Back

Development of loop-mediated isothermal amplification (LAMP) assay for detection of Pseudocercospora angolensis in sweet orange

Driciru, P.; Mugasa, C. M.; Acidri, R.; Adriko, J.

2021-01-13 microbiology
10.1101/2021.01.13.426516 bioRxiv
Show abstract

Pseudocercospora angolensis is the causative agent of Pseudocercospora leaf and fruit spot disease in citrus which can result in up to 100% yield loss. Early diagnosis of this disease is vital for effective control. This study aimed at developing a loop-mediated amplification (LAMP) system for detecting P. angolensis in sweet oranges in comparison with Polymerase Chain Reaction (PCR) and using microscopy as a gold standard. Twelve non-target species were used to assess the analytical specificity of LAMP and PCR whereas the analytical sensitivity was determined using serial dilutions of P. angolensis DNA. The diagnostic accuracies of the two assays were evaluated using DNA from 150 diseased and 50 non-diseased sweet orange leaf samples. The analytical sensitivity and detection time of LAMP were of 10-4 ng/ l and 40 minutes, respectively. The analytical sensitivity of PCR was 10ng/l and it was specific to P. angolensis whereas three relatives of P. angolensis were detectable by LAMP. The diagnostic sensitivities of LAMP (93%) and microscopy (100%) were significantly different (X2 = 8.38, P = 0.0038) unlike the diagnostic specificities (90%) and (100%), respectively (X2 = 3.37, P = 0.066). Microscopy was significantly more sensitive than PCR (32.6%) (X2 = 149.26, P < 2.2e-16) and equally specific as PCR (P=NA). The positive predictive values of PCR and LAMP were 100% and 96.5% respectively whereas the negative predictive values were 33.1% and 81.8% respectively. The LAMP assay developed in this study offers a great tool for routine screening sweet orange samples for P. angolensis.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.