tests of adaptive hypotheses, and that taking this approach will lead to a better understanding of syndrome-like evolution and its drivers.

Syndromes, wherein multiple traits evolve convergently in response to a shared selective driver, form a central concept in ecology and evolution. Recent work has questioned the utility and indeed the existence of some of the classic syndromes, such as pollination and seed dispersal syndromes. Here, we discuss some of the major issues that have plagued research into syndromes in macroevolution. First, observation of co-evolving traits (sometimes called “trait syndromes'') is often used as evidence of adaptation to a particular driver, even when the link between traits and adaptation is not well-tested. Second, the study of syndromes often uses a biased sampling approach, focusing on the most extreme examples, which may obscure significant continuous variation between traits. Finally, researchers often focus on the traits that are easiest to measure even though these may not be the most directly relevant to adaptive hypotheses. We argue that these issues can be avoided by combining macroevolutionary studies of trait variation across entire clades with explicit tests of adaptive hypotheses, and that taking this approach will lead to a better understanding of syndrome-like evolution and its drivers.

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