title: A pattern recognition tool for quantitative analysis of in planta hyphal growth of powdery mildew fungi.
abstract: The development of fungal pathogens can be quantified easily at the level of spore germination or penetration. However, the exact quantification of hyphal growth rates after initial, successful host invasion is much more difficult. Here, we report on the development of a new pattern recognition software (HyphArea) for automated quantitative analysis of hyphal growth rates of powdery mildew fungi on plant surfaces that usually represent highly irregular and noisy image backgrounds. By using HyphArea, we measured growth rates of colonies of the barley powdery mildew, Blumeria graminis f. sp. hordei, on susceptible and induced-resistant host plants. Hyphal growth was not influenced by the resistance state of the plants up to 48 h postinoculation. At later time points, growth rate increased on susceptible plants, whereas it remained restricted on induced-resistant plants. This difference in hyphal growth rate was accompanied by lack of secondary haustoria formation on induced-resistant plants, suggesting that induced resistance in barley against Blumeria graminis is caused mainly by reduced penetration rates of primary as well as secondary appressoria leading, finally, to fewer and less-developed fungal colonies. No evidence was found for reduced nutrient-uptake efficiency of the primary haustoria in induced-resistant leaves, which would be expected to have resulted in reduced hyphal growth rates during the first 48 h of the interaction.
authors.affiliation: Institute of Plant Genetics and Crop Plant Research, Corrensstrasse 3, D-06466 Gatersleben, Germany.
authors.forename: U
authors.initials: U
authors.lastname: Seiffert
authors.forename: P
authors.initials: P
authors.lastname: Schweizer
mesh_terms: D001203:Ascomycota / Q000254:growth & development / Q000472:pathogenicity; D005658:Fungi / Q000254:growth & development* / Q000472:pathogenicity*; D001467:Hordeum / Q000382:microbiology; D007091:Image Processing, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D010935:Plant Diseases / Q000382:microbiology; D010944:Plants / Q000382:microbiology*; D012984:Software