Survival Analysis as a Basis for Testing Hypotheses when Using Quantitative Ordinal Scale Disease Severity Data.

Disease severity in plant pathology is often measured by the amount of a plant or plant part that exhibits disease symptoms. This is typically assessed using a numerical scale, which allows a standardized, convenient, and quick method of rating. These scales, known as quantitative ordinal scales (QOS), divide the percentage scale into a predetermined number of intervals. There are various ways to analyze these ordinal data, with traditional methods involving the use of midpoint conversion to represent the interval. However, this may not be precise enough, as it is only an estimate of the true value. In this case, the data may be considered interval-censored, meaning that we have some knowledge of the value but not an exact measurement. This type of uncertainty is known as censoring, and techniques that address censoring, such as survival analysis (SA), use all available information and account for this uncertainty. To investigate the pros and cons of using SA with QOS measurements, we conducted a simulation based on three pathosystems. The results showed that SA almost always outperformed midpoint conversion with data analyzed using a t test, particularly when data were not normally distributed. Midpoint conversion is currently a standard procedure. In certain cases, the midpoint approach required a 400% increase in sample size to achieve the same power as the SA method. However, as the mean severity increases, fewer additional samples are needed (approximately an additional 100%), regardless of the assessment method used. Based on these findings, we conclude that SA is a valuable method for enhancing the power of hypothesis testing when analyzing QOS severity data. Future research should investigate the wider use of survival analysis techniques in plant pathology and their potential applications in the discipline.

Towards integrative plant pathology.

The field of plant pathology has revealed many of the mechanisms underlying the arms race, providing crucial knowledge and genetic resources for improving plant health. Although the host-microbe interaction seemingly favors rapidly evolving pathogens, it has also generated a vast evolutionary history of largely unexplored plant immunodiversity. We review studies that characterize the scope and distribution of genetic and ecological diversity in model and non-model systems with specific reference to pathogen effector diversity, plant immunodiversity in both cultivated species and their wild relatives, and diversity in the plant-associated microbiota. We show how the study of evolutionary and ecological processes can reveal patterns of genetic convergence, conservation, and diversification, and that this diversity is increasingly tractable in both experimental and translational systems. Perhaps most importantly, these patterns of diversity provide largely untapped resources that can be deployed for the rational engineering of durable resistance for sustainable agriculture.

Plant virology: an RNA treasure trove.

Key principles pertaining to RNA biology not infrequently have their origins in plant virology. Examples have arisen from studies on viral RNA-intrinsic properties and the infection process from gene expression, replication, movement, and defense evasion to biotechnological applications. Since RNA is at the core of the central dogma in molecular biology, how plant virology assisted in the reinforcement or adaptations of this concept, while at other instances shook up elements of the doctrine, is discussed. Moreover, despite the negative effects of viral diseases in agriculture worldwide, plant viruses can be considered a scientific treasure trove. Today they remain tools of discovery for biotechnology, studying evolution, cell biology, and host-microbe interactions.

Using Brainwave Patterns Recorded from Plant Pathology Experts to Increase the Reliability of AI-Based Plant Disease Recognition System.

One of the most challenging problems associated with the development of accurate and reliable application of computer vision and artificial intelligence in agriculture is that, not only are massive amounts of training data usually required, but also, in most cases, the images have to be properly labeled before models can be trained. Such a labeling process tends to be time consuming, tiresome, and expensive, often making the creation of large labeled datasets impractical. This problem is largely associated with the many steps involved in the labeling process, requiring the human expert rater to perform different cognitive and motor tasks in order to correctly label each image, thus diverting brain resources that should be focused on pattern recognition itself. One possible way to tackle this challenge is by exploring the phenomena in which highly trained experts can almost reflexively recognize and accurately classify objects of interest in a fraction of a second. As techniques for recording and decoding brain activity have evolved, it has become possible to directly tap into this ability and to accurately assess the expert's level of confidence and attention during the process. As a result, the labeling time can be reduced dramatically while effectively incorporating the expert's knowledge into artificial intelligence models. This study investigates how the use of electroencephalograms from plant pathology experts can improve the accuracy and robustness of image-based artificial intelligence models dedicated to plant disease recognition. Experiments have demonstrated the viability of the approach, with accuracies improving from 96% with the baseline model to 99% using brain generated labels and active learning approach.

Key Discoveries in Plant Pathology During the Past Half Century: Impacts on the Life Sciences and on Plant Disease Management.

Plant pathology plays a critical role in safeguarding plant health, food security, and food safety through science-based solutions to protect plants against recurring and emerging diseases. In addition, plant pathology contributed significantly to basic discoveries that have had broad impacts on the life sciences beyond plant pathology. In December 2021, The American Phytopathological Society (APS) conducted a survey among its members and among the readership of its journals to identify and rank key discoveries in plant pathology that have had broad impacts on science and/or practical disease management during the past half century. Based on the responses received, key discoveries that have broadly impacted the life sciences during that period include the Agrobacterium Ti plasmid and its mechanism in T-DNA transfer, bacterial ice nucleation, cloning of resistance genes, discovery of viroids, effectors and their mechanisms, pattern-triggered immunity and effector-triggered immunity, RNA interference and gene silencing, structure and function of R genes, transcription activator-like effectors, and type-III secretion system and hrp/hrc. Major advances that significantly impacted practical disease management include the deployment and management of host resistance genes; the application of disease models and forecasting systems; the introduction of modern systemic fungicides and host resistance inducers, along with a better understanding of fungicide resistance mechanisms and management; and the utilization of biological controls and suppressive soils, including the implementation of methyl-bromide alternatives. In this special issue, experts from the pertinent fields review the discovery process, recent progress, and impacts of some of the highest ranked discoveries in each category while also pointing out future directions for new discoveries in fundamental and applied plant pathology.

Adjustment and quantification of UV-visible spectrophotometry analysis: an accurate and rapid method for estimating Cladosporium spp. spore concentration in a water suspension.

Cladosporium spp. are among the most important plant pathogens, plant endophytes, insect parasites and human pathogens in nature. The aim of this study was to increase the speed and accuracy of Cladosporium spp. spore counting using UV-visible spectrophotometry based on the regression model in a water suspension. Spores of C. ramotenellum AM55, C. limoniforme Br15, C. tenuissimum K15 and C. cladosporioides Ld13 fungi were diluted in sterile distilled water several times. Spore concentration/ml (SC) was counted with a hemocytometer. The spectrophotometer visible light absorption (ABS) was measured under 14 wavelengths from 300 to 950 nm for each dilution. The results showed that the morphological variation of the spores greatly affect the determination of the suitable wavelength. 650, 750, 500 and 400 nm wavelengths had the highest coefficient of determination (R2) values respectively for C. ramotenellum AM55, C. limoniforme Br15, C. tenuissimum K15 and C. cladosporioides Ld13 on the linear regression model. R2 values were 0.9874, 0.9647, 0.8856 and 0.9711 respectively, for the 650, 750, 500 and 400 nm wavelengths. The linear equation of SC = 107 × ABS-133,040 with the highest R2 value of 0.9532 had the best fit under a combinatorial regression model where SC and ABS of all Cladosporium spp. were presented. The proposed linear regression models can be used under in vivo and in vitro conditions for medicine or plant pathology studies which certainly increase the accuracy and speed of the future experiments compared to the hemocytometer method.

How do emerging long-read sequencing technologies function in transforming the plant pathology research landscape?

KEY MESSAGE: Long-read sequencing technologies are revolutionizing the sequencing and analysis of plant and pathogen genomes and transcriptomes, as well as contributing to emerging areas of interest in plant-pathogen interactions, disease management techniques, and the introduction of new plant varieties or cultivars. Long-read sequencing (LRS) technologies are progressively being implemented to study plants and pathogens of agricultural importance, which have substantial economic effects. The variability and complexity of the genome and transcriptome affect plant growth, development and pathogen responses. Overcoming the limitations of second-generation sequencing, LRS technology has significantly increased the length of a single contiguous read from a few hundred to millions of base pairs. Because of the longer read lengths, new analysis methods and tools have been developed for plant and pathogen genomics and transcriptomics. LRS technologies enable faster, more efficient, and high-throughput ultralong reads, allowing direct sequencing of genomes that would be impossible or difficult to investigate using short-read sequencing approaches. These benefits include genome assembly in repetitive areas, creating more comprehensive and exact genome determinations, assembling full-length transcripts, and detecting DNA and RNA alterations. Furthermore, these technologies allow for the identification of transcriptome diversity, significant structural variation analysis, and direct epigenetic mark detection in plant and pathogen genomic regions. LRS in plant pathology is found efficient for identifying and characterization of effectors in plants as well as known and unknown plant pathogens. In this review, we investigate how these technologies are transforming the landscape of determination and characterization of plant and pathogen genomes and transcriptomes efficiently and accurately. Moreover, we highlight potential areas of interest offered by LRS technologies for future study into plant-pathogen interactions, disease control strategies, and the development of new plant varieties or cultivars.

Computational Analysis of HTS Data and Its Application in Plant Pathology.

High-throughput sequencing is a basic tool of biological research, and it is extensively used in plant pathology projects. Here, we describe how to handle data coming from a variety of sequencing experiments, focusing on the analysis of Illumina reads. We describe how to perform genome assembly and annotation with DNA reads, correctly analyze RNA-seq data to discover differentially expressed genes, handle amplicon sequencing data from microbial communities, and utilize small RNA sequencing data to predict miRNA sequences and their putative targets.

Historical and Scientific Evidence for the Origin and Cultural Importance to Australia's First-Nations Peoples of the Laboratory Accession of Nicotiana benthamiana, a Model for Plant Virology.

Nicotiana benthamiana is an indigenous plant species distributed across northern Australia. The laboratory accession (LAB) of N. benthamiana has become widely adopted as a model host for plant viruses, and it is distinct from other accessions morphologically, physiologically, and by having an attenuation-of-function mutation in the RNA-dependent RNA polymerase 1 (NbRdr1) gene, referred to as NbRdr1m. Recent historical evidence suggested LAB was derived from a 1936 collection by John Cleland at The Granites of the Northern Territory, although no scientific evidence was provided. We provide scientific evidence and further historical evidence supporting the origin of LAB as The Granites. Analysis of a herbarium specimen of N. benthamiana collected by Cleland in 1936 revealed that The Granites population contains plants heterozygous for the NbRdr1 locus, having both the functional NbRdr1 and the mutant NbRdr1m alleles. N. benthamiana was an important cultural asset actively utilised as the narcotic Pituri (chewing tobacco) by the Warlpiri Aboriginal people at the site, who prevented women of child-bearing age from consuming it. We propose that Aboriginal people selected some of the unique traits of LAB that have subsequently facilitated its adoption as a model plant, such as lack of seed dormancy, fast maturity, low nornicotine content, and gracility.

Transmission Electron Microscopic Methods for Plant Virology.

Transmission electron microscopy (TEM) is an important tool for observing the ultrastructure of plant virions and their host cells. The two main applicable TEM technologies used in plant virology are negative staining and ultrathin section. Negative staining is mainly used to observe the high-resolution structure of virus particles under a transmission electron microscope. Sample preparation for negative staining is convenient and fast, making it suitable for studying the virions in crude sap or purified solution. A modification of negative staining, by combining immunological reaction, named as technique of immuno-negative staining, is used to enrich or identify viruses. Ultrathin section is used for ultrastructural cytopathological studies in the virus-infected host cells, including the morphology of virus particles, the structure of viral induced inclusion bodies, the subcellular distribution of virions and the structural alteration of the host cell induced by viral infection. Such information is valuable to analyze the behavior of virus in replication, assembly, and intercellular transportation, and thus to understand the viral infection cycle. The present chapter describes the operation details of negative staining and ultrathin section TEM.

Engineered nanomaterials in plant diseases: can we combat phytopathogens?

Engineered nanomaterials (ENM) have a high potential for use in several areas of agriculture including plant pathology. Nanoparticles (NPs) alone can be applied for disease management due to their antimicrobial properties. Moreover, nanobiosensors allow a rapid and sensitive diagnosis of pathogens because NPs can be conjugated with nucleic acids, proteins and other biomolecules. The use of ENM in diagnosis, delivery of fungicides and therapy is an eco-friendly and economically viable alternative. This review focuses on different promising studies concerning ENM used for plant disease management including viruses, fungi, oomycetes and bacteria; diagnosis and delivery of antimicrobials and factors affecting the efficacy of nanomaterials, entry, translocation and toxicity. Although much research is required on metallic NPs due to the possible risks to the final consumer, ENMs are undoubtedly very useful tools to achieve food security in the world. KEY POINTS: • Increasing global population and fungicides have necessitated alternative technologies. • Nanomaterials can be used for detection, delivery and therapy of plant diseases. • The toxicity issues and safety should be considered before the use of nanomaterials.

Legacy of Plant Virology in Croatia-From Virus Identification to Molecular Epidemiology, Evolution, Genomics and Beyond.

This paper showcases the development of plant virology in Croatia at the University of Zagreb, Faculty of Science, from its beginning in the 1950s until today, more than 70 years later. The main achievements of the previous and current group members are highlighted according to various research topics and fields. Expectedly, some of those accomplishments remained within the field of plant virology, but others make part of a much-extended research spectrum exploring subviral pathogens, prokaryotic plant pathogens, fungi and their viruses, as well as their interactions within ecosystems. Thus, the legacy of plant virology in Croatia continues to contribute to the state of the art of microbiology far beyond virology. Research problems pertinent for directing the future research endeavors are also proposed in this review.

Medicinal potentialities and pathogenic profile of Lasiodiplodia genus.

Considering that current biotechnological advances have been contributing towards improving the well-being of humanity, endophytic fungi, such as Lasiodiplodia, are promising sources of new substances to be used in chemical, pharmaceutical and agrochemical processes. Bioactive secondary metabolites are examples of such substances, although it is widely known that Lasiodiplodia inflicts irreparable damage to several crops of major economic importance. They are often produced as a response against biotic and abiotic factors, thus revealing that they play different roles, such as in signaling and defense mechanisms. Therefore, this review presents a few subtle differences between pathogenicity and mutualistic endophyte-host interactions. Moreover, the main secondary metabolites produced by Lasiodiplodia endophytes have been described with respect to their relevant antimicrobial and cytotoxic activities.

Cytokinin Inhibits Fungal Development and Virulence by Targeting the Cytoskeleton and Cellular Trafficking.

Cytokinin (CK) is an important plant developmental regulator, having activities in many aspects of plant life and response to the environment. CKs are involved in diverse processes in the plant, including stem cell maintenance, vascular differentiation, growth and branching of roots and shoots, leaf senescence, nutrient balance, and stress tolerance. In some cases, phytopathogens secrete CKs. It has been suggested that to achieve pathogenesis in the host, CK-secreting biotrophs manipulate CK signaling to regulate the host cell cycle and nutrient allocation. CK is known to induce host plant resistance to several classes of phytopathogens from a few works, with induced host immunity via salicylic acid signaling suggested to be the prevalent mechanism for this host resistance. Here, we show that CK directly inhibits the growth, development, and virulence of fungal phytopathogens. Focusing on Botrytis cinerea (Bc), we demonstrate that various aspects of fungal development can be reversibly inhibited by CK. We also found that CK affects both budding and fission yeast in a similar manner. Investigating the mechanism by which CK influences fungal development, we conducted RNA next-generation sequencing (RNA-NGS) on mock- and CK-treated B. cinerea samples, finding that CK alters the cell cycle, cytoskeleton, and endocytosis. Cell biology experiments demonstrated that CK affects cytoskeleton components and cellular trafficking in Bc, lowering endocytic rates and endomembrane compartment sizes, likely leading to reduced growth rates and arrested developmental programs. Mutant analyses in yeast confirmed that the endocytic pathway is altered by CK. Our work uncovers a remarkably conserved role for a plant growth hormone in fungal biology, suggesting that pathogen-host interactions resulted in fascinating molecular adaptations on fundamental processes in eukaryotic biology. IMPORTANCE Cytokinins (CKs), important plant growth/developmental hormones, have previously been associated with host disease resistance. Here, we demonstrate that CK directly inhibits the growth, development, and virulence of B. cinerea (Bc) and many additional phytopathogenic fungi. Molecular and cellular analyses revealed that CK is not toxic to Bc, but rather, Bc likely recognizes CK and responds to it, resulting in cell cycle and individual cell growth retardation, via downregulation of cytoskeletal components and endocytic trafficking. Mutant analyses in yeast confirmed that the endocytic pathway is a CK target. Our work demonstrates a conserved role for CK in yeast and fungal biology, suggesting that pathogen-host interactions may cause molecular adaptations in fundamental processes in eukaryotic biology.

Advances in the biological control of phytoparasitic nematodes via the use of nematophagous fungi.

Agricultural production is one of most important activities for food supply and demand, that provides a source of raw materials, and generates commercial opportunities for other industries around the world. It may be both positively and negatively affected by climatic and biological factors. Negative biological factors are those caused by viruses, bacteria, or parasites. Given the serious problems posed by phytoparasitic nematodes for farmers, causing crop losses globally every year, the agrochemical industry has developed compounds with the capacity to inhibit their development; however, they can cause the death of other beneficial organisms and their lixiviation can contaminate the water table. On the other hand, the positive biological factors are found in biotechnology, the scientific discipline that develops products, such as nematophagous fungi (of which Purpureocillium lilacinum and Pochonia chlamydosporia have the greatest potential), for the control of pests and/or diseases. The present review focuses on the importance of nematophagous fungi, particularly sedentary endoparasitic nematodes, their research on the development of biological control agents, the mass production of fungi Purpureocillium lilacinum and Pochonia chlamydosporia, and their limited commercialization due to the lack of rigorous methods that enable the anticipation of complex interactions between plant and phytopathogenic agents.

Reflections on a Career in Plant Virology: A Chip Floating on a Stream.

At the time I entered college and for a few years afterward, I had very few concrete goals. Hence, my progress was more a matter of luck than planning and was somewhat analogous to a small wood chip floating down a slow stream, bumping into various objects tossed and turned hither and thither, all the while being surrounded by larger and more appealing chips. I have been extremely lucky to have been associated with numerous helpful and knowledgeable mentors, colleagues, postdocs, students, and coworkers whose advice had major impacts on my life. Therefore, throughout this article, I have attempted to acknowledge central individuals who contributed to my progress in academia and to highlight the positive bumps to my chip on the steam that affected the directions of my career.

Machine learning for phytopathology: from the molecular scale towards the network scale.

With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant-pathogen interactions and discuss the applications and advances of machine learning in plant-pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein-protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology.