Basil Downy Mildew (Peronospora belbahrii): Discoveries and Challenges Relative to Its Control.

Basil (Ocimum spp.) is one of the most economically important and widely grown herbs in the world. Basil downy mildew, caused by Peronospora belbahrii, has become an important disease in sweet basil (O. basilicum) production worldwide in the past decade. Global sweet basil production is at significant risk to basil downy mildew because of the lack of genetic resistance and the ability of the pathogen to be distributed on infested seed. Controlling the disease is challenging and consequently many crops have been lost. In the past few years, plant breeding efforts have been made to identify germplasm that can be used to introduce downy mildew resistance genes into commercial sweet basils while ensuring that resistant plants have the correct phenotype, aroma, and tastes needed for market acceptability. Fungicide efficacy studies have been conducted to evaluate current and newly developed conventional and organic fungicides for its management with limited success. This review explores the current efforts and progress being made in understanding basil downy mildew and its control.

Variability Among Forecast Models for the Apple Sooty Blotch/Flyspeck Disease Complex.

Several disease forecast models have been developed to guide treatment of the sooty blotch and flyspeck (SBFS) disease complex of apple. Generally, these empirical models are based on the accumulation of hours of leaf wetness (leaf wetness duration [LWD]) from a biofix at or near the phenological growth stage petal fall, when apple flower petals senesce and drop. The models recommend timing of the initial fungicide application targeting SBFS. However, there are significant differences among SBFS forecast models in terms of biofix and the length of LWD thresholds. A comparison of models using a single input data set generated recommendations for the first SBFS fungicide application that differed by up to 5 weeks. In an attempt to improve consistency among models, potential sources for differences were examined. Leaf wetness (LW) is a particularly variable parameter among models, depending on whether on-site or remote weather data were used, the types of sensors and their placement for on-site monitors, and the models used to estimate LW remotely. When SBFS models are applied in the field, recommended treatment thresholds do not always match the method of data acquisition, leading to potential failures. Horticultural factors, such as tree size, canopy density, and cultivar, and orchard site factors such as the distance to potential inoculum sources can impact risk of SBFS and should also be considered in forecast models. The number of fungal species identified as contributors to the SBFS disease complex has expanded tremendously in recent years. A lack of understanding of key epidemiological factors for different fungi in the complex, and which fungi represent the most challenging management problems, are obvious issues in the development of improved SBFS models. If SBFS forecast models are to be adopted, researchers will need to address these issues.

A De Novo-Assembly Based Data Analysis Pipeline for Plant Obligate Parasite Metatranscriptomic Studies.

Current and emerging plant diseases caused by obligate parasitic microbes such as rusts, downy mildews, and powdery mildews threaten worldwide crop production and food safety. These obligate parasites are typically unculturable in the laboratory, posing technical challenges to characterize them at the genetic and genomic level. Here we have developed a data analysis pipeline integrating several bioinformatic software programs. This pipeline facilitates rapid gene discovery and expression analysis of a plant host and its obligate parasite simultaneously by next generation sequencing of mixed host and pathogen RNA (i.e., metatranscriptomics). We applied this pipeline to metatranscriptomic sequencing data of sweet basil (Ocimum basilicum) and its obligate downy mildew parasite Peronospora belbahrii, both lacking a sequenced genome. Even with a single data point, we were able to identify both candidate host defense genes and pathogen virulence genes that are highly expressed during infection. This demonstrates the power of this pipeline for identifying genes important in host-pathogen interactions without prior genomic information for either the plant host or the obligate biotrophic pathogen. The simplicity of this pipeline makes it accessible to researchers with limited computational skills and applicable to metatranscriptomic data analysis in a wide range of plant-obligate-parasite systems.