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Data · dataset · 2026

Performance of a Group II Intron Reverse Transcriptase from Anoxybacillus flavithermus in Duplex RT-LAMP for SARS-CoV‑2 Detection

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Reverse transcriptases from bacterial group II introns (GII RTs) demonstrate superior thermostability and processivity compared with retroviral counterparts.

Description

While GII RTs have proven advantageous for RNA-seq, their performance in other applications, such as reverse transcription loop-mediated isothermal amplification (RT-LAMP), remains poorly studied. RT-LAMP is a widely used method suitable for point-of-care diagnostics of RNA viruses and other pathogens.

In this study, we compared the performance in RT-LAMP of GII RT from the thermophilic bacterium Anoxybacillus flavithermus (Afl RT) with that of the retroviral RNase H-deficient reverse transcriptase M-MuLV. A duplex RT-LAMP targeting SARS-CoV-2 and MS2 phage genomic RNA (internal control) served as a model. Preheating at 95 °C and 1.5 M betaine increased the efficiency of RT-LAMP with both reverse transcriptases.

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Under these conditions, the optimal temperature range and time of reverse transcription were similar for both enzymes, 55–60 °C and 15 min, respectively. However, high concentrations of Afl RT (>50 U/reaction) inhibited RT-LAMP. Notably, RT-LAMP with Afl RT demonstrated higher sensitivity for detecting both in vitro-transcribed RNA fragments and SARS-CoV-2–positive samples.

In validation against RT-qPCR using nasal swabs (200 SARS-CoV-2-positive and 200 negative samples) from patients with respiratory symptoms, the observed sensitivity of RT-LAMP with Afl RT was 100% (200/200; 95% CI, 98.17–100%), whereas with M-MuLV RT, sensitivity was 97.0% (194/200; 95% CI, 93.58–98.89%). Specificity was 100% for both RTs (200/200; 95% CI, 98.17–100%). These observations demonstrate that Afl RT is suitable for RT-LAMP and may increase the sensitivity of LAMP-based diagnostics.

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RNA sequencing 65%
Provenance · 1 source records, 15 field assertions
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ZivaHuboai:figshare.com:article/3402741512 d agoJSON v1
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