Development of a Quantification Method to SpecificAnti-NS3 Antibodies against BVDV using a Blocking ELISAStephan Guillossou1,2, Daniel Thomson1, Cindy Thomson2
1Kansas State University, College of Veterinary Medicine, Department of Clinical Science, Manhattan, KS, USA; 2Synbiotics Corporation, Manhattan, KS, USA;
Introduction
Controlling BVDV
Identify the best cost effective model
European epidemiological models for BVDV control
Identify herds harboring PI animals (sentinel animals)
Inside these herds, Identify and Remove PI animals
Difficulties to adapt in area with use of vaccination
needs differentiation between vaccinated induced Abs and virus infection induced Abs(Presence of PI animal)
BVD Ab
Introduction
Envelop
External glycoproteinStrain variability
(Except Erns: better stability
but not perfect!)Important neutralizing properties
(Mainly for the gp53 = E2)
Non Structural proteins
ex: NS 2-3 (p80/125)
Highly antigenic without generating immunity( no protection)
Highly conserved among strainsProduction occurs during viral replication
inside the cell
Capsidproteininternalweak variabilityNon protected Ab
BVD Ab
Objective
Sero Neutralizing Test (SNT) measures only antibodies with seroneutralizing properties
Western blot are specific to antibody subpopulations
Currently, no existing standardized method to quantify subpopulation of antibodies without SN properties
Objective of the study:
Development of a quantification method to titer anti-NS3 antibody subpopulation
Commercial ELISA
SERELISA® BVD p80 Ab Mono Blocking
Synbiotics® Europe, France
BVD Ab
Diagnostic Ab Mono Blocking ELISA
Negative sample
Positive sample
BVD Ab
Objectives & limits/opportunities
Objectives:
to develop a quantitative serum antibody test for BVDV for a subpopulation of antibodies with a commercially available test
Limits/opportunities:
Blocking ELISA are specific of only a sub population of antibody targeting a specific epitope of an antigen
But blocking ELISA usuallyhave less linearitythan indirect ELISA
BVD Ab
indirect
blocking
Material and Methods 1/2
Samples: positive reference serum (Sbio) was used to establish a reference panel
Labs: Synbiotics Corporation, Manhattan, KS, USA
Kansas State University, College of Veterinary Medicine,Department of Clinical Science, Manhattan, KS, USA
OD results: Results are expressed as a function of OD obtained with the ELISA (SN, SNc, or PI) that includes correction with the controls (positive and negative controls)
Model selection: Eight models have been investigated:
T fn(OD)
1/T fn(1/OD)
T fn[Log(OD)]
T fn[logit(OD)]
LogT fn(OD)
Log(1/T) fn(1/OD)
LogT fn[Log(OD)]
LogT fn[logit(OD)]
p-1
ploglogit p
BVD Ab
Material and Methods 2/2
Methods: Model development and selection
For each of the previous models, graphical and mathematical pertinence of the models are assessed by interpretation of coefficient of determination R2 and residual analysis
Sample dilution protocol:
Reference serum was used at the following dilutions (final dilutions into wells:
1/10, 1/50, 1/100, 1/500, 1/1000, 1/5000, 1/10000Measures were repeated four times
Statistical analysis: R 2.7.2
SPSS for Windows ver.16.0
Excel ver2003
BVD Ab
Results model selection
y = 232.86x - 38.96R² = 0.8842
-50
0
50
100
150
200
250
0.00 0.20 0.40 0.60 0.80 1.00
dilution
sncratio
T=f(OD)y = 184.69x + 154.69
R² = 0.7045
-50
0
50
100
150
200
250
-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20
dilution
Log(sncratio)
T=f(LogOD)
y = 0.7371x + 1.6619R² = 0.9835
0.00
0.50
1.00
1.50
2.00
2.50
-1.00 -0.50 0.00 0.50 1.00 1.50
Log(dilution)
logit(sncratio)
LogT=f(logitOD)
y = 1.4764x + 2.2671R² = 0.9766
0.00
0.50
1.00
1.50
2.00
2.50
-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20
Log(dilution)
Log(sncratio)
LogT=f(LogOD)
BVD Ab
Results model selection
Analysis of coefficient of determination (R2)
T fn(OD) R2=0.8842
1/T fn(1/OD) R2=1
T fn[Log(OD)] R2=0.7045
T fn[logit(OD)] R2=0.9167
LogT fn(OD) R2=0.9899
Log(1/T) fn(1/OD) R2=0.8979
LogT fn[Log(OD)] R2=0.9766
LogT fn[logit(OD)] R2=0.9835
BVD Ab
Results model selection
Analysis of residual dispersion
LogT=f(logitOD)
1.50
2.00
2.50
3.00
3.50
4.00
-2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50logit(s/n ratio)
Log(dilution)LogT=f(LogOD)
1.50
2.00
2.50
3.00
3.50
4.00
-1.60 -1.40 -1.20 -1.00 -0.80 -0.60 -0.40 -0.20 0.00log(s/n ratio)
Log(dilution)
BVD Ab
Results selected model
Best fited model
Best linearity achieved with SNc between 0.11 and 0.93
With:
Slope
Intercept
ab SNclogitTiterlog
0.7371 b
1.6619a
y = 0.7371x + 1.6619R² = 0.9835
0.00
0.50
1.00
1.50
2.00
2.50
-1.00 -0.50 0.00 0.50 1.00 1.50
Log(dilution)
logit(sncratio)
LogT=f(logitOD)
BVD Ab
Results final model
1/10,0001/1,0001/100
Titer
cSN
As all models are valid within it’s limits and to ensure a quantification from very low to very high titers, samples were diluted in three sample wells and logit model applied inside each of these wells
A Excel worksheet is available upon request from the authors
BVD Ab
Discussion/Conclusion
Innovative quantitative method for specific detection of anti-NS3 (p80) antibodies against BVDV using latest quantitative model from human medicine/biostatistics
Excellent linearity
Quantitative method for a specific antibody subpopulation using a blocking ELISA
This standardized quantitative test is a tool that will lead to a breakthrough in the understanding of the BVDV epidemiology by monitoring antibodies populations and be utilized to assess BVDV control measures.
BVD Ab
Acknowledgments
This project was funded through the Kansas State University College of Veterinary Medicine and the Beef Cattle Institute in conjunction with Synbiotics Corporation
BVD Ab
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