Differential protein profiling as a potential multi-marker approach for TSE diagnosis

Abstract

Background: Transmissible spongiform encephalopathy describes a family of diseases affecting both man and animals. Current tests for the diagnosis of these diseases are based on the detection of an abnormal misfolded form of the host protein PrP which is found within the central nervous and lymphoreticular systems of affected animals. Recently, concern that this marker may not be as reliable as previously thought, coupled with an urgentneed for a pre-clinical live animal test, has led to the search for alternative assays for the detection of TSE disease.

Methods: This "proof of concept" study, examines the use of differential protein expression profiling using surface enhanced laser desorption and ionisationtime of flight mass spectrometry (SELDI-TOF) for the diagnosis of TSE disease. Spectral output from all proteins selectively captured from individual murine brain homogenate samples, are compared as "profiles" in groups of infected and non-infected animals. Differential protein expression between groups is thus highlighted and statistically significant protein "peaks" used to construct a panel of disease specific markers.Studies at both terminal stages of disease and throughout the time course of disease have shown a disease specific protein profile or "disease fingerprint" which could be used to distinguish between groups of TSE infected and uninfected animals at an early time point of disease.

Results: Our results show many differentially expressed proteins in diseased and control animals, some at early stages of disease. Three proteins identified by SELDI-TOF analysis were verified by immunohistochemistry in brain tissue sections. We demonstrate that by combining the most statistically significant changes in expression, a panel of markers can be constructed that can distinguish between TSE diseased and normal animals.

Conclusion: Differential protein expression profiling has the potential to be used for the detection of disease in TSE infected animals. Having established that a "training set" of potential markers can be constructed, more work would be required to further test the specificity and sensitivity of the assay in a "testing set". Based on these promising results, further studies are being performed using blood samples from infected sheep to assess the potential use of SELDI-TOF as a pre-mortem blood based diagnostic.

Figure 1

Differential protein expression profiling spectra displayed by ProteinChip software. Spectra of proteins expressed in scrapie infected (n = 2) and control (n = 2) brain homogenates from animals at the terminal stage of disease (~260 dpi) (A) displaying similar spectral peaks in all samples. (B) spectra as in (A) at a different part of the spectrum displaying differential expression at a lower molecular weight. (C) differential expression shown in different areas of the same brain.

Figure 2

Optimisation. (A) Effect of pH. Supernatant 1 and supernatant 2 fractions were applied to arrays and buffers of various pH used for incubating samples and washing of arrays. (B) Effect of dilution. Same sample diluted and applied to array (further explored in Additional File 4: optimising experiments). (C) Established protocol for the preparation of samples, handling and analysis of data.

Figure 3

Panel of biomarkers. (A) data from the ProteinChip^® Reader is visualized in spectral format as clusters of differentially expressed protein peaks at 7063 (cluster X21), 7847(X26), 8811 (X31), 10101 (X34), 10300 (X35), 12449 (X43) m/z. Each peak within a cluster represents an individual brain sample i.e. 12 scrapie infected (red) and 12 uninfected (green) animals. Corresponding box plots for each marker displays the separation of markers based on peak height intensity, scrapie infected (red) uninfected (green). (B) cluster analysis shows separation of groups, scrapie infected (ME7) and uninfected (NB) samples. (C) pairwise plots of the above six highly significant proteins one protein against the other (red indicates scrapie infected green indicates control).

Figure 4

Decision tree. The intensity thresholds of two markers 8811 Da and 10101 Da from the panel of markers in Figure 3 were applied to mixed group data. Used in partnership these successfully identified the diseased group.

Figure 5

Q10 array statistics. Panels show box plots of proteins found to be significantly different and separate groups at 90 days post injection(dpi) and 210(dpi). A cluster analysis of the proteins at 210 dpi displays separation of groups. Examples of protein marker differences over time (S1 16885 m/z, S1 14183 m/z, S1 5234 m/z) with corresponding ROC plots(5234,16885 m/z) showing diagnostic potential.

Figure 6

CM10 array statistics. Panels show a dot plot of statistically significant protein peaks at 210 days post injection, a cluster analysis of the same proteins displaying separation between groups. Protein markers at 10180, 22300 m/z supernatant 1 fraction, and 14090 m/z supernatant 2 fraction plotted over time course of disease. 22300 m/z supernatant 2 fractions and 6333 m/z supernatant 1 fraction separation of groups.

Figure 7

Protein identification. (A-C) data from the ProteinChip^® Reader is visualized in spectral format as a cluster of peaks (individual animals n = 12 normal and n = 12 scrapie infected) at 10834, 10101, 11784 m/z. Immunocytochemistry brain sections from terminally ill animals:-infected animals, (A-C ME7) and uninfected animals (A-C NB). (A) Cpn10, CA3 region of hippocampus × 60 oil magnification cropped confocal Z series ME7 (upper panel), NB (lower panel).(B) DBI staining in the hippocampus and dentate gyrus of scrapie infected animal × 20 magnification(ME7 upper panel), NB(lower panel). (C) FKBP12 CA3 hippocampus ME7 (upper panel) and NB (lower panel) ×100 oil magnification.