Innovative tools for detection of plant pathogenic viruses and bacteria

Serological and Molecular Detection of Citrus Tristeza Virus: A Review

Citrus tristeza virus (CTV) is a globally pervasive and economically significant virus that negatively impacts citrus trees, leading to substantial reductions in fruit yield. CTV occurs within the phloem of infected plants, causing a range of disease phenotypes, such as stem pitting (SP), quick decline (QD), and other detrimental diseases. Research on CTV is challenging due to the large size of its RNA genome and the diversity of CTV populations. Comparative genomic analyses have uncovered genetic diversity in multiple regions of CTV isolates' genomes, facilitating the classification of the virus into distinct genotypes. Despite these challenges, notable advancements have been made in identifying and controlling CTV strains through serological and molecular methods. The following review concentrates on the techniques of nucleic acid identification and serological analysis for various CTV isolates, assisting in the comparison and evaluation of various detection methods, which are crucial for the effective management of CTV diseases, and so contributes to the innovation and development of CTV detection methods.

Review - Plant nutritional status analysis employing the visible and near-infrared spectroscopy spectral sensor

Experiments demonstrated that visible and near-infrared (Vis-NIR) spectroscopy is a highly reliable tool for determining the nutritional status of plants. Although numerous studies on various kinds of plants have been conducted, there are only a few summaries of the research findings regarding the absorbance bands in the visible and near-infrared region and how they relate to the nutritional status of plants. This article will discuss the application of Vis-NIR spectroscopy for monitoring the nutrient conditions of plants, with a particular emphasis on three major components required by plants, namely nitrogen (N), phosphorus (P), and potassium (K), or NPK. Each section discussed different topics, for instance, the essential nutrients needed by plants, the application of Vis-NIR spectroscopy in nutrient status analysis, chemometrics tools, and absorbance bands related to the nutrient status, respectively. Deduction made concluded that factors affecting the plant's structure are contributed by several circumstances like the age of leaves, concentration of pigments, and water content. These factors are intertwined, strongly correlated, and can be observed in the visible and near-infrared regions. While the visible region is commonly utilised for nutritional analysis in plants, the literature review performed in this paper shows that the near-infrared region as well contains valuable information about the plant's nutritional status. A few wavelengths related to the direct estimation of nutrients in this review explained that information on nutrients can be linked with chlorophyll and water absorption bands such that N and P are the components of chlorophyll and protein; on the other hand, K exists in the form of cationic carbohydrates which are sensitive to water region.

Imprinted Polymers on the Route to Plastibodies for Biomacromolecules (MIPs), Viruses (VIPs), and Cells (CIPs)

Around 30% of the scientific papers published on imprinted polymers describe the recognition of proteins, nucleic acids, viruses, and cells. The straightforward synthesis from only one up to six functional monomers and the simple integration into a sensor are significant advantages as compared with enzymes or antibodies. Furthermore, they can be synthesized against toxic substances and structures of low immunogenicity and allow multi-analyte measurements via multi-template synthesis. The affinity is sufficiently high for protein biomarkers, DNA, viruses, and cells. However, the cross-reactivity of highly abundant proteins is still a challenge.

Current and emerging trends in techniques for plant pathogen detection

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

Diagnosis of Bacterial Plant Diseases via a Nitroreductase-Activated Fluorescent Sensor

Plant diseases caused by bacteria have become one of the serious problems that threaten human food security, which led to the remarkable reduction of agricultural yields and economic loss. Nitroreductase (NTR), as an important biomarker, is highly expressed in bacteria, and the level of NTR is closely related to the progression of pathogen infection. Therefore, the design of small-molecule fluorescent sensors targeting NTR is of great significance for the detection and diagnosis of plant pathogenic bacteria. In this study, a new fluorescent sensor targeting NTR was discovered and then successfully applied to the imaging of zebrafish and pathogenic bacteria. Most importantly, the developed sensor achieved the real-time diagnosis of Brassica napus L. infected with bacteria, which provides a promising tool for examining the temporal and spatial infection of plant pathogens in precision agriculture.

Selection of potential reference genes for RT-qPCR in the plant pathogenic fungus Colletotrichum fructicola

Colletotrichum is widespread, and these pathogenic fungi can cause various plant diseases. Studies have shown that Colletotrichum fructicola cause oil-tea (Camellia oleifera) anthracnose and is widely distributed as a dominant fungus in all Ca. oleifera-producing regions. Real-time quantitative PCR(RT-qPCR) is considered the most reliable technique for simultaneously measuring relative gene expression levels in different tissues. Target genes are typically quantified using RT-qPCR to explore gene function, and reliable RT-qPCR results require data normalization using stable reference genes. No studies have reported a suitable reference gene in C. fructicola. This study has eight candidate reference genes (CfCk, CfRpp, CfUce, CfRrp, CfAdrh, CfDd, CfAct, and CfTub) which were selected from C. fructicola-Ca. oleifera transcriptome data and evaluated and sequenced using geNorm, NormFinder, and BestKeeper algorithms. The results showed that CfRrp had better stability in C. fructicola, both during the growth of pure pathogenic fungi and during the invasion of different oil-tea leaves. After normalization with CfRrp, the differentially expressed target genes were similar to the transcriptome. Our work provides suitable reference genes for future studies to quantify target gene expression levels in C. fructicola.

Nanotechnological Interventions in Agriculture

Agriculture is an important sector that plays an important role in providing food to both humans and animals. In addition, this sector plays an important role in the world economy. Changes in climatic conditions and biotic and abiotic stresses cause significant damage to agricultural production around the world. Therefore, the development of sustainable agricultural techniques is becoming increasingly important keeping in view the growing population and its demands. Nanotechnology provides important tools to different industrial sectors, and nowadays, the use of nanotechnology is focused on achieving a sustainable agricultural system. Great attention has been given to the development and optimization of nanomaterials and their application in the agriculture sector to improve plant growth and development, plant health and protection and overall performance in terms of morphological and physiological activities. The present communication provides up-to-date information on nanotechnological interventions in the agriculture sector. The present review deals with nanoparticles, their types and the role of nanotechnology in plant growth, development, pathogen detection and crop protection, its role in the delivery of genetic material, plant growth regulators and agrochemicals and its role in genetic engineering. Moreover, the role of nanotechnology in stress management is also discussed. Our aim in this review is to aid researchers to learn quickly how to use plant nanotechnology for improving agricultural production.

Establishment and Application of a Multiplex PCR Assay for the Rapid Detection of Rhizoctonia solani Anastomosis Group (AG)-3PT, the Pathogen Causing Potato Black Scurf and Stem Canker

Rhizoctonia solani anastomosis group 3 (AG-3) is the main causative agent of the soil-borne disease known as potato black scurf, which poses a huge threat to potato production. Rapid and accurate identification of R. solani AG-3 isolates in soil and potato seed tubers prior to planting is essential for good production. In this study, a multiplex PCR assay was established for the detection of R. solani AG-3. Two pairs of target-specific primers were designed from sequences for endopolygalacturonase and pyridoxine biosynthesis genes downloaded from GenBank. The main factors influencing PCR amplification, such as annealing temperature and primer concentration, were optimized. Results show that the proposed multiplex PCR assay is highly sensitive and specific for the target genes in the pathogen even when the DNA concentration is reduced to 20 fg/μL. The resulting calibration plot shows a linear relationship between electrophoretic band peaks and genomic DNA concentration (R² = 0.98). The primer specificity was confirmed by applying them to other R. solani AG groups and plant pathogen species on which no amplicons were produced. Using the primers, we successfully detected small amounts of R. solani AG-3 present in soil and potato tuber samples. Taken together, the detection assay developed in this study has high sensitivity, strong specificity, and accuracy and can be used to detect and identify soil and potato seed tubers infected with Rhizoctonia solani AG-3.

Discrepancies in Serology-Based and Nucleic Acid-Based Detection and Quantitation of Tomato Spotted Wilt Orthotospovirus in Leaf and Root Tissues from Symptomatic and Asymptomatic Peanut Plants

Thrips-transmitted tomato spotted wilt orthotospovirus (TSWV) causes spotted wilt disease in peanuts. A serological test (DAS-ELISA) is often used to detect TSWV in peanut leaf samples. However, in a few studies, DAS-ELISA detected more TSWV infection in root than leaf samples. It was not clear if the increased detection was due to increased TSWV accumulation in root tissue or merely an overestimation. Additionally, it was unclear if TSWV detection in asymptomatic plants would be affected by the detection technique. TSWV infection in leaf and root tissue from symptomatic and asymptomatic plants was compared via DAS-ELISA, RT-PCR, and RT-qPCR. TSWV incidence did not vary by DAS-ELISA, RT-PCR, and RT-qPCR in leaf and root samples of symptomatic plants or in leaf samples of asymptomatic plants. In contrast, significantly more TSWV infection and virus load were detected in root samples of asymptomatic plants via DAS-ELISA than other techniques suggesting that DAS-ELISA overestimated TSWV incidence and load. TSWV loads from symptomatic plants via RT-qPCR were higher in leaf than root samples, while TSWV loads in leaf and root samples from asymptomatic plants were not different but were lower than those in symptomatic plants. These findings suggested that peanut tissue type and detection technique could affect accurate TSWV detection and/or quantitation.

The Potential Use of Isothermal Amplification Assays for In-Field Diagnostics of Plant Pathogens

Rapid, sensitive, and timely diagnostics are essential for protecting plants from pathogens. Commonly, PCR techniques are used in laboratories for highly sensitive detection of DNA/RNA from viral, viroid, bacterial, and fungal pathogens of plants. However, using PCR-based methods for in-field diagnostics is a challenge and sometimes nearly impossible. With the advent of isothermal amplification methods, which provide amplification of nucleic acids at a certain temperature and do not require thermocyclic equipment, going beyond the laboratory has become a reality for molecular diagnostics. The amplification stage ceases to be limited by time and instruments. Challenges to solve involve finding suitable approaches for rapid and user-friendly plant preparation and detection of amplicons after amplification. Here, we summarize approaches for in-field diagnostics of phytopathogens based on different types of isothermal amplification and discuss their advantages and disadvantages. In this review, we consider a combination of isothermal amplification methods with extraction and detection methods compatible with in-field phytodiagnostics. Molecular diagnostics in out-of-lab conditions are of particular importance for protecting against viral, bacterial, and fungal phytopathogens in order to quickly prevent and control the spread of disease. We believe that the development of rapid, sensitive, and equipment-free nucleic acid detection methods is the future of phytodiagnostics, and its benefits are already visible.

Centenary of Soil and Air Borne Wheat Karnal Bunt Disease Research: A Review

Karnal bunt (KB) of wheat (Triticum aestivum L.), known as partial bunt has its origin in Karnal, India and is caused by Tilletia indica (Ti). Its incidence had grown drastically since late 1960s from northwestern India to northern India in early 1970s. It is a seed, air and soil borne pathogen mainly affecting common wheat, durum wheat, triticale and other related species. The seeds become inedible, inviable and infertile with the precedence of trimethylamine secreted by teliospores in the infected seeds. Initially the causal pathogen was named Tilletia indica but was later renamed Neovossia indica. The black powdered smelly spores remain viable for years in soil, wheat straw and farmyard manure as primary sources of inoculum. The losses reported were as high as 40% in India and also the cumulative reduction of national farm income in USA was USD 5.3 billion due to KB. The present review utilizes information from literature of the past 100 years, since 1909, to provide a comprehensive and updated understanding of KB, its causal pathogen, biology, epidemiology, pathogenesis, etc. Next generation sequencing (NGS) is gaining popularity in revolutionizing KB genomics for understanding and improving agronomic traits like yield, disease tolerance and disease resistance. Genetic resistance is the best way to manage KB, which may be achieved through detection of genes/quantitative trait loci (QTLs). The genome-wide association studies can be applied to reveal the association mapping panel for understanding and obtaining the KB resistance locus on the wheat genome, which can be crossed with elite wheat cultivars globally for a diverse wheat breeding program. The review discusses the current NGS-based genomic studies, assembly, annotations, resistant QTLs, GWAS, technology landscape of diagnostics and management of KB. The compiled exhaustive information can be beneficial to the wheat breeders for better understanding of incidence of disease in endeavor of quality production of the crop.

Pre-Harvest and Post-Harvest Techniques for Plant Disease Detections

As the agriculture industry is growing fast, many efforts are introduced to ensure a high quality of produce. Diseases and defects found in plants and crops affect greatly the agriculture industry. Hence, many techniques and technologies have been developed to help solve or reduce the impact of plant diseases. Imagining analysis tools and gas sensors are becoming more frequently integrated into smart systems for plant disease detection. Many disease detection systems incorporate imaging analysis tools and VOC (Volatile Organic Compound) profiling techniques to detect early symptoms of diseases and defects of plants, fruits, and vegetative produce. These disease detection techniques can be further categorized into two main groups: preharvest disease detection and postharvest disease detection techniques. This paper aims to introduce the available disease detection techniques and to compare them with the latest innovative smart systems that feature visible imaging, hyperspectral imaging, and VOC profiling. In addition, this paper considers the efforts to automate imaging techniques to help accelerate the disease detection process. Different approaches are analyzed and compared in terms of work environment, automation, implementation, and accuracy of disease identification along with the future evolution perspective in this field.

Advanced DNA Detection via Multispectral Plasmonic Metasurfaces

We propose and demonstrate a sensing platform based on plasmonic metasurfaces for the detection of very low concentrations of deoxyribo-nucleic acid (DNA) fragments. The platform relies on surface-enhanced infrared absorption spectroscopy, implemented via a multispectral metasurface. Specifically, different regions (“pixels”) are engineered so as to separately cover the medium-infrared range of the electromagnetic spectrum extending from the functional-groups to the fingerprint region of a single analyte. In conjunction with a suitable bio-functionalization, this enables univocal and label-free recognition of specific molecules. For experimental validation, we fabricate a large-area gold metasurface on a silicon chip, and functionalize it with a recognition layer of peptide nucleic acid (PNA). Our experimental results indicate the possibility to detect complementary DNA fragments in concentrations as low as 50 fM, i.e., well below the value attained by standard methods, with additional advantages in terms of processing time, versatility and ease of implementation/operation.

Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

Trends in Molecular Diagnosis and Diversity Studies for Phytosanitary Regulated Xanthomonas

Bacteria in the genus Xanthomonas infect a wide range of crops and wild plants, with most species responsible for plant diseases that have a global economic and environmental impact on the seed, plant, and food trade. Infections by Xanthomonas spp. cause a wide variety of non-specific symptoms, making their identification difficult. The coexistence of phylogenetically close strains, but drastically different in their phenotype, poses an added challenge to diagnosis. Data on future climate change scenarios predict an increase in the severity of epidemics and a geographical expansion of pathogens, increasing pressure on plant health services. In this context, the effectiveness of integrated disease management strategies strongly depends on the availability of rapid, sensitive, and specific diagnostic methods. The accumulation of genomic information in recent years has facilitated the identification of new DNA markers, a cornerstone for the development of more sensitive and specific methods. Nevertheless, the challenges that the taxonomic complexity of this genus represents in terms of diagnosis together with the fact that within the same bacterial species, groups of strains may interact with distinct host species demonstrate that there is still a long way to go. In this review, we describe and discuss the current molecular-based methods for the diagnosis and detection of regulated Xanthomonas, taxonomic and diversity studies in Xanthomonas and genomic approaches for molecular diagnosis.

Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics

Human activities significantly contribute to worldwide spread of phytopathological adversities. Pathogen-related food losses are today responsible for a reduction in quantity and quality of yield and decrease value and financial returns. As a result, “early detection” in combination with “fast, accurate, and cheap” diagnostics have also become the new mantra in plant pathology, especially for emerging diseases or challenging pathogens that spread thanks to asymptomatic individuals with subtle initial symptoms but are then difficult to face. Furthermore, in a globalized market sensitive to epidemics, innovative tools suitable for field-use represent the new frontier with respect to diagnostic laboratories, ensuring that the instruments and techniques used are suitable for the operational contexts. In this framework, portable systems and interconnection with Internet of Things (IoT) play a pivotal role. Here we review innovative diagnostic methods based on nanotechnologies and new perspectives concerning information and communication technology (ICT) in agriculture, resulting in an improvement in agricultural and rural development and in the ability to revolutionize the concept of “preventive actions”, making the difference in fighting against phytopathogens, all over the world.

Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review

Plant diseases result in 20-40% of agricultural loss every year worldwide. Timely detection of plant diseases can effectively prevent the development and spread of diseases and ensure the agricultural yield. High-throughput and rapid methods are in great demand. This review investigates the advanced application of Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) in the detection of plant diseases. The determination of bacterial diseases and stress-induced diseases, fungal diseases, viral diseases, pests in beans, and mycotoxins related to plant diseases using RS and SERS are discussed in detail. Then, biomarkers for RS and SERS detection are analyzed with regard to plant disease diagnosis. Finally, the advantages and challenges are further illustrated. Additionally, potential alternatives are proposed for the challenges. The review is expected to provide a reference and guidance for the use of RS and SERS in plant disease diagnostics.

Rapid and Specific Detection of the Poplar Black Spot Disease Caused by Marssonina brunnea Using Loop-Mediated Isothermal Amplification Assay

Marssonina brunnea is the main pathogen that causes poplar black spot disease, which leads to the decrease of the photosynthetic efficiency and significantly affects the production and quality of timber. Currently, no in-field diagnostic exists for M. brunnea. Here, we described a loop-mediated isothermal amplification (LAMP) assay for the rapid and sensitive detection of M. brunnea. A set of six oligonucleotide primers was designed to recognize eight distinct sequences of the internal transcribed spacer (ITS) region of M. brunnea. The LAMP assay was optimized by the combination of high specificity, sensitivity, and rapidity for the detection of less than 10 pg/μL of target genomic DNA in 60 min per reaction at 65 °C, whereas with PCR, there was no amplification of DNA with concentration less than 1 ng/μL. Among the genomic DNA of 20 fungalisolates, only the samples containing the genomic DNA of M. brunnea changed from violet to sky blue (visible to the naked eye) by using hydroxynaphthol blue (HNB) dye. No DNA was amplified from the eight other fungus species, including two other Marssonina pathogens, three other foliar fungi pathogens of poplar, and three common foliar fungal endophytes of poplar. Moreover, the detection rates of M. brunnea from artificially and naturally infected poplar leaves were 10/16 (62.5%) and 6/16 (37.5%) using PCR, respectively, while the positive-sample ratios were both 16/16 (100%) using the LAMP assay. Overall, the ITS LAMP assay established here can be a better alternative to PCR-based techniques for the specific and sensitive detection of M. brunnea in poplar endemic areas with resource-limited settings.

Amplicon Sequencing Reveals Extensive Coinfections of Foliar Pathogens in Soybean

Soybean (Glycine max) is one of the most economically important crops grown in North America and in other regions worldwide. However, the plant is susceptible to a variety of foliar pathogenic microorganisms, some of which are a significant threat to production. Several molecular and serological approaches are currently available to diagnose plant pathogens, but all have limitations including their capability to accurately detect coinfections of individual plants. We therefore used 16S and internal transcribed spacer amplicon sequencing to identify the suite of bacterial and fungal organisms infecting 96 soybean leaf samples collected throughout southern Manitoba, Canada, at two growth stages (V2/3 and R6). We were able to confirm the presence of pathogens previously known to our sampling regions, such as Septoria glycines, Alternaria alternata, and Pseudomonas spp. Importantly, we found that most of plants were infected by more than one putative pathogen, with 64% of V2/3 and 26% of R6 plants infected by three or more pathogens. Amplicon sequencing also indicated the presence of residual pathogens that infect crops other than soybean, as well as nonfoliar pathogens and nonpathogenic microorganisms. We discuss some of the benefits and drawbacks of using amplicon sequencing to detect foliar pathogens of soybean.

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Isolation of single-chain variable fragment (scFv) antibodies for detection of Chickpea chlorotic dwarf virus (CpCDV) by phage display

Chickpea chlorotic dwarf virus (CpCDV, genus Mastrevirus), has a wide host range and geographic distribution in many parts of the world, and it is one of the most important legume-infecting viruses. Detection of CpCDV-infected plants in the field and evaluation of viral resistance of plant cultivars are possible by conducting serological assays. Here, development and characterization of a specific recombinant monoclonal antibody for CpCDV as a diagnostic tool are described. For this purpose, the coat protein of CpCDV was expressed in Escherichia coli strain Rosetta (DE3) and used to screen a Tomlinson phage display antibody library to select a specific single-chain variable fragment (scFv). In each round of biopanning, the affinity of the phage for CpCDV-CP was tested by enzyme-linked immunosorbent assay (ELISA). The results showed that the specificity of the eluted phages increased after each round of panning. Testing of individual clones by ELISA showed that five clones of the monoclonal phage were more strongly reactive against CpCDV than the other clones. All selected positive clones contained the same sequence. The phage-displayed scFv antibody, which was named CpCDV-scFvB9, did not bind to other tested plant pathogens and showed high sensitivity in the detection of CpCDV. A Western blot assay demonstrated that CpCDV-scFvB9 reacted with the recombinant coat protein of CpCDV. Finally, the interaction CpCDV-scFvB9 and CpCDV-CP was analyzed in a molecular docking experiment. This is the first report on production of an scFv antibody against CpCDV, which could be useful for immunological detection of the virus.

Applications of Proteomic Tools to Study Insect Vector-Plant Virus Interactions

Proteins are crucial players of biological interactions within and between the organisms and thus it is important to understand the role of proteins in successful partnerships, such as insect vectors and their plant viruses. Proteomic approaches have identified several proteins at the interface of virus acquisition and transmission by their insect vectors which could be potential molecular targets for sustainable pest and viral disease management strategies. Here we review the proteomic techniques used to study the interactions of insect vector and plant virus. Our review will focus on the techniques available to identify the infection, global changes at the proteome level in insect vectors, and protein-protein interactions of insect vectors and plant viruses. Furthermore, we also review the integration of other techniques with proteomics and the available bioinformatic tools to analyze the proteomic data.

Nanotechnological interventions for plant health improvement and sustainable agriculture

Agriculture is the source of food for both humans and animals. With the growing population demands, agricultural production needs to be scaled up where nanotechnology can play a significant role. The use of nanotechnology in agriculture can manage plant disease and growth for better and quality output. Therefore, this review focuses on the use of various nanoparticles for detection of nutrients and contaminants, nanosensors for monitoring the environmental stresses and crop conditions as well as the use of nanotechnology for plant pathogen detection and crop protection. In addition, the delivery of plant growth regulators and agrichemicals like nanopesticides and nanofertilizers to the plants along with the delivery of DNA for targeted genetic engineering and production of genetically modified (GM) crops are discussed briefly. Further, the future concerns regarding the use of nanoparticles and their possible toxicity, impact on the agriculture and ecosystem needs to be assessed along with the assessment of the nanoparticles and GM crops on the environment and human health.

Development of PCR-Based Detection System for Soft Rot Pectobacteriaceae Pathogens Using Molecular Signatures

Pectobacterium and Dickeya species, usually referred to as soft rot Enterobacteriaceae, are phytopathogenic genera of bacteria that cause soft rot and blackleg diseases and are responsible for significant yield losses in many crops across the globe. Diagnosis of soft rot disease is difficult through visual disease symptoms. Pathogen detection and identification methods based on cultural and morphological identification are time-consuming and not always reliable. A polymerase chain reaction (PCR)-based detection method with the species-specific primers is fast and reliable for detecting soft rot pathogens. We have developed a specific and sensitive detection system for some species of soft rot Pectobacteriaceae pathogens in the Pectobacterium and Dickeya genera based on the use of species-specific primers to amplify unique genomic segments. The specificities of primers were verified by PCR analysis of genomic DNA from 14 strains of Pectobacterium, 8 strains of Dickeya, and 6 strains of non-soft rot bacteria. This PCR assay provides a quick, simple, powerful, and reliable method for detection of soft rot bacteria.

Synthesis of 2'-Deoxyuridine Modified with a 3,5-Difluoro-4-Methoxybenzylidene Imidazolinone Derivative for Incorporation into Oligonucleotide Probes for Detection of HER2 Breast Cancer Marker

Nucleoside intercalator conjugates (NICs) describe an innovative methodology developed in our research group for preparation of fluorescence turn-on DNA hybridization probes targeting specific mRNA sequences (e.g., breast cancer markers). In this methodology, we conjugate a non-fluorescent intercalator to the base of a nucleic acid (e.g., uracil) via a flexible spacer. This modified monomer can be incorporated into oligonucleotides by solid-phase synthesis and a large fluorescence enhancement is observed when the modified oligonucleotide is hybridized with its complementary strand due to intercalation of the fluorophore between the two strands. 5-(6-p-Methoxybenzylidene imidazolinone-1-hexene)-2'-deoxyuridine (dU^MBI ) is a synthetic monomer to which 4-methoxybenzylidene imidazolinone (MBI), the fluorescent chromophore of green fluorescent protein (GFP), has been conjugated via a flexible spacer. The detection of human epidermal growth factor receptor 2 (HER2) mRNA by this probe has already been established by our group. The fluorescent intensity of the single-strand DNA can be considered as negligible due to the free rotation of the fluorophore. Upon hybridization, however, the flexible spacer allows for the intercalation of the fluorophore between the hybridized strands, giving rise to enhanced fluorescence and indicating the presence of target mRNA. 3,5-Difluoro-4-methoxybenzylidene (DFMBI) has enhanced photophysical properties compared to MBI fluorophore. This protocol describes a simple, reliable, efficient, and general method for the synthesis of improved derivative dU^DFMBI as a monomer of fluorescent turn-on DNA hybridization probe with application for detection of HER2 mRNA. © 2020 by John Wiley & Sons, Inc. Basic Protocol: Synthesis of 5-[(6)-3,5-difluoro-4-methoxybenzylidene imidazolinone-1-hexene]-2'-deoxyuridine.

Development of the Droplet Digital PCR to Detect the Teliospores of Tilletia controversa Kühn in the Soil With Greatly Enhanced Sensitivity

BACKGROUND AND AIMS

The dwarf bunt disease of wheat is caused by Tilletia controversa Kühn. This pathogen is primarily involved in the stunted growth of wheat and affects seed quality. Many countries in the world have therefore imposed quarantine bans to prevent the spread of T. controversa. Morphological observations are the main method of detecting teliospores in soil. However, this is a lengthy and laborious process; this method is thus unable to quickly meet the demand for detection of teliospores in the soil.

METHODS

We compared PCR, real-time PCR and droplet digital PCR (ddPCR) for the qualitative and quantitative measurement of the teliospores of T. controversa in soil.

RESULTS

We suggest the use of ddPCR for detection of the soil samples, which was demonstrated to have the most sensitive detection at 2.1 copies/μL. In contract, SYBR Green I real-time PCR could detect 7.97 copies/μL of T. controversa in soil, and this sensitivity was 100 times more sensitive than that of simple PCR.

CONCLUSION

This study was the first report using ddPCR techniques to detect T. controversa teliospores in soil with greatly enhanced sensitivity.

Visual DNA diagnosis of Tomato yellow leaf curl virus with integrated recombinase polymerase amplification and a gold-nanoparticle probe

A visual DNA diagnosis with a rapid and simple procedure has been developed on integrating recombinase polymerase amplification (RPA) and a gold nanoparticle (AuNP) probe. The entire process is implemented in only one tube with no precision instrument and requires in total 20 min to amplify a DNA fragment with RPA and to discriminate a DNA fragment with an AuNP probe. The result in various colors is directly observable with the naked eye. Through discovering a small DNA fragment of Tomato yellow leaf curl virus (TYLCV), this system can detect one copy per microlitre of virus in a pure isolate of extracted DNA and can readily identify an infected plant with a healthy appearance. This system hence provides a highly sensitive and stable DNA diagnosis. This visual method has a potential for disease diagnosis and prognostication in the field based on advantages of simplicity, high speed, portability and sensitivity.

Viral Detection: Past, Present, and Future

Viruses are essentially composed of a nucleic acid (segmented or not, DNA, or RNA) and a protein coat. Despite their simplicity, these small pathogens are responsible for significant economic and humanitarian losses that have had dramatic consequences in the course of human history. Since their discovery, scientists have developed different strategies to efficiently detect viruses, using all possible viral features. Viruses shape, proteins, and nucleic acid are used in viral detection. In this review, the development of these techniques, especially for plant and mammalian viruses, their strengths and weaknesses as well as the latest cutting-edge technologies that may be playing important roles in the years to come are described.

Nondestructive Raman Spectroscopy as a Tool for Early Detection and Discrimination of the Infection of Tomato Plants by Two Economically Important Viruses

Global population forecasts dictate a rapid adoption of multifaceted approaches to fulfill increasing food requirements, ameliorate food dietary value and security using sustainable and economically feasible agricultural processes. Plant pathogens induce up to 25% losses in vegetable crops and their early detection would contribute to limit their spread and economic impact. As an alternative to time-consuming, destructive, and expensive diagnostic procedures, such as immunological assays and nucleic acid-based techniques, Raman spectroscopy (RS) is a nondestructive rapid technique that generates a chemical fingerprinting of a sample, at low operating costs. Here, we assessed the suitability of RS combined to chemometric analysis to monitor the infection of an important vegetable crop plant, tomato, by two dangerous and peculiarly different viral pathogens, Tomato yellow leaf curl Sardinia virus (TYLCSV) and Tomato spotted wilt virus (TSWV). Experimentally inoculated plants were monitored over 28 days for symptom occurrence and subjected to RS analysis, alongside with measuring the virus amount by quantitative real-time PCR. RS allowed to discriminate mock inoculated (healthy) from virus-infected specimens, reaching an accuracy of >70% after only 14 days after inoculation for TYLCSV and >85% only after 8 days for TSWV, demonstrating its suitability for early detection of virus infection. Importantly, RS also highlighted spectral differences induced by the two viruses, providing specific information on the infecting agent.

Extraction of Plant DNA by Microneedle Patch for Rapid Detection of Plant Diseases

In-field molecular diagnosis of plant diseases via nucleic acid amplification is currently limited by cumbersome protocols for extracting and isolating pathogenic DNA from plant tissues. To address this challenge, a rapid plant DNA extraction method was developed using a disposable polymeric microneedle (MN) patch. By applying MN patches on plant leaves, amplification-assay-ready DNA can be extracted within a minute from different plant species. MN-extracted DNA was used for direct polymerase chain reaction amplification of plant plastid DNA without purification. Furthermore, using this patch device, extraction of plant pathogen DNA ( Phytophthora infestans) from both laboratory-inoculated and field-infected leaf samples was performed for detection of late blight disease in tomato. MN extraction achieved 100% detection rate of late blight infections for samples after 3 days of inoculation when compared to the conventional gold standard cetyltrimethylammonium bromide (CTAB)-based DNA extraction method and 100% detection rate for all blind field samples tested. This simple, cell-lysis-free, and purification-free DNA extraction method could be a transformative approach to facilitate rapid sample preparation for molecular diagnosis of various plant diseases directly in the field.

Applications of Next-Generation Sequencing for Large-Scale Pathogen Diagnoses in Soybean

Soybean (Glycine max) has become an important crop in Manitoba, Canada, with a 10-fold increase in dedicated acreage over the past decade. Given the rapid increase in production, scarce information about foliar diseases present in the province has been recorded. In order to describe the foliar pathogens affecting this legume, we harnessed next-generation sequencing (NGS) to carry out a comprehensive survey across Manitoba in 2016. Fields were sampled during the V2/3 (33 fields) and R6 (70 fields) growth stages, with at least three symptomatic leaves per field collected and subjected to RNA sequencing. We successfully detected several bacteria, fungi, and viruses known to infect soybean, including Pseudomonas savastanoi pv. glycinea, Septoria glycines, and Peronospora manshurica, as well as pathogens not previously identified in the province (e.g., Pseudomonas syringae pv. tabaci, Cercospora sojina, and Bean yellow mosaic virus). For some microorganisms, we were able to disentangle the different pathovars present and/or assemble their genome sequence. Since NGS generates data on the entire flora and fauna occupying a leaf sample, we also identified residual pathogens (i.e., pathogens of crops other than soybean) and multiple species of arthropod pests. Finally, the sequence information produced by NGS allowed for the development of polymerase chain reaction-based diagnostics for some of the most widespread and important pathogens. Although there are many benefits of using NGS for large-scale plant pathogen diagnoses, we also discuss some of the limitations of this technology.

Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

Sclerotinia stem rot (SSR) is one of the most destructive diseases in the world caused by Sclerotinia sclerotiorum (S. sclerotiorum), resulting in significant yield loss. Early and high-throughput detection would be critical to prevent SSR from spreading. This study aimed to propose a feasible method for SSR detection based on the hyperspectral imaging coupled with multivariate analysis. The performance of different detecting algorithms were compared by combining the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) with the random frog (RF), successive projection algorithm (SPA) and sequential forward selection (SFS). The similarity of selected optimal wavelengths by three different feature selection methods indicated a high correlation between selected wavelengths and SSR. Compared with KNN, LDA, NB, and SVM, three wavelengths (455, 671 and 747 nm) selected by SFS-CA combined with ELM could achieve relatively better results with the overall accuracy of 93.7% and the lowest false negative rate of 2.4%. These results demonstrated the potential of the presented method using hyperspectral reflectance imaging combined with multivariate analysis for SSR diagnosis.

Affimer reagents as tools in diagnosing plant virus diseases

Plant viruses can cause devastating losses to agriculture and are therefore a major threat to food security. The rapid identification of virally-infected crops allowing containment is essential to limit such threats, but plant viral diseases can be extremely challenging to diagnose. An ideal method for plant virus diagnosis would be a device which can be implemented easily in the field. Such devices require a binding reagent that is specific for the virus of interest. We chose to investigate the use of Affimer reagents, artificial binding proteins and a model plant virus Cowpea Mosaic virus (CPMV) empty virus like particles (eVLPs). CPMV-eVLP mimic the morphology of wild-type (WT) CPMV but lack any infectious genomic material and so do not have biocontainment issues. We have produced and purified an Affimer reagent selected for its ability to bind to CPMV-eVLP and have shown that the selected Affimer also specifically binds to WT CPMV. We have produced a 3.4 Å structure of WT CPMV bound to the Affimer using cryo-electron microscopy. Finally, we have shown that this Affimer is capable of reliably detecting the virus in crude extracts of CPMV-infected leaves and can therefore form the basis for the future development of diagnostic tests.

Point-of-Need DNA Testing for Detection of Foodborne Pathogenic Bacteria

Foodborne pathogenic bacteria present a crucial food safety issue. Conventional diagnostic methods are time-consuming and can be only performed on previously produced food. The advancing field of point-of-need diagnostic devices integrating molecular methods, biosensors, microfluidics, and nanomaterials offers new avenues for swift, low-cost detection of pathogens with high sensitivity and specificity. These analyses and screening of food items can be performed during all phases of production. This review presents major developments achieved in recent years in point-of-need diagnostics in land-based sector and sheds light on current challenges in achieving wider acceptance of portable devices in the food industry. Particular emphasis is placed on methods for testing nucleic acids, protocols for portable nucleic acid extraction and amplification, as well as on the means for low-cost detection and read-out signal amplification.

Selection of reference genes for quantitative real-time PCR normalization in the plant pathogen Puccinia helianthi Schw

BACKGROUND

Real-time RT-PCR has become a common and robust technique to detect and quantify low-abundance mRNA expression and is a prefered tool when examining fungal gene expression in infected host tissues. However, correct evaluation of gene expression data requires accurate and reliable normalization against a reference transcript. Thus, the identification of reference genes with stable expression during different conditions is of paramount importance. Here, we present a study where in vitro and in planta experiments were used to validate the expression stability of reference gene candidates of Puccinia helianthi Schw., an obligate pathogen that causes rust in sunflower (Helianthus annuus).

RESULTS

Eleven reference genes of P. helianthi were validated at different growth stages. Excel-based software geNorm, BestKeeper and NormFinder were used to evaluate the reference gene transcript stabilities. Of eleven reference gene candidates tested, three were stably expressed in urediniospores, germinating growth stage and in planta. Two of these genes (UBC, EF2), encoding ubiquitin-conjugating enzyme and elongation factor 2, proved to be the most stable set of reference genes under the experimental conditions used.

CONCLUSION

We found that UBC and EF2 are suitable candidates for for the standardization of gene expression studies in the plant pathogen P. helianthi and potentially other related pathogens.

Identification of Thiobacillus bacteria in agricultural soil in Iran using the 16S rRNA gene

Thiobacillus, as useful soil bacteria, plays an important role in sulfur cycling. The purpose of this study was to identify the species Thiobacillus thioparus, Thiobacillus novellas and Thiobacillus denitrificans in rainfed and irrigated lands soil in Ajabshir, Ilam, Qorveh, Rojintaak, Sonqor, Kermanshah and Research Farm of Razi University in Iran. Sampling was performed as randomized completely with three replications at depth of 0-30 cm. The Thiobacillus species were determined via 16S rRNA characteristics. The results of agarose gel electrophoresis indicated that T. thioparus was the highest amount in the irrigated land in Research Farm and its lowest amount was in the Rojintaak rainfed land. These species not found in four locations and conditions including the Ajabshir irrigated, Qorveh rainfed, Research Farm rainfed and Rojintaak irrigated lands. The results of the T. novellas indicated that this was found in Ilam irrigated, Qorveh rainfed, Research Farm irrigated, Rojintaak irrigated and Rojintaak rainfed lands. The highest and lowest amount of T. novellas was indicated in the Rojintaak and Ilam irrigated lands respectively. The T. denitrificans gene showed that this bacterium was observed only in both samples of Ajabshir. Our study showed that Thiobacillus was not detected in all of the soils. If sulfur fertilizer is given to the soil without this bacterium, it is necessary to use sulfur fertilizer with Thiobacillus bacteria inoculation for better sulfur oxidation.

Recent Advances on the Multiplex Molecular Detection of Plant Viruses and Viroids

Plant viruses are still one of the main contributors to economic losses in agriculture. It has been estimated that plant viruses can cause as much as 50 billion euros loss worldwide, per year. This situation may be worsened by recent climate change events and the associated changes in disease epidemiology. Reliable and early detection methods are still one of the main and most effective actions to develop control strategies for plant viral diseases. During the last years, considerable progress has been made to develop tools with high specificity and low detection limits for use in the detection of these plant pathogens. Time and cost reductions have been some of the main objectives pursued during the last few years as these increase their feasibility for routine use. Among other strategies, these objectives can be achieved by the simultaneous detection and (or) identification of several viruses in a single assay. Nucleic acid-based detection techniques are especially suitable for this purpose. Polyvalent detection has allowed the detection of multiple plant viruses at the genus level. Multiplexing RT polymerase chain reaction (PCR) has been optimized for the simultaneous detection of more than 10 plant viruses/viroids. In this short review, we provide an update on the progress made during the last decade on techniques such as multiplex PCR, polyvalent PCR, non-isotopic molecular hybridization techniques, real-time PCR, and array technologies to allow simultaneous detection of multiple plant viruses. Also, the potential and benefits of the powerful new technique of deep sequencing/next-generation sequencing are described.

Recent Advances on Detection and Characterization of Fruit Tree Viruses Using High-Throughput Sequencing Technologies

Perennial crops, such as fruit trees, are infected by many viruses, which are transmitted through vegetative propagation and grafting of infected plant material. Some of these pathogens cause severe crop losses and often reduce the productive life of the orchards. Detection and characterization of these agents in fruit trees is challenging, however, during the last years, the wide application of high-throughput sequencing (HTS) technologies has significantly facilitated this task. In this review, we present recent advances in the discovery, detection, and characterization of fruit tree viruses and virus-like agents accomplished by HTS approaches. A high number of new viruses have been described in the last 5 years, some of them exhibiting novel genomic features that have led to the proposal of the creation of new genera, and the revision of the current virus taxonomy status. Interestingly, several of the newly identified viruses belong to virus genera previously unknown to infect fruit tree species (e.g., Fabavirus, Luteovirus) a fact that challenges our perspective of plant viruses in general. Finally, applied methodologies, including the use of different molecules as templates, as well as advantages and disadvantages and future directions of HTS in fruit tree virology are discussed.

Research Progress on Rolling Circle Amplification (RCA)-Based Biomedical Sensing

Enhancing the limit of detection (LOD) is significant for crucial diseases. Cancer development could take more than 10 years, from one mutant cell to a visible tumor. Early diagnosis facilitates more effective treatment and leads to higher survival rate for cancer patients. Rolling circle amplification (RCA) is a simple and efficient isothermal enzymatic process that utilizes nuclease to generate long single stranded DNA (ssDNA) or RNA. The functional nucleic acid unit (aptamer, DNAzyme) could be replicated hundreds of times in a short period, and a lower LOD could be achieved if those units are combined with an enzymatic reaction, Surface Plasmon Resonance, electrochemical, or fluorescence detection, and other different kinds of biosensor. Multifarious RCA-based platforms have been developed to detect a variety of targets including DNA, RNA, SNP, proteins, pathogens, cytokines, micromolecules, and diseased cells. In this review, improvements in using the RCA technique for medical biosensors and biomedical applications were summarized and future trends in related research fields described.

A Systems Approach to Agricultural Biosecurity

This article highlights the importance of systems approaches in addressing agricultural biosecurity threats. On the basis of documentary analysis and stakeholder interaction, a brief survey of agricultural biosecurity threats and vulnerabilities from global and Indian perspectives is provided, followed by an exploration of technological and institutional capabilities. Finally, a perspective on the agricultural disease diagnostic networks is provided, drawing instances from global developments. Technical barriers to agroterrorism are lower than those to human-targeted bioterrorism, and the sector is unique as even a very small disease outbreak could prompt international export restrictions. Key vulnerabilities in the agriculture sector stem from, among others, the structure of agricultural production; insufficient monitoring, surveillance, and controls systems at the borders and in the food chain; inefficient systems for reporting unusual occurrences and outbreaks of disease; and lack of sufficiently trained human resources capable of recognizing or treating transboundary pathogens and diseases. An assessment of technology and institutions pertaining to crop and animal protection management suggests certain gaps. Investment in developing new technologies for civilian application in agriculture, as well as for legitimate actions pertaining to defense, detection, protection, and prophylaxis, and in upgrading laboratory facilities can increase the agricultural sector's level of preparedness for outbreaks. To address potential threats and vulnerabilities of agroterrorism effectively requires the development of a comprehensive strategy and a combined, interagency approach, ideally on an international level. It is proposed that a systems-oriented approach for developing knowledge and innovation networks and strengthening skills and capacities would enable a more resilient agricultural biosecurity system.

Plant Pest Detection Using an Artificial Nose System: A Review

This paper reviews artificial intelligent noses (or electronic noses) as a fast and noninvasive approach for the diagnosis of insects and diseases that attack vegetables and fruit trees. The particular focus is on bacterial, fungal, and viral infections, and insect damage. Volatile organic compounds (VOCs) emitted from plants, which provide functional information about the plant's growth, defense, and health status, allow for the possibility of using noninvasive detection to monitor plants status. Electronic noses are comprised of a sensor array, signal conditioning circuit, and pattern recognition algorithms. Compared with traditional gas chromatography-mass spectrometry (GC-MS) techniques, electronic noses are noninvasive and can be a rapid, cost-effective option for several applications. However, using electronic noses for plant pest diagnosis is still in its early stages, and there are challenges regarding sensor performance, sampling and detection in open areas, and scaling up measurements. This review paper introduces each element of electronic nose systems, especially commonly used sensors and pattern recognition methods, along with their advantages and limitations. It includes a comprehensive comparison and summary of applications, possible challenges, and potential improvements of electronic nose systems for different plant pest diagnoses.

Single Domain Antibodies as New Biomarker Detectors

Biomarkers are defined as indicators of biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention. Biomarkers have been widely used for early detection, prediction of response after treatment, and for monitoring the progression of diseases. Antibodies represent promising tools for recognition of biomarkers, and are widely deployed as analytical tools in clinical settings. For immunodiagnostics, antibodies are now exploited as binders for antigens of interest across a range of platforms. More recently, the discovery of antibody surface display and combinatorial chemistry techniques has allowed the exploration of new binders from a range of animals, for instance variable domains of new antigen receptors (V_NAR) from shark and variable heavy chain domains (V_HH) or nanobodies from camelids. These single domain antibodies (sdAbs) have some advantages over conventional murine immunoglobulin owing to the lack of a light chain, making them the smallest natural biomarker binders thus far identified. In this review, we will discuss several biomarkers used as a means to validate diseases progress. The potential functionality of modern singe domain antigen binders derived from phylogenetically early animals as new biomarker detectors for current diagnostic and research platforms development will be described.

A Portable Impedance Immunosensing System for Rapid Detection of Salmonella Typhimurium

SalmonellaTyphimurium is one of the most dangerous foodborne pathogens and poses a significant threat to human health. The objective of this study was to develop a portable impedance immunosensing system for rapid and sensitive detection of S. Typhimurium in poultry. The developed portable impedance immunosensing system consisted of a gold interdigitated array microelectrode (IDAM), a signal acquisitive interface and a laptop computer with LabVIEW software. The IDAM was first functionalized with 16-Mercaptohexadecanoic acid, and streptavidin was immobilized onto the electrode surface through covalent bonding. Then, biotin-labelled S. Typhimurium-antibody was immobilized onto the IDAM surface. Samples were dropped on the surface of the IDAM and the S. Typhimurium cells in the samples were captured by the antibody on the IDAM. This resulted in impedance changes that were measured and displayed with the LabVIEW software. An equivalent circuit of the immunosensor demonstrated that the largest change in impedance was due to the electron-transfer resistance. The equivalent circuit showed an increase of 35% for the electron-transfer resistance value compared to the negative control. The calibration result indicated that the portable impedance immunosensing system could be used to measure the standard impedance elements, and it had a maximum error of measurement of approximately 13%. For pure culture detection, the system had a linear relationship between the impedance change and the logarithmic value of S. Typhimurium cells ranging from 76 to 7.6 × 10⁶ CFU (colony-forming unit) (50 μL)⁻¹. The immunosensor also had a correlation coefficient of 0.98, and a high specificity for detection of S. Typhimurium cells with a limit of detection (LOD) of 10² CFU (50 μL)⁻¹. The detection time from the moment a sample was introduced to the display of the results was 1 h. To conclude, the portable impedance immunosensing system for detection of S. Typhimurium achieved an LOD that is comparable with commercial electrochemical impedance instruments. The developed impedance immunosensor has advantages in portability, low cost, rapid detection and label-free features showing a great potential for in-field detection of foodborne pathogens.

Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-learning Classifiers

We investigated the feasibility and potentiality of presymptomatic detection of tobacco disease using hyperspectral imaging, combined with the variable selection method and machine-learning classifiers. Images from healthy and TMV-infected leaves with 2, 4, and 6 days post infection were acquired by a pushbroom hyperspectral reflectance imaging system covering the spectral range of 380-1023 nm. Successive projections algorithm was evaluated for effective wavelengths (EWs) selection. Four texture features, including contrast, correlation, entropy, and homogeneity were extracted according to grey-level co-occurrence matrix (GLCM). Additionally, different machine-learning algorithms were developed and compared to detect and classify disease stages with EWs, texture features and data fusion respectively. The performance of chemometric models with data fusion manifested better results with classification accuracies of calibration and prediction all above 80% than those only using EWs or texture features; the accuracies were up to 95% employing back propagation neural network (BPNN), extreme learning machine (ELM), and least squares support vector machine (LS-SVM) models. Hence, hyperspectral imaging has the potential as a fast and non-invasive method to identify infected leaves in a short period of time (i.e. 48 h) in comparison to the reference images (5 days for visible symptoms of infection, 11 days for typical symptoms).

Multiple DNA Markers for Identification of Xanthomonas arboricola pv. juglandis Isolates and its Direct Detection in Plant Samples

Xanthomonas arboricola pv. juglandis (Xaj) is the etiological agent of walnut (Juglans regia L.) bacterial blight (WBB), and has been associated to other walnut emerging diseases, namely brown apical necrosis (BAN) and vertical oozing canker (VOC), altogether severely affecting the walnut production worldwide. Despite the research efforts carried out to disclose Xaj genetic diversity, reliable molecular methods for rapid identification of Xaj isolates and culture-independent detection of Xaj in infected plant samples are still missing. In this work, we propose nine novel specific DNA markers (XAJ1 to XAJ9) selected by dedicated in silico approaches to identify Xaj isolates and detect these bacteria in infected plant material. To confirm the efficacy and specificity of these markers, dot blot hybridization was carried out across a large set of xanthomonads. This analysis, which confirmed the pathovar specificity of these markers, allowed to identify four broad-range markers (XAJ1, XAJ4, XAJ6, and XAJ8) and five narrow-range markers (XAJ2, XAJ3, XAJ5, XAJ7, and XAJ9), originating 12 hybridization patterns (HP1 to HP12). No evident relatedness was observed between these hybridization patterns and the geographic origin from which the isolates were obtained. Interestingly, four isolates that clustered together according the gyrB phylogenetic analysis (CPBF 1507, 1508, 1514, and 1522) presented the same hybridization pattern (HP11), suggesting that these nine markers might be informative to rapidly discriminate and identify different Xaj lineages. Taking into account that a culture-independent detection of Xaj in plant material has never been described, a multiplex PCR was optimized using markers XAJ1, XAJ6, and XAJ8. This triplex PCR, besides confirming the dot blot data for each of the 52 Xaj, was able to detect Xaj in field infected walnut leaves and fruits. Altogether, these nine Xaj-specific markers allow conciliating the specificity of DNA-detection assays with typing resolution, contributing to rapid detection and identification of potential emergent and acutely virulent Xaj genotypes, infer their distribution, disclose the presence of this phytopathogen on potential alternative host species and improve phytosanitary control.

Immune Tissue Print and Immune Capture-PCR for Diagnosis and Detection of Candidatus Liberibacter Asiaticus

'Candidatus Liberibacter asiaticus' (CaLas), associated with citrus Huanglongbing (HLB), is a non culturable member of the α-proteobacteria. In this study serologically based methods for the detection of CaLas were developed. An anti-outer membrane protein A (OmpA) polyclonal antibody previously produced (in our laboratory) was highly effective for the detection of CaLas from citrus tissues in a simple tissue printing format. The antibody was also used to capture bacteria from periwinkle extracts. About 80% of all field samples analyzed tested positive with both immune tissue printing and qPCR; whereas 95% were positive with at least one of these two methods. When asymptomatic citrus tissues were tested, the tissue printing method gave a higher rate of detection (83%) than the qPCR method (64%). This is consistent with a lower concentration of CaLas DNA, but a higher proportion of viable cells, in the asymptomatic tissues. The immune tissue printing method also highlights the detail of the spatial distribution of 'Ca. Liberibacter asiaticus' in diseased citrus tissues. Both the immune capture PCR and immune tissue printing methods offer the advantages of low cost, high throughput, ease of scaling for multiple samples and simplicity over current PCR-based methods for the detection of 'Ca. Liberibacter asiaticus'.