scholarly journals Development and validation of a one-step reverse transcription loop-mediated isothermal amplification (RT-LAMP) for rapid detection of ZIKV in patient samples from Brazil

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Severino Jefferson Ribeiro da Silva ◽  
Keith Pardee ◽  
Udeni B. R. Balasuriya ◽  
Lindomar Pena

AbstractWe have previously developed and validated a one-step assay based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) for rapid detection of the Zika virus (ZIKV) from mosquito samples. Patient diagnosis of ZIKV is currently carried out in centralized laboratories using the reverse transcription-quantitative polymerase chain reaction (RT-qPCR), which, while the gold standard molecular method, has several drawbacks for use in remote and low-resource settings, such as high cost and the need of specialized equipment. Point-of-care (POC) diagnostic platforms have the potential to overcome these limitations, especially in low-resource countries where ZIKV is endemic. With this in mind, here we optimized and validated our RT-LAMP assay for rapid detection of ZIKV from patient samples. We found that the assay detected ZIKV from diverse sample types (serum, urine, saliva, and semen) in as little as 20 min, without RNA extraction. The RT-LAMP assay was highly specific and up to 100 times more sensitive than RT-qPCR. We then validated the assay using 100 patient serum samples collected from suspected cases of arbovirus infection in the state of Pernambuco, which was at the epicenter of the last Zika epidemic. Analysis of the results, in comparison to RT-qPCR, found that the ZIKV RT-LAMP assay provided sensitivity of 100%, specificity of 93.75%, and an overall accuracy of 95.00%. Taken together, the RT-LAMP assay provides a straightforward and inexpensive alternative for the diagnosis of ZIKV from patients and has the potential to increase diagnostic capacity in ZIKV-affected areas, particularly in low and middle-income countries.

Author(s):  
Matthew A Lalli ◽  
Joshua S Langmade ◽  
Xuhua Chen ◽  
Catrina C Fronick ◽  
Christopher S Sawyer ◽  
...  

Abstract Background Rapid, reliable, and widespread testing is required to curtail the ongoing COVID-19 pandemic. Current gold-standard nucleic acid tests are hampered by supply shortages in critical reagents including nasal swabs, RNA extraction kits, personal protective equipment, instrumentation, and labor. Methods To overcome these challenges, we developed a rapid colorimetric assay using reverse-transcription loop-mediated isothermal amplification (RT-LAMP) optimized on human saliva samples without an RNA purification step. We describe the optimization of saliva pretreatment protocols to enable analytically sensitive viral detection by RT-LAMP. We optimized the RT-LAMP reaction conditions and implemented high-throughput unbiased methods for assay interpretation. We tested whether saliva pretreatment could also enable viral detection by conventional reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Finally, we validated these assays on clinical samples. Results The optimized saliva pretreatment protocol enabled analytically sensitive extraction-free detection of SARS-CoV-2 from saliva by colorimetric RT-LAMP or RT-qPCR. In simulated samples, the optimized RT-LAMP assay had a limit of detection of 59 (95% confidence interval: 44–104) particle copies per reaction. We highlighted the flexibility of LAMP assay implementation using 3 readouts: naked-eye colorimetry, spectrophotometry, and real-time fluorescence. In a set of 30 clinical saliva samples, colorimetric RT-LAMP and RT-qPCR assays performed directly on pretreated saliva samples without RNA extraction had accuracies greater than 90%. Conclusions Rapid and extraction-free detection of SARS-CoV-2 from saliva by colorimetric RT-LAMP is a simple, sensitive, and cost-effective approach with broad potential to expand diagnostic testing for the virus causing COVID-19.


Aquaculture ◽  
2019 ◽  
Vol 507 ◽  
pp. 35-39 ◽  
Author(s):  
Theerawut Phusantisampan ◽  
Puntanat Tattiyapong ◽  
Palita Mutrakulcharoen ◽  
Malinee Sriariyanun ◽  
Win Surachetpong

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