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. 2015 Jun 17;7(6):3130-54.
doi: 10.3390/v7062763.

Evaluation of Signature Erosion in Ebola Virus Due to Genomic Drift and Its Impact on the Performance of Diagnostic Assays

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Evaluation of Signature Erosion in Ebola Virus Due to Genomic Drift and Its Impact on the Performance of Diagnostic Assays

Shanmuga Sozhamannan et al. Viruses. .

Abstract

Genome sequence analyses of the 2014 Ebola Virus (EBOV) isolates revealed a potential problem with the diagnostic assays currently in use; i.e., drifting genomic profiles of the virus may affect the sensitivity or even produce false-negative results. We evaluated signature erosion in ebolavirus molecular assays using an in silico approach and found frequent potential false-negative and false-positive results. We further empirically evaluated many EBOV assays, under real time PCR conditions using EBOV Kikwit (1995) and Makona (2014) RNA templates. These results revealed differences in performance between assays but were comparable between the old and new EBOV templates. Using a whole genome approach and a novel algorithm, termed BioVelocity, we identified new signatures that are unique to each of EBOV, Sudan virus (SUDV), and Reston virus (RESTV). Interestingly, many of the current assay signatures do not fall within these regions, indicating a potential drawback in the past assay design strategies. The new signatures identified in this study may be evaluated with real-time reverse transcription PCR (rRT-PCR) assay development and validation. In addition, we discuss regulatory implications and timely availability to impact a rapidly evolving outbreak using existing but perhaps less than optimal assays versus redesign these assays for addressing genomic changes.

Keywords: BioVelocity; EBOV; PSET; WGS; Western African outbreak; qRT-PCR; signature erosion.

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Figures

Figure 1
Figure 1
Genome maps of five members of the Ebolavirus genus (drawn to scale). The various genes (green arrows) and the location of the PCR amplicons of the current assays (purple arrows), and the BioVelocity generated signatures (blue arrows) are marked. The numbers of the purple arrows correspond to the assay numbers described in Table S1 and those of the blue arrows correspond to sequences described in Table S4.
Figure 2
Figure 2
Heat map of assay and amplicon hits for each assay based on percentage mismatches between the reference and various GenBank sequences. The various assays, numbered in the same order as in Table S1, are grouped according to specificity of the assay; i.e., EBOV (1–17), BDBV (19–20), TAFV (21–22), RESTV (23–26), and SUDV (27–30). The GenBank accession numbers are provided on the left, and the corresponding ebolaviruses and isolation countries are labeled on the right. The country of isolation is based on the information provided in GenBank entries. Country codes: AGO-Angola; CIV-Ivory Coast; COD-Democratic Republic of Congo; COG-Congo; GAB-Gabon; GBR-United Kingdom; GIN-Guinea; LBR-Liberia; PHL-Philippines; RUS-Russian Federation; SLE-Sierra Leone; SUD-Sudan; UGA-Uganda; UNK-Unknown; USA-United States of America; ZAR-South Africa. Assays/amplicon hits that match at 100/100 rule are in green, and others with various percent mismatches are color coded according to the scale at the bottom. (A) represents the sensitivity (color) and specificity (species) of true-positive hits to the respective assays; (B) represents the sensitivity (color) and lack of specificity (cross reactivity) to the respective assays. False-positive (#10, #16), true-negatives (#4, #24, #27). Note that the heat index scale is different in panels A and B.
Figure 3
Figure 3
SYBR Green RT-PCR assay results as a function of pfu/mL vs. Cq (cycle of quantitation). Each individual assay was run with a 1:10 serial dilution of either EBOV Kikwit (A) or EBOV Makona (B). Each PCR was run in triplicates, and the lowest virus concentration indicated is the lowest virus dilution that still resulted in all three replicates being called positive. For each assay, a sample was considered positive if the Cq value was less than the average of two no template controls. The error bars indicate the standard deviation of the mean.
Figure 4
Figure 4
Various ebolavirus-specific Conserved Sequence Domains (CSD) vs. Signature Sequence Domains (SSD) identified using BioVelocity. The bars are color coded according to the k-mer size.
Figure 5
Figure 5
Heat map of percentage identity of BioVelocity signature hits in various ebolaviruses. The numbers on the x-axis represent species-specific signature IDs (EBOV: 1–10; SUDV: 11–25; RESTV: 26–41). The number of hits for each species for any given signature is given in Table S5. As expected the species-specific signatures are highly conserved in their respective species (indicated by green) compared to other species. The heat index on the left shows the percentage identity.

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