Development of an Indirect ELISA and Dot-Blot Assay for Serological Detection of Rice Tungro Disease

Novel plant disease detection techniques-a brief review

Plant pathogens cause severe losses to agricultural yield worldwide. Tracking plant health and early disease detection is important to reduce the disease spread and thus economic loss. Though visual scouting has been practiced from former times, detection of asymptomatic disease conditions is difficult. So, DNA-based and serological methods gained importance in plant disease detection. The progress in advanced technologies challenges the development of rapid, non-invasive, and on-field detection techniques such as spectroscopy. This review highlights various direct and indirect ways of detecting plant diseases like Enzyme-linked immunosorbent assay, Lateral flow assays, Polymerase chain reaction, spectroscopic techniques and biosensors. Although these techniques are sensitive and pathogen-specific, they are more laborious and time-intensive. As a consequence, a lot of interest is gained in in-field adaptable point-of-care devices with artificial intelligence-assisted pathogen detection at an early stage. More recently computer-aided techniques like neural networks are gaining significance in plant disease detection by image processing. In addition, a concise report on the latest progress achieved in plant disease detection techniques is provided.

Introgression of tsv1 improves tungro disease resistance of a rice variety BRRI dhan71

Rice Tungro disease poses a threat to rice production in Asia. Marker assisted backcross breeding is the most feasible approach to address the tungro disease. We targeted to introgress tungro resistance locus tsv1 from Matatag 1 into a popular but tungro susceptible rice variety of Bangladesh, BRRI dhan71. The tsv1 locus was traced using two tightly linked markers RM336 and RM21801, and background genotyping was carried out using 7 K SNPs. A series of three back crosses followed by selfing resulted in identification of plants similar to BRRI dhan71. The background recovery varied at 91-95% with most of the lines having 95%. The disease screening of the lines showed moderate to high level of tungro resistance with a disease index score of ≤ 5. Introgression Lines (ILs) had medium slender grain type, and head rice recovery (59.2%), amylose content (20.1%), gel consistency (40.1 mm) and gelatinization temperature were within the acceptable range. AMMI and Kang's stability analysis based on multi-location data revealed that multiple selected ILs outperformed BRRI dhan71 across the locations. IR144480-2-2-5, IR144483-1-2-4, IR144484-1-2-2 and IR144484-1-2-5 are the most promising lines. These lines will be further evaluated and nominated for varietal testing in Bangladesh.

Ultrasensitive nano-gold labelled, duplex lateral flow immunochromatographic assay for early detection of sugarcane mosaic viruses

Sugarcane is one of the important food and bioenergy crops, cultivated all over the world except European continent. Like many other crops, sugarcane production and quality are hampered by various plant pathogens, among them viruses that infect systemically and cause severe impact to cane growth. The viruses are efficiently managed by their elimination through tissue culture combined with molecular diagnostics, which could detect virus titre often low at 10^-12 g mL^-1. To harmonize the virus diagnostics by molecular methods, we established a nanocatalysis-based high sensitive lateral flow immunochromatographic assay (LFIA) simultaneously to detect two major sugarcane viruses associated with mosaic disease in sugarcane. LFIA is known for poor sensitivity and stability with its signalling conjugates. However, we synthesized positively charged Cysteamine-gold nanoparticles and used them to prepare highly stable to sensitive immunoconjugates and as a colourimetric detection label. Further nanogold signal enhancement was performed on LFIA to obtain a high detection sensitivity, which is higher than the conventional immunoassays. The linear detection range of the nano-LIFA was 10^-6 to 10^-9 g mL^-1, and with the signal enhancement, the LOD reached up to 10^-12 g ml^-1. This research paper provides relative merits and advancement on nano-LFIA for specific detection of sugarcane viruses in sugarcane for the first time.

Salivary GFAP as a potential biomarker for diagnosis of mild cognitive impairment and Alzheimer's disease and its correlation with neuroinflammation and apoptosis

Glial fibrillary acidic protein (GFAP) is the main constituent of the astrocytic cytoskeleton, overexpressed during reactive astrogliosis-a hallmark of Alzheimer's Disease (AD). GFAP and established biomarkers of neurodegeneration, inflammation, and apoptosis have been determined in the saliva of amnestic-single-domain Mild Cognitive Impairment (MCI) (Ν = 20), AD (Ν = 20) patients, and cognitively healthy Controls (Ν = 20). Salivary GFAP levels were found significantly decreased in MCI and AD patients and were proven an excellent biomarker for discriminating Controls from MCI or AD patients. GFAP levels correlate with studied biomarkers and Aβ₄₂, IL-1β, and caspase-8 are its main predictors.

Pixel-Based Machine Learning and Image Reconstitution for Dot-ELISA Pathogen Diagnosis in Biological Samples

Serological methods serve as a direct or indirect means of pathogen infection diagnosis in plant and animal species, including humans. Dot-ELISA (DE) is an inexpensive and sensitive, solid-state version of the microplate enzyme-linked immunosorbent assay, with a broad range of applications in epidemiology. Yet, its applicability is limited by uncertainties in the qualitative output of the assay due to overlapping dot colorations of positive and negative samples, stemming mainly from the inherent color discrimination thresholds of the human eye. Here, we report a novel approach for unambiguous DE output evaluation by applying machine learning-based pattern recognition of image pixels of the blot using an impartial predictive model rather than human judgment. Supervised machine learning was used to train a classifier algorithm through a built multivariate logistic regression model based on the RGB ("Red," "Green," "Blue") pixel attributes of a scanned DE output of samples of known infection status to a model pathogen (Lettuce big-vein associated virus). Based on the trained and cross-validated algorithm, pixel probabilities of unknown samples could be predicted in scanned DE output images, which would then be reconstituted by pixels having probabilities above a cutoff. The cutoff may be selected at will to yield desirable false positive and false negative rates depending on the question at hand, thus allowing for proper dot classification of positive and negative samples and, hence, accurate diagnosis. Potential improvements and diagnostic applications of the proposed versatile method that translates unique pathogen antigens to the universal basic color language are discussed.

Rhodopseudomonas palustris Quorum Sensing Molecule pC-HSL Induces Systemic Resistance to TMV Infection via Upregulation of NbSIPK/NbWIPK Expressions in Nicotiana benthamiana

G-negative bacteria produce myriad N-acyl-homoserine lactones (AHLs) that can function as quorum sensing (QS) signaling molecules. AHLs are also known to regulate various plant biological activities. p-Coumaroyl-homoserine lactone (pC-HSL) is the only QS molecule produced by a photosynthetic bacterium, Rhodopseudomonas palustris. The role of pC-HSL in the interaction between R. palustris and plant has not been investigated. In this study, we investigated the effect of pC-HSL on plant immunity and found that this QS molecule can induce a systemic resistance to Tobacco mosaic virus (TMV) infection in Nicotiana benthamiana. The results show that pC-HSL treatment can prolong the activation of two mitogen-associated protein kinase genes (i.e., NbSIPK and NbWIPK) and increase the expression of transcription factor WRKY8 as well as immune response marker genes NbPR1 and NbPR10, leading to an increased accumulation of reactive oxygen species (ROS) in the TMV-infected plants. Our results also show that pC-HSL treatment can increase activities of two ROS-scavenging enzymes, peroxidase and superoxide dismutase. Knockdown of NbSIPK or NbWIPK expression in N. benthamiana plants through virus-induced gene silencing nullified or attenuated pC-HSL-induced systemic resistance, indicating that the functioning of pC-HSL relies on the activity of those two kinases. Meanwhile, pC-HSL-pretreated plants also showed a strong induction of kinase activities of NbSIPK and NbWIPK after TMV inoculation. Taken together, our results demonstrate that pC-HSL treatment increases plant resistance to TMV infection, which is helpful to uncover the outcome of interaction between R. palustris and its host plants.