Detection Of Plant Pathogens Research Articles

Application of loop-mediated isothermal amplification (LAMP) in plant pathogen detection.

Plant diseases impact the production of all kinds of crops, resulting in significant economic losses worldwide. Timely and accurate detection of plant pathogens is crucial for surveillance and management of plant diseases. In recent years, loop-mediated isothermal amplification (LAMP) has become a popular method for pathogen detection and disease diagnosis due to the advantages of its simple instrument requirement and constant reaction temperature. In this review, we provide an overview of current research on LAMP, including the reaction system, design of primers, selection of target regions, visualization of amplicons, and application of LAMP on the detection of all major groups of plant pathogens. We also discuss plant pathogens for which LAMP is yet to be developed, potential improvements of plant disease diagnosis, and disadvantages that need to be considered.

Noninvasive Raman spectroscopy for the detection of rice bacterial leaf blight and bacterial leaf streak

Plant diseases pose significant threats to agricultural yields and are responsible for nearly 20 % of losses in total food production. Therefore, the rapid detection of plant pathogens is critically important for preventing the rapid development of plant diseases and minimizing crop damage. Raman spectroscopy (RS) has been shown to be effective for detecting living biological samples. Compared with traditional detection methods, RS is fast, sensitive, and non-destructive; it also does not require sample labeling. In this study, we used Laser tweezers Raman spectroscopy combined with convolutional neural networks to detect two closely related strains of bacteria, Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), exuded from bacteria-infected rice leaves. The accuracy of this technique was 97.5 %. For the application of RS in the field, we used the portable Raman spectrometer to detect mock-inoculated as well as Xoo- and Xoc-infected rice leaves at different disease courses. The identification accuracy via this technique was 87.02 % in the early stage, in which no obvious symptoms were apparent. This method also revealed spectral differences in rice leaves caused by the two bacteria, which could be leveraged for subsequent analysis of the molecular mechanism of infection. Our results indicate that RS is a promising approach for the early detection of bacterial diseases in rice in the field, as well as for in-depth single-cell analysis in laboratory settings.

Achieving precise dual detection: One-tube reverse transcription-recombinase aided amplification (RT-RAA) combined with lateral flow strip (LFS) assay for RNA and DNA target genes from pepper mild mottle virus and Colletotrichum species in crude plant samples

Ensuring the detection sensitivity of both RNA-derived and DNA-derived target genes in a single reaction has posed a significant challenge for on-site detection of plant pathogens. This challenge was addressed by developing a one-tube dual RT-RAA assay combined with LFS for the rapid on-site detection of pepper mild mottle virus (PMMoV) and four Colletotrichum species causing anthracnose in Solanaceous crops. By testing four different combinations of primer groups, two combinations were precisely adjusted within the dual RT-RAA system to balance amplification efficiency and maintain consistent levels of amplification in crude plant samples. Utilizing commercially accessible small-scale equipment and following a streamlined optimization strategy, the assay achieved a limit of detection of 0.32 copies/μL of target genes in the reaction. Importantly, it demonstrated no cross-reactivity with other plant pathogens, thereby affirming the high sensitivity and specificity of the developed dual RT-RAA-LFS detection assay. Moreover, the entire process took only 25 min from sample collection to the visible presentation of results. The assay was validated with 60 field samples and 10 seed samples, producing results consistent with reverse transcription quantitative polymerase chain reaction (RT-qPCR). Notably, it successfully detected PMMoV in systemic leaves without visible symptoms three days post-inoculation, underscoring its effectiveness in early disease detection. This streamlined strategy offers a valuable approach for rapid, low-cost, and highly sensitive on-site simultaneous detection of RNA genome-contained PMMoV and DNA genome-contained Colletotrichum species.

Simulated High Throughput Sequencing Datasets: A Crucial Tool for Validating Bioinformatic Pathogen Detection Pipelines.

The validation of diagnostic assays in plant pathogen detection is a critical area of research. It requires the use of both negative and positive controls containing a known quantity of the target pathogen, which are crucial elements when calculating analytical sensitivity and specificity, among other diagnostic performance metrics. High Throughput Sequencing (HTS) is a method that allows the simultaneous detection of a theoretically unlimited number of plant pathogens. However, accurately identifying the pathogen from HTS data is directly related to the bioinformatic pipeline utilized and its effectiveness at correctly assigning reads to their associated taxa. To this day, there is no consensus about the pipeline that should be used to detect the pathogens in HTS data, and results often undergo review and scientific evaluation. It is, therefore, imperative to establish HTS resources tailored for evaluating the performance of bioinformatic pipelines utilized in plant pathogen detection. Standardized artificial HTS datasets can be used as a benchmark by allowing users to test their pipelines for various pathogen infection scenarios, some of the most prevalent being multiple infections, low titer pathogens, mutations, and new strains, among others. Having these artificial HTS datasets in the hands of HTS diagnostic assay validators can help resolve challenges encountered when implementing bioinformatics pipelines for routine pathogen detection. Offering these purely artificial HTS datasets as benchmarking tools will significantly advance research on plant pathogen detection using HTS and enable a more robust and standardized evaluation of the bioinformatic methods, thereby enhancing the field of plant pathogen detection.

New Approaches to Plant Pathogen Detection and Disease Diagnosis.

Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.

Emergence of Microfluidic Platforms as Point of Care Diagnostics for the Detection of Plant and Foodborne Pathogens

Emergence of Microfluidic Platforms as Point of Care Diagnostics for the Detection of Plant and Foodborne Pathogens

Advances in plant pathogen detection: integrating recombinase polymerase amplification with CRISPR/Cas systems.

Plant pathogens are causing substantial economic losses and thus became a significant threat to global agriculture. Effective and timely detection methods are prerequisite for combating the damages caused by the plant pathogens. In the realm of plant pathogen detection, the isothermal amplification techniques, e.g., recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP), have emerged as a fast, precise, and most sensitive alternative to conventional PCR but they often comprise high rates of non-specific amplification and operational complexity. In recent advancements, clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated nuclease Cas systems, particularly Cas12, have emerged as powerful tools for highly sensitive, specific, and rapid pathogen detection. Exploiting the collateral activities of Cas12, which selectively cleaves single-stranded DNA (ssDNA), novel detection platforms have been developed. The mechanism employs the formation of a triple complex molecule comprising guide RNA, Cas12 enzyme, and the substrate target nucleotide sequence. Upon recognition of the target, Cas12 indiscriminately cleaves the DNA strand, leading to the release of fluorescence from the cleaved ssDNA reporter. Integration of isothermal amplification methods with CRISPR/Cas12 enables one-step detection assays, facilitating rapid pathogen identification within 30min at a single temperature. This integrated RPA-CRISPR/Cas12a approach eliminates the need for RNA extraction and cDNA conversion, allowing direct use of crude plant sap as a template. With an affordable fluorescence visualization system, this portable method achieves 100-fold greater sensitivity than conventional techniques. This review summarizes recent advances in RPA-CRISPR/Cas12a for detecting plant pathogens, covering primer design, field-level portability, and enhanced sensitivity.

Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis

Plant diseases pose a significant threat to plant and crop health, leading to reduced yields and economic losses. The traditional methods for diagnosing plant diseases are often invasive and time-consuming and may not always provide accurate results. In recent years, there has been growing interest in utilizing Raman microscopy as a non-invasive and label-free technique for plant disease diagnosis. Raman microscopy is a powerful analytical tool that can provide detailed molecular information about samples by analyzing the scattered light from a laser beam. This technique has the potential to revolutionize plant disease diagnosis by offering rapid and accurate detection of various plant pathogens, including bacteria and fungi. One of the key advantages of Raman microscopy/spectroscopy is its ability to provide real-time and in situ analyses of plant samples. By analyzing the unique spectral fingerprints of different pathogens, researchers can quickly identify the presence of specific diseases without the need for complex sample preparation or invasive procedures. This article discusses the development of a Raman microspectroscopy system for disease diagnosis that can accurately detect and identify various plant pathogens, such as bacteria and fungi.

Combined ultra‐microelectrode: Exploring new potentials for in vivo/in situ ascorbic acid electroanalysis

AbstractMiniaturized and portable analytical tools show promise for sophisticated analysis, particularly in biological systems such as fruits, and they are suitable for advanced agriculture and related food industries. In this study, we developed combined ultra‐microelectrodes (UME) by modifying a microscale carbon fiber electrode (33 μm) coated with an Au nano‐film in a micropipette‐tip system. The proposed UME@Au exhibited a linear response to AC concentrations ranging from 30 to 1400 μM, with a 16 μM limit of detection. It demonstrated the ability to perform in vivo‐in vitro AC analysis in micro‐zones and volumes, such as different points of fruit tissue (Such as lemon) and within the body of a living plant (Such as Cactus arms and trunk), serving as a tiny implanted probe.In the first part of our study, we analyzed AC levels in lemon tissue directly. Our measurements revealed that AC levels are distributed heterogeneously in a single fruit. Additionally, stored AC levels depend on the color of the lemon (yellow ones have higher levels than the green ones). Furthermore, the UME was applied to control AC levels in different storage conditions, including opened containers, airtight containers, with and without exposing daylight, etc.In the second part, the UME@Au was utilized as an implanted sensor for in vivo analysis of AC in different parts of the cactus, recognized as a source of AC. No sample preparation is needed with minimum damage. The implanted microsensor could perform electroanalysis inside the live plant and stored parenchyma cells, etc. Notably, our results showed that AC levels are higher in the younger arms compared to the older ones, and so on.Based on our findings, the miniaturized, small, cheap, user‐friendly electrode demonstrated many capabilities, such as being implantable, having satisfactory stability, and not requiring sample preparations for analysis. It can open up a new window for micro‐electroanalysis in food and analytical plant sciences. We predict that this microscale platform can be modified and used for bioanalysis of other (bio)targets, such as vitamins, ions, and even the detection of plant pathogens in plants and crops directly. This involvement in the smart and modern farming industry is anticipated in the near future.

Plant pathogen detection: exploring modern techniques and future directions

Plant pathogen detection: exploring modern techniques and future directions

Applications of Surface Plasmon Resonance (SPR) to the Study of Diverse Protein-Ligand Interactions.

Functional characterization of enzymes/proteins requires determination of the binding affinity of small molecules or other biomolecules with the target proteins. Several available techniques, such as proteomics and drug discovery strategies, require a precise and high-throughput assay for rapid and reliable screening of potential candidates for further testing. Surface plasmon resonance (SPR), a well-established label-free technique, directly measures biomolecular affinities. SPR assays require immobilization of one interacting component (ligand) on a conductive metal (mostly gold or silver) and a continuous flow of solution containing potential binding partner (analyte) across the surface. The SPR phenomenon occurs when polarized light excites the electrons at the interface of the metal and the dielectric medium to generate electromagnetic waves that propagate parallel to the surface. Changes in the refractive index due to interaction between the ligand and analyte are measured by detecting the reflected light, providing real-time data on kinetics and specificity. A prominent use of SPR is identifying compounds in crude plant extracts that bind to specific molecules. Procedures that utilize SPR are becoming increasingly applicable outside the laboratory setting, and SPR imaging and localized SPR (LSPR) are cheaper and more portable alternative for in situ detection of plant or mammalian pathogens and drug discovery studies. LSPR, in particular, has the advantage of direct attachment to test tissues in live-plant studies. Here, we describe three protocols utilizing SPR-based assays for precise analysis of protein-ligand interactions. © 2024 Wiley Periodicals LLC. Basic Protocol 1: SPR comparison of binding affinities of viral reverse transcriptase polymorphisms Basic Protocol 2: SPR screening of crude plant extract for protein-binding agents Basic Protocol 3: Localized SPR-based antigen detection using antibody-conjugated gold nanoparticles.

Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges.

Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future.

Amelioration in nanobiosensors for the control of plant diseases: current status and future challenges

The increase in global population has had a tremendous impact on sustainable agri-food practices. With the growth in world population, various modern technologies are being utilized that more often result in the opening of tremendous opportunities in the agriculture and food sectors. Nanotechnology is used in agri-food sectors for a variety of purposes, including enhancing flavor, pest/pathogen diagnosis, production, processing, storage, packaging, and transportation of agricultural products. Plant pathogenic microorganisms including bacteria, viruses, fungi, and nematodes have a significant impact on the global economy. In particular, advances in nanotechnology, including nanobiosensors, have been used in the detection of plant diseases and pathogens, the evaluation and examination of infections caused by microorganisms, the management of diseases and, thus, the promotion of food security. Apart from the management of plant diseases, nanobiosensors offer better opportunities for sustainable agri-food production by controlling physical, chemical, and biological processes, thus improving food safety and the agricultural economy. This review outlines the application of nano-integrated nanobiosensors for better agricultural and food practices.

A simple graphene oxide-based DNA purification strategy for plant pathogen detection.

The on-site molecular detection of plant pathogens is particularly important for the development of sustainable agriculture. Extracting DNA from plant tissues, microbes or coexisting environments is complex, labor-intensive and time-consuming. To facilitate this process, we propose a DNA purification strategy based on graphene oxide (GO). The excellent adsorption ability of GO was verified by visualizing changes in its microscopic surface and macroscopic mixture. To further optimize the DNA purification, we determined the optimal GO concentration and treatment time at 95 °C (2 mg mL-1 and 2 min, respectively). We confirmed that our strategy is effective on plant tissues and various microorganisms, and that the obtained DNA can be directly used for polymerase chain reaction amplification. Combining the proposed GO-based DNA purification method with the loop-mediated isothermal amplification method is superior, in terms of the required steps, time, cost and detection effect, to the cetyltrimethylammonium bromide method and a commercial kit for detecting plant pathogens. We present a feasible, rapid, simple and low-cost DNA purification method with high practical value for scientific applications in plant pathogen detection. This strategy can also provide important technical support for future research on plant-microbial microenvironments. © 2024 Society of Chemical Industry.

Detection and Monitoring of Plant Pathogens and Pests

Detection and Monitoring of Plant Pathogens and Pests

Early Detection of Fusarium Basal Rot Infection in Onions and Shallots Based on VOC Profiles Analysis.

Gas chromatography ion-mobility spectrometry (GC-IMS) technology is drawing increasing attention due to its high sensitivity, low drift, and capability for the identification of compounds. The noninvasive detection of plant pests and pathogens is an application area well suited to this technology. In this work, we employed GC-IMS technology for early detection of Fusarium basal rot in brown onion, red onion, and shallot bulbs and for tracking disease progression during storage. The volatile profiles of the infected and healthy control bulbs were characterized using GC-IMS and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS data combined with principal component analysis and supervised methods provided discrimination between infected and healthy control bulbs as early as 1 day after incubation with the pathogen, classification regarding the proportion of infected to healthy bulbs in a sample, and prediction of the infection's duration with an average R2 = 0.92. Furthermore, GC-TOF-MS revealed several compounds, mostly sulfides and disulfides, that could be uniquely related to Fusarium basal rot infection.

Low-High-Low Rotationally Pulse-Actuated Serial Dissolvable Film Valves Applied to Solid Phase Extraction and LAMP Isothermal Amplification for Plant Pathogen Detection on a Lab-on-a-Disc.

The ability of the centrifugal Lab-on-a-Disc (LoaD) platform to closely mimic the "on bench" liquid handling steps (laboratory unit operations (LUOs)) such as metering, mixing, and aliquoting supports on-disc automation of bioassay without the need for extensive biological optimization. Thus, well-established bioassays, normally conducted manually using pipettes or using liquid handling robots, can be relatively easily automated in self-contained microfluidic chips suitable for use in point-of-care or point-of-use settings. The LoaD's ease of automation is largely dependent on valves that can control liquid movement on the rotating disc. The optimum valving strategy for a true low-cost and portable device is rotationally actuated valves, which are actuated by changes in the disc spin-speed. However, due to tolerances in disc manufacturing and variations in reagent properties, most of these valving technologies have inherent variation in their actuation spin-speed. Most valves are actuated through stepped increases in disc spin-speed until the motor reaches its maximum speed (rarely more than 6000 rpm). These manufacturing tolerances combined with this "analogue" mechanism of valve actuation limits the number of LUOs that can be placed on-disc. In this work, we present a novel valving mechanism called low-high-low serial dissolvable film (DF) valves. In these valves, a DF membrane is placed in a dead-end pneumatic chamber. Below an actuation spin-speed, the trapped air prevents liquid wetting and dissolving the membrane. Above this spin-speed, the liquid will enter and wet the DF and open the valve. However, as DFs take ∼40 s to dissolve, the membrane can be wetted, and the disc spin-speed reduced before the film opens. Thus, by placing valves in a series, we can govern on which "digital pulse" in spin-speeding a reagent is released; a reservoir with one serial valve will open on the first pulse, a reservoir with two serial valves on the second, and so on. This "digital" flow control mechanism allows the automation of complex assays with high reliability. In this work, we first describe the operation of the valves, outline the theoretical basis for their operation, and support this analysis with an experiment. Next, we demonstrate how these valves can be used to automate the solid-phase extraction of DNA on on-disc LAMP amplification for applications in plant pathogen detection. The disc was successfully used to extract and detect, from a sample lysed off-disc, DNA indicating the presence of thermally inactivated Clavibacter michiganensis ssp. michiganensis (Cmm), a bacterial pathogen on tomato leaf samples.

Prediction of Plant Leaf Diseases using Drone and Image Processing Techniques

Plant diseases are one of the major threats to global food production. Efficient monitoring and detection of plant pathogens are instrumental in restricting and effectively managing the spread of the disease and reducing the cost of pesticides. Traditional, molecular, and serological methods that are widely used for plant disease detection are often ineffective if not applied during the initial stages of pathogenesis, when no or very weak symptoms appear. Moreover, they are almost useless in acquiring spatialized diagnostic results on plant diseases. On the other hand, remote sensing (RS) techniques utilizing drones are very effective for the rapid identification of plant diseases in their early stages. Currently, drones, play a pivotal role in the monitoring of plant pathogen spread, detection, and diagnosis to ensure crops’ health status. The advantages of drone technology include high spatial resolution (as several sensors are carried aboard), high efficiency, usage flexibility, and more significantly, quick detection of plant diseases across a large area with low cost, reliability, and provision of high-resolution data. Drone technology employs an automated procedure that begins with gathering images of diseased plants using various sensors and cameras. After extracting features, image processing approaches use the appropriate traditional machine learning or deep learning algorithms. Features are extracted from images of leaves using edge detection and histogram equalization methods. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Drones may be able to provide early warning of plant diseases, allowing farmers to prevent costly crop failures

Emerging trends in Maldi-Tof MS: transforming plant pathogen detection and disease management

Emerging trends in Maldi-Tof MS: transforming plant pathogen detection and disease management

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

BackgroundDetection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen.ResultsWe have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license.ConclusionsPhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe.