In vivo detection of scrapie cases from blood by infrared spectroscopy

Pedro Carmona1, Marta Monzón2, Eva Monleón2, Juan José Badiola2 and Jaime Monreal3

1 Instituto de Estructura de la Materia (CSIC), Serrano 121, 28006 Madrid, Spain
2 Centro Nacional de Referencia de EET (University of Zaragoza), Miguel Servet 177, 50013 Zaragoza, Spain
3 Instituto de Neurobiología Santiago Ramón y Cajal (CSIC), Doctor Arce 37, 28006 Madrid, Spain

Correspondence
Pedro Carmona
p.carmona{at}iem.cfmac.csic.es


   ABSTRACT
Top
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
In the present study, infrared spectroscopy was shown to be able to distinguish healthy and scrapie-infected animals by analysis of the white-cell membranous fraction from blood. Infrared spectroscopy was able to detect not only clinical cases, but also animals at a preclinical stage of the disease. These findings suggest this technique as an accurate in vivo diagnostic tool that could be applied to animal as well as human samples. In addition to possibly avoiding the slaughter of a huge number of animals with the socio-economic consequences that this poses, the test could be expected to become useful in the prevention of human transmission by blood transfusion.


   INTRODUCTION
Top
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Transmissible spongiform encephalopathies (TSEs) are a group of fatal, neurodegenerative disorders characterized by the accumulation of an abnormal (aberrantly folded) isoform of a cellular host protein (PrPC), denominated prion protein (PrPSc) (Prusiner, 2001). Ovine and caprine scrapie, bovine spongiform encephalopathy (BSE) and human variant Creutzfeldt–Jakob disease (vCJD) are all caused by this infectious agent.

The primary route of prion transmission is associated with the ingestion of contaminated material. Subsequently, two routes of neuroinvasion (entry of TSE agent into the central nervous system) have been hypothesized: a direct route spreading to the brain via the peripheral nervous system (PNS) and an indirect route via the lymphoreticular system (LRS) prior to PNS involvement (Race et al., 2000; Mabbott & Bruce, 2001). Furthermore, although the causal agent has not yet been isolated from naturally affected individual blood samples, dissemination of the agent in blood has also been proposed by others (Aguzzi, 2001; van Keulen et al., 2002). Infectivity of blood during clinical as well as preclinical stages has been demonstrated (Bons et al., 2002; Hunter et al., 2002). Although blood transfusions are leukodepleted in some countries, this finding must be considered especially relevant in the human medical field, as the potential infectivity of blood donated by symptom-free vCJD-infected human beings may represent a risk of spreading vCJD infection within the human population (Houston et al., 2000).

Currently, TSE diagnosis in ruminants is based on the observation of clinical signs, followed by post-mortem confirmation by the demonstration of characteristic lesions of vacuolar changes located in specific nuclei in the central nervous system, and on the detection of PrPSc by using immunochemical techniques (Wells et al., 2000). Analysis of samples of nictitating membranes or tonsil collected by biopsy has also been suggested as an in vivo diagnostic tool for scrapie (van Keulen et al., 1996; O'Rourke et al., 2000). However, the low sensitivity of these techniques has been demonstrated in several cases and, therefore, no ante-mortem method for TSE diagnosis is currently available.

The association between vCJD and BSE (Bruce et al., 1997; Hill et al., 1997) represents a potential risk for public health and so precautionary measures currently applied to TSE diagnosis have been implemented. Specifically, a total of 1 140 591 sheep and goats has been analysed in the EU since 2002 (up to July 2004) and a large number of small ruminants have been slaughtered as a consequence of positive results (the slaughter of the whole flock where a positive scrapie case is detected, or at least all those presenting a susceptible genotype, is mandatory according to EU regulations). Among these animals, only 1897 samples were positive (http://europa.eu.int). These data, combined with the socio-economic effects implied, clearly indicate a need for an in vivo analytical rapid method to allow accurate diagnosis of prion diseases.

As blood is a readily accessible source of material for analysis, several attempts to develop a blood test have been reported (Schmerr et al., 1999; Brown et al., 2001; Carmona et al., 2004). However, only partial success has been demonstrated by their application and none of them is therefore currently used.

Infrared spectroscopy is a powerful and widely applied tool for analysis of protein secondary structure because it can distinguish among {alpha}-helical, {beta}-sheet and unordered structures. It is known that PrPSc is much richer in {beta}-sheet structures than the cellular PrPC (Caughey et al., 1991), so this method has been one of the techniques chosen to be tested for prion-disease detection (Lasch et al., 2003; Thomzig et al., 2004).

The main objective of the present study was to assess the capability of the infrared spectroscopy method to discriminate between negative and naturally infected scrapie cases in animals at clinical and preclinical stages, using whole blood.


   METHODS
Top
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Collection of samples.
A total of 36 sheep was included in the present study. These were Rasa Aragonesa, Roya Bilbilitana, Saltz or crossbreeds and all presented the ARQ/ARQ genotype, excluding sheep 33 (ARQ/ARH) and sheep 31 (ARR/ARQ). Fifteen healthy sheep from three different flocks where scrapie cases had never been observed were used as negative-control cases (sheep 22–36; see Table 1 for genotypes). The 21 remaining sheep consisted of six animals from the Spanish scrapie-surveillance programme (corresponding to the subpopulation of clinical cases; sheep 14–19) and 15 from a regularly monitored Rasa Aragonesa sheep flock, in which an outbreak of natural scrapie occurred in June 2002. Since that date, this flock has been closed and monitored regularly, with several more cases of scrapie being observed. All sheep in this flock were raised under normal conditions for a sheep-producing flock with no physical separation between animals, and all animals used for research purposes were removed from the flock just before culling. Nictitating membrane biopsy was used for selecting negative (sheep 7, 20 and 21) and positive (sheep 1–6 and 8–13) animals, although the actual diagnosis was determined by confirmatory test (Wells et al., 2000) in all cases. No clinical signs were observed in six of these positive cases (sheep 1–6).


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Table 1. Results obtained from blood analysis of the 36 sheep included in the study at different clinical stages by the three diagnostic techniques applied: nictitating membrane biopsy (biopsy), confirmatory test (post-mortem diagnosis) and infrared spectroscopy (IR)

 
Blood samples were collected into 10 ml sterile tubes (Venoject) containing 1·4 mM EDTA from all of the animals on a single occasion, excluding six positive animals and one negative animal for which the collection was performed twice for a monitoring study. All sheep used in the study were slaughtered in order to confirm the scrapie diagnosis. Intravenous pentobarbital injection followed by exsanguination was used for euthanasia. Immediately after slaughtering, brain for scrapie-diagnosis confirmation by histopathology and immunohistochemistry (Wells et al., 2000) was removed. Other tissues from the LRS (palatine tonsils, Peyer's patch of the ileum and ileocecal valve, spleen and mesenteric, retropharyngeal and mediastinal lymph nodes) for PrPSc presence assessment were also collected in order to detect possible preclinical cases.

Rules established in the National Research Council's guide for animal experimentation were followed for animal handling and care.

Preparation of the membrane-rich fraction from blood cells.
This preparation was carried out as described previously (Carmona et al., 2004). Briefly, 6 ml blood was centrifuged at 4300 g for 30 min at 4 °C. The resulting pellet was resuspended completely in isotonic saline (0·9 % NaCl) and re-centrifuged. The resulting pellet was subjected to osmotic shock with up to 40 ml water (Milli-Q; Millipore). After the suspension became homogeneous, it was centrifuged at 12 000 g for 30 min at 4 °C and the supernatant was completely decanted, which led to a small membranous pellet that was used for infrared analysis.

Preparation of leukocyte membranous fractions.
This treatment was applied to two positive and two negative samples in order to determine the blood fraction where the cellular component detected by the spectroscopic technique was located.

Blood samples were centrifuged at 1200 g for 15 min, resulting in the white cells (buffy coat; fraction A) at the interface. The buffy coat was then fractioned by gradient centrifugation using a Lymphoprep solution (Nycomed). Two fractions were generated: fraction B, mainly containing neutrophils, eosinophils and basophils; and fraction C, mainly comprising lymphocytes and monocytes present as major and minor populations, respectively. Identification of the cellular components of the fractions obtained was achieved by Giemsa staining and microscopic observation. Cellular fractions were again subjected to osmotic shock with 25 ml Milli-Q water. Twenty-five millilitres of saline solution (1·8 % NaCl) was then added, and subsequent washes (three times) by the addition of 25 ml TBS [0·05 M Tris/HCl (pH 7·6), 0·15 M NaCl] and centrifugation at 500 g for 10 min were carried out. Finally, the resulting pellet was resuspended in TBS for infrared analysis.

Infrared spectroscopy.
The Fourier-transform infrared spectra were measured in a Perkin Elmer 1725X spectrometer equipped with a DTGS (deuterated triglycine sulfate) detector. After isotopic hydrogen/deuterium exchange carried out by dialysis against D2O, the samples were placed in cells with CaF2 windows and, when necessary, the residual H2O vapour spectrum was removed. All spectra were obtained at room temperature with a 2 cm–1 resolution by averaging 64 scans. Curve fitting to determine the secondary-structure percentages was performed by using the program GRAMS/AI (ThermoGalactic). Initial band positions were taken directly from the second derivative spectra, and initial values for the peak heights and widths were estimated from the spectra. When necessary, Fourier-transform self-deconvolution of the spectra was performed using a line-narrowing factor of K=2. The relative integrated intensity of each band component (i.e. the band area as a fraction of the total amide I area) was then calculated as indicative of its respective percentage secondary structure.

Statistical analyses.
Principal-component analysis (PCA) is a well-known pattern-recognition and multivariate data display method (Martens & Martens, 2001). It not only compares object clusters, but also displays relationships among variables, as well as variables and objects. This meant that, for this study, PCA could be used as a tool that could classify the infrared spectra into different classes if the spectra of the biological samples studied here contained absorption bands of a particular protein structure that it was necessary to quantify. With this aim, the appropriately formatted spectroscopic data of this work were exported to an SPSS software package (SPSS Inc.) for the PCA analysis. In addition, unpaired Student's t-tests were carried out by comparing the two series of infrared spectroscopic results corresponding to healthy and scrapie-infected animals.


   RESULTS
Top
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Examples of infrared spectra of the aqueous pellets of the blood-cell membranous fraction obtained from healthy and scrapie-infected animals are shown in Fig. 1. As these pellets are protein in nature, the spectral profiles were generated by the amide I vibrational modes of the pellet proteins, supported by the fact that the spectral features in the 1700–1500 cm–1 range were characteristic of this type of biomolecule. Due to overlapping of the amide I band components corresponding to various protein secondary structures, it was difficult to distinguish between these structures from the original spectra. However, some methods aimed at enhancing resolution of spectral profiles, such as derivative spectroscopy, allow the secondary-structure bands to be identified. Accordingly, Fig. 1 shows that one of the most significant differences between the second derivative spectra measured from healthy control and scrapie samples involved the intensity of the band located near 1630 cm–1, which was clearly stronger for scrapie samples. On the basis of both theoretical vibrational analysis and spectral measurements of model proteins with known conformational structures (Krimm & Bandekar, 1986; Byler & Susi, 1986), the infrared absorption near this frequency can be assigned unambiguously to a {beta}-sheet structure. In support of higher levels of {beta}-sheet content in scrapie samples, PCA revealed that all of the spectra from healthy controls could clearly be separated from those of scrapie-infected samples. Thus, we demonstrated PCA as a classification technique able to determine the relationship, if any, between different cases, which can be represented in the space generated by the factor axes (principal spectral components). Every case was characterized by its spectral coordinates in this factor axis space, and classification of individuals (cases) into categories was visualized readily by projection of all of the points (cases) in the two-dimensional factor spaces (factor planes). Fig. 2 shows the coordinates of the scrapie-infected and control samples with respect to two factor axes (factors 1 and 2, specifically, which account for >81 % of the variability in the data). Two groups could be distinguished in this two-factor score plot corresponding to scrapie-infected (SINF) and negative-control (NC) samples, respectively. Cases belonging to both groups all extended throughout the negative and positive parts of the factor 1 axis, respectively. The question arose as to the spectral significance of the positive and negative parts of this factor 1 axis. To answer this, the loadings plot (or abstract spectrum) of the principal component 1 for the score plot in Fig. 2 is also depicted (Fig. 3). This plot showed a positive peak near 1654 cm–1 and two negative peaks near 1632 and 1686 cm–1, these positive and negative signals being coincident with the amide I bands generated by {alpha}-helical and {beta}-sheet structures, respectively. Furthermore, the opposite signs of these peaks reflected the opposite change in direction of these {alpha}-helical and {beta}-sheet structural features. Taken together, the results of Figs 2 and 3 showed that the infrared spectra of scrapie samples could be separated from those of healthy controls due to variability in the {beta}-sheet structure content.



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Fig. 1. Infrared spectra of the blood membrane fraction showing more pronounced spectral features of {beta}-sheet structure in the case of a scrapie-affected animal (b) compared with a healthy control animal (a). The original (top) and second derivative (lower) spectra are shown for each sample.

 


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Fig. 2. Factor 1 versus factor 2 plot (i.e. score plot) of principal-component analysis from healthy control (NC) and scrapie-infected (SINF) samples.

 


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Fig. 3. Loadings plot of factor 1 (principal component 1) showing the factor coordinates of the frequency variables.

 
Additionally, in order to determine the type of blood cells where the highest detected {beta}-sheet percentages were located, separation of blood cells was carried out and the resulting fractions were analysed by infrared spectroscopy. Fig. 4 shows the infrared spectra of membrane pellets of various blood-cellular fractions from a scrapie-infected animal. Typical amide I band components of {alpha}-helices, random coils and {beta}-sheet protein structures are visible, particularly in the second derivative spectra (Fig. 4b). The whole white-cell fraction (buffy coat) was compared with two fractions, one containing a high proportion of neutrophils and a low proportion of eosinophils and basophils, and the other consisting of major and minor populations of lymphocytes and monocytes, respectively. The relative intensity of the band located near 1630 cm–1, which is attributable to {beta}-sheet structure, was found to be the strongest in the fraction predominantly containing lymphocytes.



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Fig. 4. Infrared spectra of membrane pellets of various blood-cellular fractions from a scrapie-infected animal. In some of these fractions, purified lymphocyte membranes are present. (a) Deconvolved original infrared spectra; (b) second derivative spectra of the leukocyte pellet. The spectra shown are from the buffy coat (whole white-cell fraction) (top spectrum), from a fraction containing predominantly neutrophils with a minor population of eosinophils and basophils (middle spectrum) and from a fraction containing major and minor populations of lymphocytes and monocytes, respectively (lower spectrum). The {beta}-sheet structure percentages were found to be 20, 5 and 23 % for buffy coat, neutrophil and lymphocyte fractions, respectively.

 
Taking into account the techniques described above for interpretation of the results provided by infrared spectroscopy, the final spectroscopic diagnoses were in total agreement with the post-mortem analyses of all animals, even though a negative result was provided by in vivo diagnosis (biopsy) in one of these cases (sheep 7; Table 1). The correlation of results between these two techniques was therefore demonstrated not only in those animals where clinical signs had been observed (sheep 7–19), but also in preclinical cases (sheep 1–6).

Statistically significant differences (P<0·0001) between the {beta}-sheet percentages of the scrapie-infected samples and those of healthy sheep were found (Fig. 5). Moreover, {beta}-sheet content increased as the disease progressed. Depending on the stage of the disease, this content reached means of 21·57±3·2, 23·9±3·8 and 28·78±2·2 % for preclinical, clinical and terminal stages, respectively. Fig. 6 shows that a time-dependent increase (relative to blood collection date) in {beta}-sheet percentage was apparent in all of the positive cases analysed. By contrast, there was no significant variation in {beta}-sheet content of samples from scrapie-free sheep over various collection dates.



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Fig. 5. {beta}-Sheet content in healthy (control) and scrapie-infected samples. Results are shown as the mean±SEM.

 


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Fig. 6. Linear representation of data provided by the monitoring study showing the {beta}-sheet percentages found in samples of blood from seven different sheep (with and without scrapie) collected on two different occasions.

 
Neurological signs were observed in two additional animals (sheep 20 and 21; Table 1) whose post-mortem analyses, as well as the spectroscopic results, proved negative, indicating that these animals were not infected. The infrared spectroscopic finding of two animals that were negative for scrapie (confirmed by immunochemistry) presenting neurological signs represents a relevant contribution with regard to the specificity of this technique. Thus, specificity, defined as the probability of a test being able to recognize truly negative samples as negative, was shown here to be a characteristic of the infrared method because it was able to distinguish between scrapie and cases affected by other neurological diseases.


   DISCUSSION
Top
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
The results of the present study indicate the accuracy and the potential applicability of the infrared spectroscopic method for detection of scrapie by analysis of blood.

The results provided by the infrared technique indicate that the bands generated by {beta}-sheets contribute greatly to the ability to distinguish between infected and control groups of animals. The strongest band observed in the samples near 1654 cm–1 corresponded to {alpha}-helical structures, while the band located near 1630 cm–1 could be ascribed to the amide I vibrational mode of {beta}-sheets, as observed in model {beta}-sheet polypeptides and proteins (Byler & Susi, 1986; Krimm & Bandekar, 1986; Surewicz & Mantsch, 1996). Nucleic acids, if present, were not detectable in these samples because of their very low concentration, as shown by the absence of bands characteristic of phosphoester groups of these biomolecules in the 900–800 and 1300–1000 cm–1 ranges and by the absence of heterocyclic base bands in the 1680–1720 cm–1 region. Although further studies are necessary to detect PrPSc by immunochemistry in the membranous pellets, spiking a pellet sample with PrPSc results in increased infrared intensity near 1630 cm–1, and thus the results presented in this work provide evidence that the spectroscopic technique tested is capable of discerning between scrapie-infected and healthy sheep. This was supported by the demonstration of a clear correlation between {beta}-sheet content and the presence of scrapie disease, and even between {beta}-sheet content and the stage of disease progression. Additionally, only some neurodegenerative diseases affecting the central nervous system involve amyloid plaques formed by {beta}-sheet-rich proteins (Antzutkin et al., 2002).

The location of prion molecules, mainly in cell membranes, endosomes and Golgi compartments (Herrmann et al., 2001; Brown & Harris, 2003; Caughey & Kocisko, 2003), is consistent with the detection of PrPSc in the membrane fractions that were analysed in the present study, prepared by osmotic shock and appropriate centrifugation. Moreover, taking into account the involvement of the LRS in scrapie pathogenesis (Klein et al., 1997; Aucouturier et al., 2000) and the demonstration of PrPC on cellular components of blood (Halliday et al., 2005), our finding of PrPSc in membranous fractions of lymphocytes is a reasonable expectation. Evidence that T lymphocytes are not involved in scrapie pathogenesis was first suggested when studies showed that thymectomy had no effect on the incubation period of the disease following peripheral infection (Fraser & Dickinson, 1978). Later, studies based on transgenic and immunodeficient mice also demonstrated that deficiencies in the T-lymphocyte compartment alone had no effect on disease susceptibility or the accumulation of infectivity in the spleen (Mabbott & Bruce, 2001). However, although it remains uncertain whether B lymphocytes are involved directly or indirectly in TSE pathogenesis (Mabbott & Bruce, 2001), their participation in the intracorporeal transportation of PrPSc seems clear.

A number of authors have reported that differences in the infrared spectra can distinguish between hamsters infected experimentally with scrapie and uninfected animals (Kneipp et al., 2002; Schmitt et al., 2002). Others have even suggested that different conformations of PrPSc are associated with different hamster TSE strains (Caughey et al., 1998). However, to our knowledge, this is the first description of such differences in natural scrapie infection. For the TSE testing of living animals, many parameters that may influence the test results (age, breed and nutrition, as well as genotype or strain in the case of small ruminants) should be controlled. The sample size was small, due to the logistics of collecting these materials. However, although the accuracy of the spectroscopic technique needs to be substantiated by larger-scale studies, the findings presented here, in combination with encouraging results described in other studies using the same technique applied to serum from bovine species (Lasch et al., 2003), allow us to suggest that infrared spectroscopy could be useful for TSE diagnosis in different species. Moreover, this spectroscopic method is not time-consuming and requires no additional reagents. However, the most attractive feature is that it is a non-invasive technique that is readily applicable to in vivo testing to detect TSE cases.

Two animals that presented neurological signs were found to be negative by this spectroscopic method, which was confirmed by immunochemistry. This indicates the specificity of the technique in its ability to distinguish between scrapie and other neurological disease-affected cases. In addition, the fact that several animals at a preclinical stage of the disease were detected as positive by infrared spectroscopy, along with the infrared detection of one negative case that was coincident with in vivo diagnosis, are indicative of the high sensitivity of this technique, which had been questioned by others (Martin et al., 2004). There were some initial problems involving osmotic shock when setting up the technique, which may explain the one contradictory result from spectroscopy compared with immunochemistry. Therefore, further studies including a larger number of animals are required to confirm whether 100 % specificity and sensitivity can be achieved in practice.

In conclusion, the findings described in this work could constitute a major advance in TSE diagnosis, as no in vivo diagnostic tool is currently available. In addition to the socio-economic consequences that the applicability of this test could have in the animal field, it could also have important repercussions in human medicine, where there is a lack of confidence in the current methods for preventing human transmission via blood or blood-product transfusions (Murphy, 2002).


   ACKNOWLEDGEMENTS
 
We thank the Red-Cien (Neurobiology and Neuropathology Group of Institute S. R. Cajal, CSIC), the Fundación Mutua Madrileña, the Ministerio de Ciencia y Tecnología (EET-2001-4844-CO3-01) and Gobierno de Aragón for financial support.


   REFERENCES
Top
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
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Received 11 April 2005; accepted 12 September 2005.



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PubMed Citation
Articles by Carmona, P.
Articles by Monreal, J.
Agricola
Articles by Carmona, P.
Articles by Monreal, J.


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