Molecular Diagnostics in the Mycosphaerella Leaf Spot Disease Complex of Banana and for Radopholus similis

Mycosphaerella leaf spots and nematodes threaten banana cultivation worldwide. The Mycosphaerella disease complex involves three related ascomycetous fungi: Mycosphaerella fijiensis, M. musicola and M. eumusae. The exact distribution of these three species and their disease epidemiology remain unclear, since their symptoms and life cycles are rather similar. Diagnosing these diseases and the respective causal agents is based on the presence of host symptoms and fungal fruiting structures, but is time consuming and not conducive to preventive management. In the present study, we developed rapid and robust species-specific diagnostic tools to detect and quantify M. fijiensis, M. musicola and M. eumusae. Conventional species-specific PCR primers were developed based on the actin gene that detected as little as 100, 1 and 10 pg/µl DNA from, respectively, M. fijiensis, M. musicola and M. eumusae. Furthermore, TaqMan real-time quantitative PCR assays that were developed based on the ß-tubulin gene detected quantities as low as 1 pg/µl DNA of each species from pure cultures and 1.6 pg/µl DNA/mg of M. fijiensis from dry leaf tissue. The efficacy of the tests was validated using naturally infected banana leaves. Similar technology has been used to develop a quantitative PCR assay for the banana burrowing nematode, Radopholus similis, which is currently being validate

Advances in plant disease detection and monitoring: From traditional assays to in-field diagnostics

none7noHuman 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.openBuja I.; Sabella E.; Monteduro A.G.; Chiriaco M.S.; De Bellis L.; Luvisi A.; Maruccio G.Buja, I.; Sabella, E.; Monteduro, A. G.; Chiriaco, M. S.; De Bellis, L.; Luvisi, A.; Maruccio, G

Next-generation methods for early disease detection in crops

: Plant pathogens are commonly identified in the field by the typical disease symptoms that they can cause. The efficient early detection and identification of pathogens are essential procedures to adopt effective management practices that reduce or prevent their spread in order to mitigate the negative impacts of the disease. In this review, the traditional and innovative methods for early detection of the plant pathogens highlighting their major advantages and limitations are presented and discussed. Traditional techniques of diagnosis used for plant pathogen identification are focused typically on the DNA, RNA (when molecular methods), and proteins or peptides (when serological methods) of the pathogens. Serological methods based on mainly enzyme-linked immunosorbent assay (ELISA) are the most common method used for pathogen detection due to their high-throughput potential and low cost. This technique is not particularly reliable and sufficiently sensitive for many pathogens detection during the asymptomatic stage of infection. For non-cultivable pathogens in the laboratory, nucleic acid-based technology is the best choice for consistent pathogen detection or identification. Lateral flow systems are innovative tools that allow fast and accurate results even in field conditions, but they have sensitivity issues to be overcome. PCR assays performed on last-generation portable thermocyclers may provide rapid detection results in situ. The advent of portable instruments can speed pathogen detection, reduce commercial costs, and potentially revolutionize plant pathology. This review provides information on current methodologies and procedures for the effective detection of different plant pathogens. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry

A spectroscopy approach to the study of virus infection in the endophytic fungus Epichloë festucae

<p>Abstract</p> <p>Background</p> <p>In this work we propose a rapid method based on visible and near-infrared (Vis-NIR) spectroscopy to determine the occurrence of double-stranded RNA (dsRNA) viruses in <it>Epichloë festucae </it>strains isolated from <it>Festuca rubra </it>plants. In addition, we examined the incidence of infections by <it>E. festucae </it>in populations of <it>F. rubra </it>collected in natural grasslands of Western Spain.</p> <p>Methods</p> <p>Vis-NIR spectra (400-2498 nm) from 124 virus-infected and virus-free <it>E. festucae </it>isolates were recorded directly from ground and freeze-dried mycelium. To estimate how well the spectra for uninfected and infected fungal samples could be differentiated, we used partial least-squares discriminant analysis (PLS1-DA) and several data pre-treatments to develop calibration models.</p> <p>Results</p> <p>Applying the best regression model, obtained with two sampling years and using standard normal variate (SNV) combined with first derivative transformation to a new validating data set (42 samples), we obtained a correct classification for 75% of the uninfected isolates and up to 86% of the infected isolates.</p> <p>Conclusions</p> <p>The results obtained suggest that Vis-NIR spectroscopy is a promising technology for detection of viral infections in fungal samples when an alternative faster approach is desirable. It provides a tool adequately exact and more time- and cost-saving than the conventional reference analysis.</p

NANOTECHNOLOGY FOR DETECTION OF DISEASES CAUSED BY VIRUSES-CURRENT OVERVIEW

Nanotechnology is having a high impact on the development of a novel class of biosensors called nanobiosensors. This technology has utilized some extremely exciting elements for sensing phenomenon improvement. The utilization of nano-materials, nano-rods, nano-particles, nano-tubes have aided rapid, reliable reproducibility and its detection in a much better way. The unique properties of nanobiosensors and its varied applications have influenced biosensing research. Since longtime, nanobiosensors have been utilized worldwide for the diagnosis of diseases co-related with molecular detection of biomarkers. This paper highlights the use of such nanobiosensors for the detection of the virus, infections, fungal pathogens, Human Immunodeficiency Virus (HIV) related diseases such as Cardiovascular diseases (CDVs), Renal Arthritis (RA) through different techniques including electrochemical biosensing, optical biosensing, point of care-diagnostics etc

Current and emerging molecular technologies for the diagnosis of plant diseases – An overview

Plant diseases caused by numerous pathogens such as bacteria, viruses, and fungi are responsible for substantial economic losses in the agricultural industry worldwide. Specific, sensitive, and efficient diagnostic tools have been developed worldwide to mitigate and prevent the pathogenic threat. The diagnostic tools have revolutionized from classical methods to more advanced molecular diagnostic approaches such as enzyme-linked immunosorbent assay (ELISA), conventional polymerase chain reaction (PCR), real-time PCR, loop-mediated isothermal amplification (LAMP), biosensor, and next-generation sequencing (NGS). Hence, this review describes the current and emerging molecular diagnostic tools to distinguish and identify pathogens in crops

Sensors and biosensors for pathogen and pest detection in agricultural systems : recent trends and oportunities

Pathogen and pest-linked diseases across agriculture and ecosystems are a major issue towards enhancing current thresholds in terms of farming yields and food security. Recent developments in nanotechnology allowed the designing of new generation sensors and biosensors in order to detect and mitigate these biological hazards. However, there are still important challenges concerning its respective applications in agricultural systems, typically related to point-of-care testing, cost reduction and real-time analysis. Thus, an important question arises: what are the current state-of-the-art trends and relationships among sensors and biosensors for pathogen and pest detection in agricultural systems? Targeted to meet this gap, a comparative study is performed by a literature review of the past decade and further data mining analysis. With the majority of the results coming from recent studies, leading trends towards new technologies were reviewed and identified, along with its respective agricultural application and target pathogens, such as bacteria, viruses, fungi, as well as pests like insects and parasites. Results have indicated lateral flow assay, lab-on-a-chip technologies and infrared thermography (both fixed and aerial) as the most promising categories related to sensors and biosensors driven to the detection of several different pathogenic varieties. The main existing interrelations between the results are especially associated to cereals, fruits and nuts, meat and dairy along with vegetables and legumes, mostly caused by bacterial and fungal infections. Additional results also presented and discussed, providing a fertile groundwork for decision-making and further developments in modern smart farming and IoT-based agriculture