Exploring Physical and Chemical Factors Influencing the Properties of Recombinant Prion Protein and the Real-Time Quaking-Induced Conversion (RT-QuIC) Assay

<div><p>Real-time quaking-induced conversion (RT-QuIC), a highly specific and sensitive assay able to detect low levels of the disease-inducing isoform of the prion protein (PrP^d) in brain tissue biopsies and cerebral spinal fluid, has great potential to become a method for diagnosing prion disease <i>ante mortem</i>. In order to standardize the assay method for routine analysis, an understanding of how physical and chemical factors affect the stability of the recombinant prion protein (rPrP) substrate and the RT-QuIC assay’s sensitivity, specificity, and reproducibility is required. In this study, using sporadic Creutzfeldt-Jakob Disease brain homogenate to seed the reactions and an <i>in vitro</i>-expressed recombinant prion protein, hamster rPrP, as the substrate, the following factors affecting the RT-QuIC assay were examined: salt and substrate concentrations, substrate storage, and pH. Results demonstrated that both the generation of the quality and quantities of rPrP substrate critical to the reaction, as well as the RT-QuIC reaction itself required strict adherence to specific physical and chemical conditions. Once optimized, the RT-QuIC assay was confirmed to be a very specific and sensitive assay method for sCJD detection. Findings in this study indicate that further optimization and standardization of RT-QuIC assay is required before it can be adopted as a routine diagnostic test.</p></div

The effect of batch variability and seed genotype on RT-QuIC.

<p>RT-QuIC was performed using two different batches, Batch #1 (A) and Batch #2 (B), of hamster rPrP. Both assays employed hamster rPrP at a concentration of 60 µg/well as the substrate and sCJD M/V brain homogenate (BH) at the indicated dilutions as the seed. (C) RT-QuIC was performed using hamster rPrP Batch #1 at a concentration of 60 µg/well as the substrate and sCJD M/M brain homogenate (BH) at indicated dilutions to seed conversion. Reactions contained minimal salt (5.5 mM NaCl).</p

The effect of substrate concentration on RT-QuIC.

<p>RT-QuIC was performed using hamster rPrP substrate at concentrations of 20, 35, 45, and 60 µg/well. Reactions contained minimal salt (5.5 mM NaCl) and employed sCJD M/V brain homogenate (BH) diluted 10⁻⁴ to seed conversion.</p

Urine Proteins Identified by Two-Dimensional Differential Gel Electrophoresis Facilitate the Differential Diagnoses of Scrapie

<div><p>The difficulty in developing a diagnostic assay for Creutzfeldt - Jakob disease (CJD) and other transmissible spongiform encephalopathies (TSEs) stems in part from the fact that the infectious agent is an aberrantly folded form of an endogenous cellular protein. This precludes the use of the powerful gene based technologies currently applied to the direct detection of other infectious agents. To circumvent this problem our research objective has been to identify a set of proteins exhibiting characteristic differential abundance in response to TSE infection. The objective of the present study was to assess the disease specificity of differentially abundant urine proteins able to identify scrapie infected mice. Two-dimensional differential gel electrophoresis was used to analyze longitudinal collections of urine samples from both prion-infected mice and a transgenic mouse model of Alzheimer's disease. The introduction of fluorescent dyes, that allow multiple samples to be co-resolved and visualized on one two dimensional gel, have increased the accuracy of this methodology for the discovery of robust protein biomarkers for disease. The accuracy of a small panel of differentially abundant proteins to correctly classify an independent naïve sample set was determined. The results demonstrated that at the time of clinical presentation the differential abundance of urine proteins were capable of identifying the prion infected mice with 87% sensitivity and 93% specificity. The identity of the diagnostic differentially abundant proteins was investigated by mass spectrometry.</p></div

Principle component analysis of the 11 Alzheimer's disease model (red) and the 12 control samples (green) collected from 4 diseased and 4 control mice at 3 time points: 32, 36, and 40 weeks of age.

<p>A sample from one of the diseased mice was not obtained at 32 weeks of age. A classifier based on 84 features was able to correctly classify the 23 samples of the training set with 100% accuracy. (PC1 = 62.0, PC2 = 11.9).</p

Flow chart depicting the development of the Disease Discriminant Classifier based upon 20 identified proteins from the 169 proteins that were present in 80% of the 38 gel images representing the clinical stage Alzheimer and scrapie sample sets exhibiting significant differential abundance (ANOVA p≤.01).

<p>Flow chart depicting the development of the Disease Discriminant Classifier based upon 20 identified proteins from the 169 proteins that were present in 80% of the 38 gel images representing the clinical stage Alzheimer and scrapie sample sets exhibiting significant differential abundance (ANOVA p≤.01).</p

Principle component analysis of 7 scrapie infected samples (red) at 15 and 17 weeks of age, 11 Alzheimer's disease samples (green) at 32, 36 and 40 weeks of age, and the 20 corresponding control samples (blue) when analyzed by the 20 protein Disease Discriminant Classifier.

<p><b>Samples were produced by 4 scrapie infected mice, 4 Alzheimer diseased mice and the 8 corresponding control mice.</b> Thirty seven of the 38 samples were correctly classified. The 32 week old control sample, misclassified as having Alzheimer's disease, is identified by an arrow.</p

Representative Cy-2 labelled internal standard gel image illustrating the proteins resolved in the pH 4 to pH 7 range.

<p>Each green dot represents a feature identified by the DeCyder software. The absence of green dots in the PI 4.5/25 kDa range indicates the location of saturated features that were excluded from the analysis. The Disease Discriminant Classifier was constructed from the 20 features circled in yellow. The positions of the two features utilized to generate the PCA plot in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064044#pone-0064044-g001" target="_blank">Figure 1</a> and create a classifier able to discriminate between scrapie infected and age matched control mice with 100% accuracy from 11 weeks post-infection onward are marked with a red dot.</p

Classification results applying the 20 protein Disease Discriminant Classifier to 20 naïve samples obtained when the disease stage of the test set mirrored that of the training set.

<p>√ indicates correctly classified samples.</p><p>X indicates incorrectly classified samples.</p><p>* indicates the correct classification for the misclassified samples.</p

Principle component analysis of the 169 feature classifier applied to the 7 scrapie infected (red) and the 8 corresponding scrapie control samples (yellow with red boarder) obtained from 4 infected and 4 control mice at 15 and 17 weeks post infection combined with the 11 Alzheimer's diseased (blue) and the 12 corresponding Alzheimer's disease control samples (yellow with blue boarder) obtained from 4 diseased and 4 control mice at 32, 36 and 40 weeks of age.

<p>(PC1 = 27.9, PC2 = 24.5).</p