Multi-Target Strategy for Pan/Foot-and-Mouth Disease Virus (FMDV) Detection: A Combination of Sequences Analysis, in Silico Predictions and Laboratory Diagnostic Evaluation
Liliam Rios, Carmen L. Perera, Liani Coronado, Damarys Relova, Ana M. Álvarez, Llilianne Ganges, Heidy Díaz de Arce, José I. Núñez, Lester J. Pérez
Abstract
Foot-and-mouth disease (FMD) is a highly contagious viral disease affecting cloven-hoofed animals that causes severe economic losses. The disease is characterized by a vesicular condition and it cannot be differentiated from other vesicular diseases. Therefore, laboratory confirmation of any suspected FMD case is compulsory. Despite viral isolation in cell cultures has been considered for many years as the gold standard for FMD diagnosis, the advantages of real-time reverse transcription polymerase chain reaction (rRT-PCR) technology have motivated its use directly in clinical specimens for FMD diagnosis. The current work was aimed to develop and validate a molecular multi-check strategy using rRT-PCR (mMulti-rRT-PCR) based on SYBR-Green I for pan/foot-and-mouth disease virus (pan/FMDV) diagnosis. From in silico approaches, different primer pairs previously reported were selected and modified to reduce the likelihood of viral escape as well as potential failures in the pan/FMDV detection. The analytical parameters were evaluated using a high number of representative viral strains. The repeatability of the assay and its performance on field samples were also assessed. The mMulti-rRT-PCR was able to detect emergent FMDV strains that circulated in South America between the years 2006-2010 and on which the single rRT-PCRs failed when they were applied independently. The results obtained here showed that the proposed system is an accurate and rapid diagnosis method for sensitive and specific detection of FMDV. Thus, a validated mMulti-rRT-PCR assay based on SYBR-Green I detection coupled to melting curves resolution for pan/FMDV diagnosis on clinical samples is proposed. This study also highlights the need to incorporate the multi-target detection principle in the diagnosis of highly variable agents, specially, of those listed by OIE like FMDV.
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 September 2018.
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