CHAPTER 10: The Disease Cycle of Fire Blight

Zwet, T. van der; Orolaza-Halbrendt, N.; Zeller, W.

doi:10.1094/9780890544839.012

Cited by 11 publications

(8 citation statements)

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“…Several disease severity rating systems are used to quantify the severity of fire blight infection (Van Der Zwet and Keil, 1979). These ratings are based on proportion of current season’s growth that was blighted or healthy, percentage of shoots that were infected per tree (i.e., incidence), and the age of wood that a lesion progressed into (i.e., infected).…”

Section: Methodsmentioning

confidence: 99%

High-Throughput Phenotyping of Fire Blight Disease Symptoms Using Sensing Techniques in Apple

et al. 2019

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Washington State produces about 70% of total fresh market apples in the United States. One of the primary goals of apple breeding programs is the development of new cultivars resistant to devastating diseases such as fire blight. The overall objective of this study was to investigate high-throughput phenotyping techniques to evaluate fire blight disease symptoms in apple trees. In this regard, normalized stomatal conductance data acquired using a portable photosynthetic system, image data collected using RGB and multispectral cameras, and visible-near infrared spectral reflectance acquired using a hyperspectral sensing system, were independently evaluated to estimate the progression of fire blight infection in young apple trees. Sensors with ranging complexity – from simple RGB to multispectral imaging to hyperspectral system – were evaluated to select the most accurate technique for the assessment of fire blight disease symptoms. The proximal multispectral images and visible-near infrared spectral reflectance data were collected in two field seasons (2016, 2017); while, proximal side-view RGB images and multispectral images using unmanned aerial systems were collected in 2017. The normalized stomatal conductance data was correlated with disease severity rating ( r = 0.51, P < 0.05). The features extracted from RGB images (e.g., maximum length of senesced leaves, area of senesced leaves, ratio between senesced and healthy leaf area) and multispectral images (e.g., vegetation indices) also demonstrated potential in evaluation of disease rating (| r | > 0.35, P < 0.05). The average classification accuracy achieved using visible-near infrared spectral reflectance data during the classification of susceptible from symptomless groups ranged between 71 and 93% using partial least square regression and quadratic support vector machine. In addition, fire blight disease ratings were compared with normalized difference spectral indices (NDSIs) that were generated from visible-near infrared reflectance spectra. The selected spectral bands in the range 710–2,340 nm used for computing NDSIs showed consistently higher correlation with disease severity rating than data acquired from RGB and multispectral imaging sensors across multiple seasons. In summary, these specific spectral bands can be used for evaluating fire blight disease severity in apple breeding programs and potentially as early fire blight disease detection tool to assist in production systems.

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Section: Methodsmentioning

confidence: 99%

High-Throughput Phenotyping of Fire Blight Disease Symptoms Using Sensing Techniques in Apple

et al. 2019

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“…The pathogen continues to colonize the stigma until high humidity or a wetting event washes cells into the nectarthodes located in the floral cup [8]. This allows the bacteria to enter the tree and spread throughout the vasculature of the host plant, leading to wilt, necrosis, and potential death of the entire tree [9]. Temporal factors and weather conditions can make effective control with biological control agents or biologicals very challenging.…”

Section: Introductionmentioning

confidence: 99%

Population Dynamics between Erwinia amylovora, Pantoea agglomerans and Bacteriophages: Exploiting Synergy and Competition to Improve Phage Cocktail Efficacy

et al. 2020

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Bacteriophages are viruses capable of recognizing with high specificity, propagating inside of, and destroying their bacterial hosts. The phage lytic life cycle makes phages attractive as tools to selectively kill pathogenic bacteria with minimal impact on the surrounding microbiome. To effectively harness the potential of phages in therapy, it is critical to understand the phage–host dynamics and how these interactions can change in complex populations. Our model examined the interactions between the plant pathogen Erwinia amylovora, the antagonistic epiphyte Pantoea agglomerans, and the bacteriophages that infect and kill both species. P. agglomerans strains are used as a phage carrier; their role is to deliver and propagate the bacteriophages on the plant surface prior to the arrival of the pathogen. Using liquid cultures, the populations of the pathogen, carrier, and phages were tracked over time with quantitative real-time PCR. The jumbo Myoviridae phage ϕEa35-70 synergized with both the Myoviridae ϕEa21-4 and Podoviridae ϕEa46-1-A1 and was most effective in combination at reducing E. amylovora growth over 24 h. Phage ϕEa35-70, however, also reduced the growth of P. agglomerans. Phage cocktails of ϕEa21-4, ϕEa46-1-A1, and ϕEa35-70 at multiplicities of infections (MOIs) of 10, 1, and 0.01, respectively, no longer inhibited growth of P. agglomerans. When this cocktail was grown with P. agglomerans for 8 h prior to pathogen introduction, pathogen growth was reduced by over four log units over 24 h. These findings present a novel approach to study complex phage–host dynamics that can be exploited to create more effective phage-based therapies.

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“…Currently, the worldwide production of apples is dominated by a few cultivars [9], which are all more or less susceptible to fire blight. The breeding of cultivars that are both competitive in the market and resistant to fire blight is a challenge, since strong resistances are mainly found in wild Malus species [3, 10, 11], and their use in breeding requires several generations of pseudo-backcrosses. An understanding of the genetics of fire blight resistance is a prerequisite for target-oriented resistance breeding; to date several quantitative trait loci (QTLs) conferring various degrees of resistance to fire blight in Malus have been reported.…”

Section: Introductionmentioning

confidence: 99%

Mapping of fire blight resistance in Malus ×robusta 5 flowers following artificial inoculation

et al. 2019

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Background: Although the most common path of infection for fire blight, a severe bacterial disease on apple, is via host plant flowers, quantitative trait loci (QTLs) for fire blight resistance to date have exclusively been mapped following shoot inoculation. It is not known whether the same mechanism underlies flower and shoot resistance. Results: We report the detection of a fire blight resistance QTL following independent artificial inoculation of flowers and shoots on two F1 segregating populations derived from crossing resistant Malus ×robusta 5 (Mr5) with susceptible 'Idared' and 'Royal Gala' in experimental orchards in Germany and New Zealand, respectively. QTL mapping of phenotypic datasets from artificial flower inoculation of the 'Idared' × Mr5 population with Erwinia amylovora over several years, and of the 'Royal Gala' × Mr5 population in a single year, revealed a single major QTL controlling floral fire blight resistance on linkage group 3 (LG3) of Mr5. This QTL corresponds to the QTL on LG3 reported previously for the 'Idared' × Mr5 and an 'M9' × Mr5 population following shoot inoculation in the glasshouse. Interval mapping of phenotypic data from shoot inoculations of subsets from both flower resistance populations reconfirmed that the resistance QTL is in the same position on LG3 of Mr5 as that for flower inoculation. These results provide strong evidence that fire blight resistance in Mr5 is controlled by a major QTL on LG3, independently of the mode of infection, rootstock and environment. Conclusions: This study demonstrates for the first time that resistance to fire blight caused by Erwinia amylovora is independent of the mode of inoculation at least in Malus ×robusta 5.

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CHAPTER 10: The Disease Cycle of Fire Blight

Cited by 11 publications

References 0 publications

High-Throughput Phenotyping of Fire Blight Disease Symptoms Using Sensing Techniques in Apple

High-Throughput Phenotyping of Fire Blight Disease Symptoms Using Sensing Techniques in Apple

Population Dynamics between Erwinia amylovora, Pantoea agglomerans and Bacteriophages: Exploiting Synergy and Competition to Improve Phage Cocktail Efficacy

Mapping of fire blight resistance in Malus ×robusta 5 flowers following artificial inoculation

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