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)

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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

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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

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Diagnostic Ab Mono Blocking ELISA

Negative sample

Positive sample

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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

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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

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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

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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)

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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

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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)

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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)

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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

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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.

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Acknowledgments

This project was funded through the Kansas State University College of Veterinary Medicine and the Beef Cattle Institute in conjunction with Synbiotics Corporation

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