1. GoPrime: In silico evaluation of rRT-PCR primers and probes for detection of foot-and-mouth disease virus
Emma Howson PhD Student, Vesicular Disease Reference Laboratory Group, The Pirbright Institute
Hello, I’m Emma Howson, 3rd year PhD student at The Pirbright Institute, within the Vesicular Reference Laboratory. Main aspects PhD is to develop molecular tools for foot and mouth disease, large part of which is evaluation of these assays. Today I’m going to be talking about chapter of my PhD (Go Prime) – which is a collaborative project between Pirbright and The University of Glasgow, to see whether it is possible to accurately evaluate PCR primers and probes in silico.

2. The Problem: High reliance on rRT-PCR
L VP2 VP VP B C A C D poly(A)
VP A VPg(s)
VPg
rRT-PCR is now a routine methodology for detection of FMDV in diagnostic laboratories
Become such a standardised technique because it is simple to run, sensitive, reliable and also high-throughput.
Current established pan-specific PCR assays, including those describes by Callahan and Reid, target highly conserved areas of the FMDV genome, within the 3D polymerase region and also 5’ UTR.
Used for routine diagnostic purposes to check for the presence of the viral RNA.

3. The Problem: High reliance on rRT-PCR
UK 2007 FMDV outbreak
n = 301
n = 257
Due to these advantages, there is now an increasing reliance on molecular testing. For example, during the 2007 UK FMDV outbreak, rRT-PCR was widely used to test submitted material. Of 3246 samples submitted, 99.1% of these were tested by rRT-PCR – only 21.8% by virus isolation (which is still considered the gold-standard technique) due to it being more time-consuming.
However, in using these rRT-PCR tests as assumption is made that the tests remain fit for purpose and that the primers and probes still accurately detect all FMDV strains. When actually, these tests have not been re-evaluated since their initial creation on any of the new strains. Due to the high reliance of these tests as an emergency response, and the costs associated with calling false negatives in outbreak scenarios, there is an increasing need to revaluate these tests or develop new assays. Number of reasons this has not been done:
Time, money, laborious nature and hassle of the task – large number of samples been used to evaluate these tests – hundreds.
Also - access to suitable recent samples – limited number of laboratories which would an extensive enough archive of up to date samples to test.
n = 130
Re-evaluation of rRT-PCR primers?
Time / money
Access to suitable samples

4. The Problem: Mismatching of rRT-PCR primers and probes
(EA = 27)
L VP2 VP VP B C A C D poly(A)
VP A VPg(s)
VPg
Why re-evaluate?
This is not only relevant for existing assays, but also the development of new assays.
Take days to design new sets of primers and probes, but months to evaluate them.
This can be especially true when considering assays to differentiate between serotypes (usually region specific), which are now being designed as they are often more informative than pan-specific assays. These target more highly variable regions such as the VP1 coding region e.g. Kasia (East Africa) and Reid (Middle East).
For instance, two examples of typing assays: By Kasia and Reid – used 356 and 98 samples respectively, with 27 of Kasia’s required testing in East Africa in order to get access to them. Not just these numbers – done in minimum duplicate, with numerous primer sets for each serotype. For instance, Reid – equivalent of over 3000 assays.
So why do we need to revaluate primers? The high variation within the FMDV genome (especially within these regions) and continuing evolution of FMDV, means that primers/probes rarely match all strains/serotypes perfectly, there will be some level of mismatch present.
This is important as Ct value is reflective of not only the amount of virus present in the sample, but also the number of mismatches present between the primer/probe and template sequences. If these mismatches are present – we don’t know whether there is either a low amount of virus in the sample, degraded sample or alternatively the assay is performing badly based on the mismatches. Could even get false negatives – especially when the amount of virus in the sample is low (for instance in OP fluid in carrier animals, at the start and end of viremia).
Therefore there is a pressing need to constantly re-evaluate the performance of these tests.
If this evaluation was going to be performed, people are restricted to either laborious laboratory screening as mentioned earlier, or bioinformatics approaches such as BLAST searches which are not that informative – they don’t quantify how the mismatches will affect the test.

5. The Solution:
Design an in silico program to quantitatively predict the performance of rRT-PCR
Investigate the influence of specific mismatches on rRT-PCR CT and efficiency
Design a bioinformatics program to predict rRT-PCR performance in silico based on empirical data
Validate the program using naturally occurring mutations for FMDV
Stage 1 - PCR targets (minus RT) Callahan 3D
90 linear DNA constructs
Changes represented along the entire length of target regions
Maximum of 6 base mismatches designed per primer
PCRs performed using Callahan primers and probes
Starting template: 106 – 100
Compared to a perfect match template
The solution was therefore to design an in silico program to enable researchers to rapidly evaluate the performance of new/existing rRT-PCR assays. To reduce the reliance on testing thousands of laboratory samples.
-> To do this we had 3 aims:
Investigate the influence of specific mismatches on rRT-PCR. There is a multitude of quantitative data looking at how specific mutations in template affect the ct of assays. However to date this is limited considering the quantity of starting template tested and also in terms of enzyme kits evaluated.
Design a bioinformatics program to predict rRT-PCR performance in silico based on this empirical data.
Validate the program with real data from naturally occurring mutations to see how well it predicts changes in CT.
To do this our starting point was to look at the effects of these mismatches on PCR (at this stage not looking at the RT step)
Therefore we designed and ordered 90 DNA constructs, which were based the Callahan target for FMDV in 3D (diagnostic laboratory PCR). These were designed to vary in the primer and probe binding regions, with mismatches designed across the entire length of the primers/probes. A perfect match template was also made for comparison. These were then diluted to numerous starting quantities (10^1 and 10^6) and used as template in real-time PCR.

6. Comparison between enzyme kits
Results 1:
Comparison between enzyme kits VS
To begin, we looked at how different PCR kits responded differently to these mismatches. The increasing use of real-time PCR has led to the growing availability of commercial real-time PCR kits.
To date – no study has looked at how this different kits respond to mismatches in primer/probe sequences.
In this study, two commercial Taq polymerase-based kits were compared: ExciteTM UF 2x Master Mix and SuperScript® III One-Step RT-PCR System (this is the kit used for diagnostics, however RT step was omitted)
Each point on graph represents a DNA construct, with axis showing the difference in Ct from the perfect match.
Strong linear relationship between the two kits - general trend that the more differences in the primer lead to increased range of differences both in the enzyme and between enzymes.
However, based on this data we decided to use both sets of data for analysis – to try and ascertain the effects of mismatches on Taq polymerase based kits.
 Quantig
Excite Fast

7. Effect of mismatches on CT
Results 2:
Effect of mismatches on CT
Correct pairing:
Transition mismatch:
Transversion mismatch:
Once all the data for all 90 template across different dilutions and enzymes had been collected - a linear model (using R) was used to look at the effect of mismatches. We did this by looking at the how each specific mismatch affected the CT of a reaction.
All mismatches were split into either transition (still led to pairing of a purine and pyrimidine base – similar conformation) or transversion (where either purine-purine or pyrimidine-pyrimidine pair, therefore giving a drastically altered conformation).
This graph represents the changes made at the 3’ end of primers. As expected, changes closer to the 3’ end of the primer had a larger effect on Ct, with transversions worse. Transversion at the very 3’ end resulted in an effect of 6 Ct on the reaction, equivalent to underestimation of viral content by two logs. Which could move a positive sample over the PCR cut off – increasing the chance of wrongly calling a doubtful or negative result.
Transition had effect of 3 ct – equivalent in terms of quantity of one log. Therefore a single mutation at 3’ end may lead to underestimations of viral content by one log.
A maximum of 2 mismatches could be tolerated in the 3’ end, before the PCR was knocked out completely.
82% match across the primers was required for any amplification to occur.
Mismatches in 3’-end (nucleotides 1-4) had the most pronounced effect
A maximum of 2 mismatches could be tolerated at the 3’-end of primers
A minimum of 82% match across primers is necessary for any amplification

8. Mismatches did not impact PCR efficiency (linear regression)
Results 3:
Mismatches did not impact PCR efficiency (linear regression)
Because we looked at each template at various starting concentrations, also enabled us to look at the effect of mutations on PCR efficiency.
Linear regression analysis was used to compare the efficiency gradients, between perfectly matched and mismatched targets.
No significant differences were evident between different templates.
This has been previously reported, and we believe is due to the fact that once a primer extension has occurred from a target, the resultant amplicon will contain the primer sequence, and therefore be a perfect match to the primers. Amplification can therefore continue as normal. Mismatch therefore only effects the first few cycles.
Top graph here shows changes to the 3’ end (top line showing a perfect match), lower shows general % change to primer and probe sequences. Changes can be seen with the Ct of the reaction, however gradient of the line is similar for all.
The impact of mismatches upon the CT of PCR reactions was predictable
Mismatches only effect the first few cycles of PCR

9. Design of the Program
Program was built based upon the previous assumptions, by Richard Orton at the University of Glasgow using Java.
Don’t need to worry about data in the table – however illustrates the output of the linear model.
Based on our experiments, we split the mismatches into different variables, linear model was used to quantify each of these variables, to look at the effect the variable (for instance, per 1% mismatch in the primer, or per specific mismatch at the 3’ end). And it was this data that went into the model.
Using this program: the user inputs primer/probe sequences, also sequences they wish to target. The program then scans the target sequences for the likely binding sites, calculates the likely effect that the mismatches present will have on Ct value. Will then output which samples the assay will likely detect and also quantify this in terms of effect on Ct.
This quantification is really important for the researcher, as dependent upon the sample used, for instance epi (high amount of virus) vs probang (low amount of virus), there will be different number of mismatched that will be allowed to be tolerated.

10. Preliminary Testing of the Program Using Naturally Occurring Sequence Variability
However, we still needed to test that this model was correct:
In order to test the model, found FMDV sequences on NCBI that differed in the 3D Callahan primer / probe region, these again were ordered as DNA constructs.
Used the model to predict the change in Ct and compared this to laboratory data collected using both of the enzymes.
Model predicted all of the sequences within +/- 2 Ct (in same log).
Really promising result – however further testing is still required on real FMDV samples.

11. Summary and Impacts
Creation of a quantitative in silico program which can:
Predict the effects of primer/probe – template mismatches on real-time PCR
Quantitatively estimate the performance of primers/probes in a rRT-PCR reaction
Rapidly screen primer/probe sets against sequence data
Enable the researcher to decide whether a primer/probe set is good enough for their use
Quickly inform of when primer/probe sets require redesigning
RT step to be incorporated soon…….
Creation of a quantitative in silico program which can:
Predict the effects of primer/probe – template mismatches on real-time PCR
Quantitatively estimate the performance of primers/probes in a rRT-PCR reaction
Rapidly screen primer/probe sets against sequence data
Enable the researcher to decide whether a primer/probe set is good enough for their use
Quickly inform of when primer/probe sets require redesigning
RT step to be incorporated soon.
At present the model only looks at DNA amplification.
It also suffice for two-step RT-PCR where random primers are used to generate the cDNA – so mismatches in the RNA will be transferred into the cDNA.
Next stage is to incorporate reverse transcription step into the model as most of the RT-PCR reactions we perform are one-step kits, therefore also use the PCR primers for the RT step.
Need to look therefore how these mismatches affect the RT-step, see if RT enzyme reacts differently to mismatches than the polymerase.

12. Co-authors Acknowledgements Veronica Fowler (The Pirbright Institute)
Richard Orton (University of Glasgow)
Donald King (The Pirbright Institute)
Sarah Cleaveland (University of Glasgow)
Valerie Mioulet (The Pirbright Institute)
Tiziana Lembo (University of Glasgow)
WRLFMD staff
Jemma Wadsworth (The Pirbright Institute)
Simon Gubbins (The Pirbright Institute)