An immunochromatographic test strip for on-site detection of Ralstonia solanacearum in tobacco leaves and soil

Recent advances in immuno-based methods for the detection of Ralstonia solanacearum

Ralstonia solanacearum (RS) is a widely recognized phytopathogenic bacterium which is responsible for causing devastating losses in a wide range of economically significant crops. Timely and accurate detection of this pathogen is pivotal to implementing effective disease management strategies and preventing crop losses. This review provides a comprehensive overview of recent advances in immuno-based detection methods for RS. The review begins by introducing RS, highlighting its destructive potential and the need for point-of-care detection techniques. Subsequently, it explores traditional detection methods and their limitations, emphasizing the need for innovative approaches. The main focus of this review is on immuno-based detection methods and it discusses recent advancements in serological detection techniques. Furthermore, the review sheds light on the challenges and prospects of immuno-based detection of RS. It emphasizes the importance of developing rapid, field-deployable assays that can be used by farmers and researchers alike. In conclusion, this review provides valuable insights into the recent advances in immuno-based detection methods for RS.

Loop-mediated isothermal amplification (LAMP)/Cas12a assay for detection of Ralstonia solanacearum in tomato

Introduction: Bacterial wilt (BW) caused by the aerobic, Gram-negative pathogenic species Ralstonia solanacearum (RS) is a major disease impacting commercial agriculture worldwide. Asian phylotype I of RS is the cause of tomato bacterial wilt, which has caused severe economic losses in southern China for many years. An urgent priority in control of bacterial wilt is development of rapid, sensitive, effective methods for detection of RS. Methods: We describe here a novel RS detection assay based on combination of loop-mediated isothermal amplification (LAMP) and CRISPR/Cas12a. crRNA1, with high trans-cleavage activity targeting hrpB gene, was selected out of four candidate crRNAs. Two visual detection techniques, involving naked-eye observation of fluorescence and lateral flow strips, were tested and displayed high sensitivity and strong specificity. Results and Discussion: The LAMP/Cas12a assay accurately detected RS phylotype Ⅰ in 14 test strains, and showed low detection limit (2.0 × 10⁰ copies). RS in tomato stem tissue and soil samples from two field sites with suspected BW infection was identified accurately, suggesting potential application of LAMP/Cas12a assay as point-of-care test (POCT). The overall detection process took less than 2 h and did not require professional lab equipment. Our findings, taken together, indicate that LAMP/Cas12a assay can be developed as an effective, inexpensive technique for field detection and monitoring of RS.

Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae

Plant diseases caused by the pathogen Pseudomonas syringae are serious problems for various plant species worldwide. Accurate detection and diagnosis of P. syringae infections are critical for the effective management of these plant diseases. In this review, we summarize the current methods for the detection and diagnosis of P. syringae, including traditional techniques such as culture isolation and microscopy, and relatively newer techniques such as PCR and ELISA. It should be noted that each method has its advantages and disadvantages, and the choice of each method depends on the specific requirements, resources of each laboratory, and field settings. We also discuss the future trends in this field, such as the need for more sensitive and specific methods to detect the pathogens at low concentrations and the methods that can be used to diagnose P. syringae infections that are co-existing with other pathogens. Modern technologies such as genomics and proteomics could lead to the development of new methods of highly accurate detection and diagnosis based on the analysis of genetic and protein markers of the pathogens. Furthermore, using machine learning algorithms to analyze large data sets could yield new insights into the biology of P. syringae and novel diagnostic strategies. This review could enhance our understanding of P. syringae and help foster the development of more effective management techniques of the diseases caused by related pathogens.