Resistance in Maize (Zea mays) to Isolates of Puccinia sorghi from Eastern Australia

Uncovering the Mechanisms: The Role of Biotrophic Fungi in Activating or Suppressing Plant Defense Responses

This paper discusses the mechanisms by which fungi manipulate plant physiology and suppress plant defense responses by producing effectors that can target various host proteins. Effector-triggered immunity and effector-triggered susceptibility are pivotal elements in the complex molecular dialogue underlying plant-pathogen interactions. Pathogen-produced effector molecules possess the ability to mimic pathogen-associated molecular patterns or hinder the binding of pattern recognition receptors. Effectors can directly target nucleotide-binding domain, leucine-rich repeat receptors, or manipulate downstream signaling components to suppress plant defense. Interactions between these effectors and receptor-like kinases in host plants are critical in this process. Biotrophic fungi adeptly exploit the signaling networks of key plant hormones, including salicylic acid, jasmonic acid, abscisic acid, and ethylene, to establish a compatible interaction with their plant hosts. Overall, the paper highlights the importance of understanding the complex interplay between plant defense mechanisms and fungal effectors to develop effective strategies for plant disease management.

Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher - log p values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.