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Detection and Diagnostics of Bacterial Plant Pathogens

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A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Protection and Biotic Interactions".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 36170

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Dr. Ye Xia

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Department of Plant Pathology, College of Food, Agricultural and Environmental Sciences, The Ohio State University, Columbus, OH 43210, USA
Interests: molecular biology; plant pathology; phytopathology; plant disease resistance
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Special Issue Information

Dear Colleagues,

Plant bacterial pathogens infect plants worldwide and cause destructive damages and losses to plant quality and yield. The early and quick detection and diagnostics of bacterial plant pathogens are critical for developing efficient approaches to control these harmful plant pathogens and diseases to reduce economic losses. However, there are challenges in the early and quick detection and diagnostics of some of these bacterial pathogens, such as in facing the low amount of bacterial pathogens without symptoms at the early stage, the emerging bacterial pathogens, and the simultaneous co-existence of multiple plant pathogens in plants and the environment. In recent decades, there have been significant improvements and developments in microscopy skills, next-generation sequencing techniques, molecular and genetic tools, omics approaches, etc. All these facilitate and improve our capability to detect and diagnose these harmful plant bacterial pathogens quicker and more efficiently. This Special Issue aims to address how different techniques can help us to detect, diagnose, and quantify these harmful plant bacterial pathogens with more sensitivity, accuracy, convenience, and feasible applications. Case studies of newly identified plant bacterial pathogens using different approaches will also be included in this Special Issue.

Dr. Ye Xia
Guest Editor

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Keywords

plant bacterial pathogen detection and diagnostics
plant bacterial pathogen quantification
omics approaches
molecular and genetic tools
fast and convenient approaches
plant bacterial pathogen–plant interaction

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Published Papers (6 papers)

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Research

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11 pages, 1055 KiB

Open AccessEditor’s ChoiceArticle

Genetic Divergence and Population Structure of Xanthomonas albilineans Strains Infecting Saccharum spp. Hybrid and Saccharum officinarum

by Zhong-Ting Hu, Mbuya Sylvain Ntambo, Jian-Ying Zhao, Talha Javed, Yang Shi, Hua-Ying Fu, Mei-Ting Huang and San-Ji Gao

Plants 2023, 12(10), 1937; https://doi.org/10.3390/plants12101937 - 9 May 2023

Cited by 6 | Viewed by 2198

Abstract

Leaf scald caused by Xanthomonas albilineans (Xa) is a major bacterial disease in sugarcane that represents a threat to the global sugar industry. Little is known about the population structure and genetic evolution of this pathogen. In this study, 39 Xa strains were collected from 6 provinces in China. Of these strains, 15 and 24 were isolated from Saccharum spp. hybrid and S. officinarum plants, respectively. Based on multilocus sequence analysis (MLSA), with five housekeeping genes, these strains were clustered into two distinct phylogenetic groups (I and II). Group I included 26 strains from 2 host plants, Saccharum spp. hybrid and S. officinarum collected from 6 provinces, while Group II consisted of 13 strains from S. officinarum plants in the Zhejiang province. Among the 39 Xa strains, nucleotide sequence identities from 5 housekeeping genes were: ABC (99.6–100%), gyrB (99.3–100%), rpoD (98.4–100%), atpD (97.0–100%), and glnA (97.6–100%). These strains were clustered into six groups (A–F), based on the rep-PCR fingerprinting, using primers for ERIC2, BOX A1R, and (GTG)5. UPGMA and PCoA analyses revealed that group A had the most strains (24), followed by group C with 11 strains, while there was 1 strain each in groups B and D–F. Neutral tests showed that the Xa population in S. officinarum had a trend toward population expansion. Selection pressure analysis showed purification selection on five concatenated housekeeping genes from all tested strains. Significant genetic differentiation and infrequent gene flow were found between two Xa populations hosted in Saccharum spp. hybrids and S. officinarum. Altogether, these results provide evidence of obvious genetic divergence and population structures among Xa strains from China. Full article

(This article belongs to the Special Issue Detection and Diagnostics of Bacterial Plant Pathogens)

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11 pages, 2693 KiB

Open AccessArticle

Development of a Specific Nested PCR Assay for the Detection of 16SrI Group Phytoplasmas Associated with Sisal Purple Leafroll Disease in Sisal Plants and Mealybugs

by Guihua Wang, Weihuai Wu, Shibei Tan, Yanqiong Liang, Chunping He, Helong Chen, Xing Huang and Kexian Yi

Plants 2022, 11(21), 2817; https://doi.org/10.3390/plants11212817 - 23 Oct 2022

Cited by 4 | Viewed by 2684

Abstract

Sisal purple leafroll disease (SPLD) is currently the most destructive disease affecting sisal in China, yet its aetiology remains unclear. In our previous research, it was verified to be associated with phytoplasmas, and nested PCR based on the 16S rRNA gene using universal primers R16mF2/R16mR1 followed by R16F2n/R16R2 was confirmed as the most effective molecular method for the detection of phytoplasmas associated with SPLD (SPLDaP). However, the method has a shortcoming of inaccuracy, for it could produce false positive results. To further manage the disease, accurate detection is needed. In this study, we developed a specific nested PCR assay using universal primers R16F2n/R16R2, followed by a set of primers designed on 16Sr gene sequences amplified from SPLDaP, nontarget bacteria from sisal plants, and other phytoplasma subgroups or groups. This established method is accurate, specific, and effective for detection of 16SrI group phytoplasma in sisal, and its sensitivity is up to 10 fg/μL of total DNA. It also minimized the false positive problem of nested PCR using universal primers R16mF2/R16mR1 followed by R16F2n/R16R2. This method was further used to verify the presence of phytoplasma in Dysmicoccusneobrevipes, and the results showed that D. neobrevipes could be infected by SPLDaP and thus could be a candidate for vector transmission assays. Full article

(This article belongs to the Special Issue Detection and Diagnostics of Bacterial Plant Pathogens)

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21 pages, 2485 KiB

Open AccessArticle

Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae

by Mafalda Reis-Pereira, Renan Tosin, Rui Martins, Filipe Neves dos Santos, Fernando Tavares and Mário Cunha

Plants 2022, 11(16), 2154; https://doi.org/10.3390/plants11162154 - 19 Aug 2022

Cited by 17 | Viewed by 3661

Abstract

Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV–VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325–1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis. Full article

(This article belongs to the Special Issue Detection and Diagnostics of Bacterial Plant Pathogens)

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Review

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27 pages, 5887 KiB

Open AccessReview

Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae

by Piao Yang, Lijing Zhao, Yu Gary Gao and Ye Xia

Plants 2023, 12(9), 1765; https://doi.org/10.3390/plants12091765 - 25 Apr 2023

Cited by 28 | Viewed by 11230

Abstract

Plant diseases caused by the pathogen Pseudomonas syringae are serious problems for various plant species worldwide. Accurate detection and diagnosis of P. syringae infections are critical for the effective management of these plant diseases. In this review, we summarize the current methods for the detection and diagnosis of P. syringae, including traditional techniques such as culture isolation and microscopy, and relatively newer techniques such as PCR and ELISA. It should be noted that each method has its advantages and disadvantages, and the choice of each method depends on the specific requirements, resources of each laboratory, and field settings. We also discuss the future trends in this field, such as the need for more sensitive and specific methods to detect the pathogens at low concentrations and the methods that can be used to diagnose P. syringae infections that are co-existing with other pathogens. Modern technologies such as genomics and proteomics could lead to the development of new methods of highly accurate detection and diagnosis based on the analysis of genetic and protein markers of the pathogens. Furthermore, using machine learning algorithms to analyze large data sets could yield new insights into the biology of P. syringae and novel diagnostic strategies. This review could enhance our understanding of P. syringae and help foster the development of more effective management techniques of the diseases caused by related pathogens. Full article

(This article belongs to the Special Issue Detection and Diagnostics of Bacterial Plant Pathogens)

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24 pages, 1433 KiB

Open AccessReview

Citrus Canker Pathogen, Its Mechanism of Infection, Eradication, and Impacts

by Esha Shahbaz, Mobeen Ali, Muhammad Shafiq, Muhammad Atiq, Mujahid Hussain, Rashad Mukhtar Balal, Ali Sarkhosh, Fernando Alferez, Saleha Sadiq and Muhammad Adnan Shahid

Plants 2023, 12(1), 123; https://doi.org/10.3390/plants12010123 - 26 Dec 2022

Cited by 30 | Viewed by 7468

Abstract

Citrus canker is a ravaging bacterial disease threatening citrus crops. Its major types are Asiatic Canker, Cancrosis B, and Cancrosis C, caused by Xanthomonas citri pv. citri (Xcc), Xanthomonas citri pv. aurantifolii pathotype-B (XauB), and pathotype-C (XauC), respectively. The bacterium enters its host through stomata and wounds, from which it invades the intercellular spaces in the apoplast. It produces erumpent corky necrotic lesions often surrounded by a chlorotic halo on the leaves, young stems, and fruits, which causes dark spots, defoliation, reduced photosynthetic rate, rupture of leaf epidermis, dieback, and premature fruit drop in severe cases. Its main pathogenicity determinant gene is pthA, whose variants are present in all citrus canker-causing pathogens. Countries where citrus canker is not endemic adopt different methods to prevent the introduction of the pathogen into the region, eradicate the pathogen, and minimize its dissemination, whereas endemic regions require an integrated management program to control the disease. The main aim of the present manuscript is to shed light on the pathogen profile, its mechanism of infection, and fruitful strategies for disease management. Although an adequate method to completely eradicate citrus canker has not been introduced so far, many new methods are under research to abate the disease. Full article

(This article belongs to the Special Issue Detection and Diagnostics of Bacterial Plant Pathogens)

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19 pages, 3321 KiB

Open AccessReview

A Pan-Global Study of Bacterial Leaf Spot of Chilli Caused by Xanthomonas spp.

by Desi Utami, Sarah Jade Meale and Anthony Joseph Young

Plants 2022, 11(17), 2291; https://doi.org/10.3390/plants11172291 - 1 Sep 2022

Cited by 14 | Viewed by 7398

Abstract

Bacterial Leaf Spot (BLS) is a serious bacterial disease of chilli (Capsicum spp.) caused by at least four different Xanthomonas biotypes: X. euvesicatoria pv. euvesicatoria, X. euvesicatoria pv. perforans, X. hortorum pv. gardneri, and X. vesicatoria. Symptoms include black lesions and yellow halos on the leaves and fruits, resulting in reports of up to 66% losses due to unsalable and damaged fruits. BLS pathogens are widely distributed in tropical and subtropical regions. Xanthomonas is able to survive in seeds and crop residues for short periods, leading to the infections in subsequent crops. The pathogen can be detected using several techniques, but largely via a combination of traditional and molecular approaches. Conventional detection is based on microscopic and culture observations, while a suite of Polymerase Chain Reaction (PCR) and Loop-Mediated Isothermal Amplification (LAMP) assays are available. Management of BLS is challenging due to the broad genetic diversity of the pathogens, a lack of resilient host resistance, and poor efficacy of chemical control. Some biological control agents have been reported, including bacteriophage deployment. Incorporating stable host resistance is a critical component in ongoing integrated management for BLS. This paper reviews the current status of BLS of chilli, including its distribution, pathogen profiles, diagnostic options, disease management, and the pursuit of plant resistance. Full article

(This article belongs to the Special Issue Detection and Diagnostics of Bacterial Plant Pathogens)

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