Paradigm Shift in Plant Disease Diagnostics: A Journey from Conventional Diagnostics to Nano-diagnostics

Pioneering Nit Gene Exploitation to Develop Molecular Diagnostic Assay for Rapid Detection of Cotton Root Rot Incitant, Macrophomina phaseolina (Tassi) Goid, in Field Soil

Cotton root rot caused by Macrophomina phaseolina pose a significant threat to cotton production, leading to substantial yield and quality losses. Early and accurate diagnosis of this pathogen in soil is crucial for effective disease management. This study presents a pioneering investigation into the utilization of the nit gene encoding nitrilase for the development of a molecular diagnostic assay aimed at the rapid detection of M. phaseolina in field soils. The methodology involved the design and validation of primers targeting the Nit gene sequence, followed by the optimization of PCR conditions for efficient amplification. Leveraging state-of-the-art molecular techniques, the assay offers a novel protocol to accurately identify the presence of M. phaseolina in soil with high sensitivity and specificity. The specificity of the designed primers was confirmed through PCR amplification using DNA from M. phaseolina and other related fungi. Sensitivity tests demonstrated that the PCR assay reliably detected M. phaseolina DNA at concentrations as low as 1 ng. Furthermore, the performance of the diagnostic assay was rigorously evaluated using field soil samples with a known status of M. phaseolina infection, demonstrating its reliability and efficacy in real-world scenarios. This study introduces a novel molecular marker for the detection of M. phaseolina and offers a rapid and efficient means for screening M. phaseolina in large soil samples with minimal time and manpower.

Recent advances in the population biology and management of maize foliar fungal pathogens Exserohilum turcicum, Cercospora zeina and Bipolaris maydis in Africa

Maize is the most widely cultivated and major security crop in sub-Saharan Africa. Three foliar diseases threaten maize production on the continent, namely northern leaf blight, gray leaf spot, and southern corn leaf blight. These are caused by the fungi Exserohilum turcicum, Cercospora zeina, and Bipolaris maydis, respectively. Yield losses of more than 10% can occur if these pathogens are diagnosed inaccurately or managed ineffectively. Here, we review recent advances in understanding the population biology and management of the three pathogens, which are present in Africa and thrive under similar environmental conditions during a single growing season. To effectively manage these pathogens, there is an increasing adoption of breeding for resistance at the small-scale level combined with cultural practices. Fungicide usage in African cropping systems is limited due to high costs and avoidance of chemical control. Currently, there is limited knowledge available on the population biology and genetics of these pathogens in Africa. The evolutionary potential of these pathogens to overcome host resistance has not been fully established. There is a need to conduct large-scale sampling of isolates to study their diversity and trace their migration patterns across the continent.

Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning

Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350-2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.

Whole-genome single nucleotide polymorphism analysis for typing the pandemic pathogen Fusarium graminearum sensu stricto

Recent improvements in microbiology and molecular epidemiology were largely stimulated by whole- genome sequencing (WGS), which provides an unprecedented resolution in discriminating highly related genetic backgrounds. WGS is becoming the method of choice in epidemiology of fungal diseases, but its application is still in a pioneer stage, mainly due to the limited number of available genomes. Fungal pathogens often belong to complexes composed of numerous cryptic species. Detecting cryptic diversity is fundamental to understand the dynamics and the evolutionary relationships underlying disease outbreaks. In this study, we explore the value of whole-genome SNP analyses in identification of the pandemic pathogen Fusarium graminearum sensu stricto (F.g.). This species is responsible for cereal diseases and negatively impacts grain production worldwide. The fungus belongs to the monophyletic fungal complex referred to as F. graminearum species complex including at least sixteen cryptic species, a few among them may be involved in cereal diseases in certain agricultural areas. We analyzed WGS data from a collection of 99 F.g. strains and 33 strains representing all known cryptic species belonging to the FGSC complex. As a first step, we performed a phylogenomic analysis to reveal species-specific clustering. A RAxML maximum likelihood tree grouped all analyzed strains of F.g. into a single clade, supporting the clustering-based identification approach. Although, phylogenetic reconstructions are essential in detecting cryptic species, a phylogenomic tree does not fulfill the criteria for rapid and cost-effective approach for identification of fungi, due to the time-consuming nature of the analysis. As an alternative, analysis of WGS information by mapping sequence data from individual strains against reference genomes may provide useful markers for the rapid identification of fungi. We provide a robust framework for typing F.g. through the web-based PhaME workflow available at EDGE bioinformatics. The method was validated through multiple comparisons of assembly genomes to F.g. reference strain PH-1. We showed that the difference between intra- and interspecies variability was at least two times higher than intraspecific variation facilitating successful typing of F.g. This is the first study which employs WGS data for typing plant pathogenic fusaria.

Point of care diagnosis of plant virus: Current trends and prospects

Plant viral diseases accounts for major global economic losses in modern-day agriculture. Plant viral disease management is the primary challenge for both farmers and researchers. Detection and identification of plant viruses are of paramount importance for successful management of a viral disease. Recent advancements in molecular biology have contributed to significant progress in the development of new, sensitive, and effective diagnostic methods. However, most techniques are neither time/cost-effective nor user-friendly and require sophisticated labs. Hence, the past few decades of agricultural research have mainly focused on developing farmer-friendly, point-of-care diagnostic tools that provide high-sensitive rapid diagnosis. The current trend in plant virus diagnostic tools is cheaper, easy-to-use portable devices with no compromise on sensitivity and reproducibility.

Development of a Multiplex High-Throughput Diagnostic Assay for the Detection of Strawberry Crown Rot Diseases Using High-Resolution Melting Analysis

Rapid and accurate disease diagnosis is a prerequisite for an effective disease management program in strawberry production. In Florida, Colletotrichum spp., Phytophthora spp., and Macrophomina phaseolina are the primary microorganisms causing strawberry crown rot. Even though the diseases can be caused by different pathogens, symptoms are indistinguishable and equally devastating. To inform strawberry growers in a timely fashion of diagnostic results for effective deployment of chemical control practices, we developed a multiplex high-resolution melting (HRM) assay to rapidly and accurately detect the abovementioned pathogens. The multiplex HRM assays using three predesigned primer pairs showed high specificity for individual species by generating specific melting peaks without cross-reaction between primers or with other common strawberry pathogens. The amplification limit of the assay was 1 pg of Colletotrichum and Phytophthora and 100 pg of M. phaseolina DNA per 10-μl reaction. However, the presence of different melting peaks was observed in mixed DNA samples and was concentration and target DNA dependent. A crude DNA extraction protocol was developed to allow high-throughput screening by minimizing the inhibitory effects. Moreover, we applied the HRM assay to 522 plant samples and found high correlations between conventional pathogen isolation and HRM and between singleplex and multiplex assays. Altogether, this multiplex HRM assay is specific, cost effective, and reliable for the timely detection of strawberry crown rot pathogens.

Recent Advances in Molecular Diagnostics of Fungal Plant Pathogens: A Mini Review

Phytopathogenic fungal species can cause enormous losses in quantity and quality of crop yields and this is a major economic issue in the global agricultural sector. Precise and rapid detection and identification of plant infecting fungi are essential to facilitate effective management of disease. DNA-based methods have become popular methods for accurate plant disease diagnostics. Recent developments in standard and variant polymerase chain reaction (PCR) assays including nested, multiplex, quantitative, bio and magnetic-capture hybridization PCR techniques, post and isothermal amplification methods, DNA and RNA based probe development, and next-generation sequencing provide novel tools in molecular diagnostics in fungal detection and differentiation fields. These molecular based detection techniques are effective in detecting symptomatic and asymptomatic diseases of both culturable and unculturable fungal pathogens in sole and co-infections. Even though the molecular diagnostic approaches have expanded substantially in the recent past, there is a long way to go in the development and application of molecular diagnostics in plant diseases. Molecular techniques used in plant disease diagnostics need to be more reliable, faster, and easier than conventional methods. Now the challenges are with scientists to develop practical techniques to be used for molecular diagnostics of plant diseases. Recent advancement in the improvement and application of molecular methods for diagnosing the widespread and emerging plant pathogenic fungi are discussed in this review.

A Pathogen Secreted Protein as a Detection Marker for Citrus Huanglongbing

The citrus industry is facing an unprecedented crisis due to Huanglongbing (HLB, aka citrus greening disease), a bacterial disease associated with the pathogen Candidatus Liberibacter asiaticus (CLas) that affects all commercial varieties. Transmitted by the Asian citrus psyllid (ACP), CLas colonizes citrus phloem, leading to reduced yield and fruit quality, and eventually tree decline and death. Since adequate curative measures are not available, a key step in HLB management is to restrict the spread of the disease by identifying infected trees and removing them in a timely manner. However, uneven distribution of CLas cells in infected trees and the long latency for disease symptom development makes sampling of trees for CLas detection challenging. Here, we report that a CLas secreted protein can be used as a biomarker for detecting HLB infected citrus. Proteins secreted from CLas cells can presumably move along the phloem, beyond the site of ACP inoculation and CLas colonized plant cells, thereby increasing the chance of detecting infected trees. We generated a polyclonal antibody that effectively binds to the secreted protein and developed serological assays that can successfully detect CLas infection. This work demonstrates that antibody-based diagnosis using a CLas secreted protein as the detection marker for infected trees offers a high-throughput and economic approach that complements the approved quantitative polymerase chain reaction-based methods to enhance HLB management programs.