New Approaches in Biomedical Spectroscopy - American Chemical

Chapter 4

Downloaded by PENNSYLVANIA STATE UNIV on August 6, 2012 | http://pubs.acs.org Publication Date: August 2, 2007 | doi: 10.1021/bk-2007-0963.ch004

New Approaches Detection Secondary Conformation of Prion Protein in Frozen-Section Tissue by Fourier-Transform Infrared Microscopy 1

2

2

Norio Miyoshi , Hiroyuki Okada , Masuhiro Takata , Morikazu Shinagawa , and Kenichi Akao 2

3

1

Division of Tumor Pathology, Department of Pathological Sciences, Faculty of Medicine, University of Fukui, Matsuoka, Eiheiji, Fukui 910-1193, Japan Prion Disease Research Center, National Institute of Animal Health, Kannondai, Tsukuba 305-0856, Japan Spectrometry Instrument Division, JASCO Company, Ishikawa-cho, Hachioji-City, Tokyo 192-8537, Japan

2

3

The spectra and the relative ratio of the secondary conformations (α-helix and β-sheet) of prion infected brains in frozen sectioned tissue were measured by Fourier-transform infrared (FT-IR) microscopy. The tissues were obtained from the Prion Diseases Research Center in the National Animal Health Institute (PDRC/NAHI) of Japan. Both prion infected and normal brain tissues of mice and hamster were embedded adjacently and were frozen-sectioned for the FT-IR microscopy. Spectra from the normal and the prion-infected brain tissue sides were compared. The C - H stretching components in the normal tissue and the amide-I, -II components in the prion tissue were larger than the other sides, respectively. Furthermore, we developed the software to analyze the relative ratios of protein secondary conformation in the tissue by FT-IR microscopy. Results showed that the relative ratio of the β-sheet component was at a higher level (37-40%) in the prion side compared to that in the normal © 2007 American Chemical Society

In New Approaches in Biomedical Spectroscopy; Kneipp, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2007.

41

42 brain tissue. The β-sheet component percentage data were highly contrast against the normal tissue and was small standard deviations comparing the curve-fitting method. We mapped images of the infected brain tissue with respect to the lipid ester, phosphate and protein content.


Introduction According to the "Prion theory" prion proteins are capable of having more than one secondary conformational structure. This phenomenon can been investigated using several techniques including fluorescence spectroscopy and a fluorescent probe both of which can be problematic in their application. In addition, the aggregation and infection mechanisms remain unclear. The protein biopolymers in brain tissue is not simple at the tissue level. Ideally the brain tissue should be analysed with little or minimal sample preparation such as fixing, embedding in paraffin at high temperaturse, sectioning, de-paraffinising using ethanol or staining with hematoxylin and eosinlt is expected that protein conformation will change as a result of pathological processes. Kneipp, J. et al. (7) have reported the molecular changes of preclinical scrapie detected by infrared spectroscopy. Following our work concerning cancer diagnosis (2) using FTIR spectroscopic techniques we recently extended our studies to prioninfected brain tissue (without the pathological treatments) using Fouriertransform infrared microscopy (FTIRM) in conjunction with mapping software (IR-SSE developed in collaboration with J A S C O Inc., Japan ). We report how this approach using a combination of FTIR techniques should prove very useful for explaining protein conformational changes.

Experiments and Methods (1) Sampling: The infected intra-cerebrally mice for Obihiro strain (3) and hamsters for Sc237 (4) at each infected stages were sent from P D R C / N A H I (Ibaraki, Japan) after all the safety precautions for the study were fulfilled. Brain tissues (hippocampus domain) were sampled under P2 level conditions and were embedded adjacently with normal brain tissues (hippocampus domain) into an embedding medium for frozen tissue specimens (OCT: Optimal Cutting Temperature; Tissue-Tek, Sakura Fine-technical Co., Ltd., Tokyo, Japan), respectively. (2) Frozen-sectioning: The frozen compound blocks were sectioning in a 10 μηι series at -20 C (Mode: CM3050 S, Leica Co., Germany). One of the


43 sections was used for the FT-IR spectra measurement and the other one was H & E stained. (3) FT-IR spectra measurements: Lattice mapping spectra in the 4000-400 cm" range were collected by a JASCO FT-IRM-410M spectrometer equipped with an Irtron IRT-30M IR microscope (JASCO Co., Ltd., Tokyo, Japan), a motorized X Y Z stage, and a liquid nitrogen-cooled mercury-cadmium-telluridedetector. A screen image recorder camera attached to the microscope enabled the acquisition of a photomicrograph of the investigated area. The object area imaged by an individual aperture size was 20x20 μπι . Sequential spectra were collected from 400 points (20 χ 20 points) in the specimen. The area of spectral acquisition amounted to a total of 180 μπι . For each spectrum, 100 spectra were collected, signal-averaged, and Fourier-transformed to generate spectra with a resolution of 4 cm" in the transmission mode. A l l sample spectra of brain tissue were subtracted with the O C T compound frozen section. (4) Assignments and F T - I R M Image: The peak position of the derived spectra in each necrotic area and undiseased area of the brain tissues were confirmed by Fourier self de-convolution and the second-derivative spectra with a band width of 35 cm' . The wave numbers of characteristic absorption band in each of the selected spectra were assigned (5-8). To estimate biochemical components, the data obtained were referred to a computerized spectral library data of the FT-IRM-410M software. (5) Creation of Images by Protein Secondary Structure Analysis: We investigated the conformational changes in protein secondary structure in both the infected and normal hippocampus region brain tissue using a new spectral analytic program (IR-SSE; JASCO Co., Ltd., Tokyo, Japan). This program applies singular value decomposition techniques to estimate the mathematical relationship between the secondary structure types. The program classified the protein conformations into four categories: α-helix, β-sheet, β-turn, and the others included random coil. 1


2

2

1

1

Results and Discussion (1) Difference spectrum of FT-IR spectra for the infected and the normal brain tissues: Typical FT-IR spectra for both the infected-prion and the normal hippocampus brain tissue are shown in Figure la. The difference spectrum for the normal and infected-prion brain samples are shown in Figure lb. From the decrease in the lipid (C-H symmetric and anti-symmetric stretching vibration modes; 2,700-3,000 cm" ) and the phosphate (P=0 symmetrical stretching vibration mode; 1,080 cm" ) components, it was concluded that the lipid and nuclear D N A components are decreased in the infected tissue. 1

1



Figure 1. Typical FT-IR spectra of the infected and the normal brain tissue of mice.



45 (2) Curve Fitting Results: Curve fitting (P) of the amide-I peak was used for analyzing the secondary protein conformations in the infected and the normal brain tissues. The curve fitting analysis results are shown in Figure 2. 10 components for 4 secondary conformations were separated. Both amide-I absorption spectra for the normal and infected hippocampus brain tissues were each fitted with the original spectra as shown in Figure 2. (3) IR-SSE software results and the Comparison: Akao et al. (70) have developed a secondary structural estimation program of protein to be used in conjunction with FT-IR mapping . The protein secondary structure is estimated by a principal component regression (PCR) (77) or partial least square (PLS) method. The curve fitting spectra were analyzed using the P C R algorithmic method as shown in (Figure 3) for the same samples in Figure 2. This software included the subtraction analysis of a phosphate buffer and of water (vapor), and we tested for 5% (w/v) solutions of the five types of standard proteins (Lysozyme, Ig-G, Cytochrome-C, Ovalbumin, Pepsin) dissolved in a phosphate buffer. 10 μΐ of the sample were sandwiched between two CaF plates and then measured using a transmission technique. The instrument of choice for this test was the FT-IR-680 plus, and measurements were repeated three times with 32 integrations, at a of resolution of 4 cm" , and cosine apodization. The results were as good as can be achieved with respect to reproducibility. In addition, a comparison with the X-ray data reveals a slight shift, and this is thought to be due to the differences between the crystal and liquid states as well as differences due to variations such as purity, species, and tissue types of the measured proteins . Taking these issues into consideration we believe this program is well suited to the secondary structure analysis of protein in brain tissue. The methods most widely used to analyze the secondary structure of proteins involve samples that are crystallized for X-ray crystallography or N M R and C D spectroscopic techniques using aqueous solutions.. However, it is extremely difficult to analyze the secondary structure of multi-component proteins that have anatomy-like structures. On the other hand, since IR spectrascopic techniques can easily measure non-crystalline samples, they are suitable for tissues under sampling conditions as close to in-vivo as possible. Furthermore, micro-FTIR enables the surface analysis of heterogeneous samples. Consequently, the IR-SSE mapping program was developed to analyze mapping data The IR-SSE mapping program allows one to use the P C R or PLS technique to analyze each IR spectrum obtained automatically and to display the distribution map of the secondary structure of proteins by color-coding, contour lines, and other means. It also includes a feature that automatically eliminates calculations for areas in which proteins do not exist within a sample. In Figure 3 the four components of protein secondary conformations are shown in the center (calculated spectrum) of right hand side. The experimental results are listed in the table (bottom left hand side),.. The curve fitting results are compared to the P C R data (IR-SSE, J A S C O Co., Ltd., Tokyo, Japan) as shown in Table I. 2

1



Figure 2.Curve fitting of Amide-I band in the normal and the infected brain tissue of mice.



Figure 3 . A typical result of protein secondary conformation analysis in the infected prion side are using the IR SSE software


48 Table I. The percentages (%) of α-helix and β-sheet components in infected and normal brain tissue (Normal brain) α-helix: Curve fitting/PCR; β-sheet: Curve fitting/PCR


23.16/41 17.92/41 19.98/43 17.63 / 3 2 (Average values) 20% / 39%

40.87/ 13 37.96/ 13 35.27/ 12 37.51 / 2 0 38% / 1 5 %

(Infected brain) α-helix: Curve fitting/PCR; β-sheet: Curve fitting/ PCR 16.18 / 16 3.22 / 19 2.27 / 16 19.82 /24 (Average values) 10% / 1 9 %

56.6 /34 66.71 /33 70.19 /36 49.06 121 60% / 33%

(4) Standard deviation values for the β-sheet component using PCR were up to more than half those obtained by the curve fitting method. It appears to us reasonable to use these P C R values to estimate the secondary conformation ratio in those brain tissues; α-Helix and β-sheet components for normal brain tissue were 39% and 15%, for infected brain tissue 19% and 33%, respectively. Typically in infected brain this corresponds to a decrease in the α-helix of 49%. A corresponding more than doubling in the β-sheet component was observed. These data appear to be reasonably in agreement with the values reported for those in the scrapie-associated protein PrP 27-30 in aqueous solution as determined by infrared spectroscopy (12). The software used in this analysis has also been applied to a study on nerve toxicity and the physicochemical properties of Αβ mutant peptides from cerebral amyloid angiopathy by Murakami et al (13). (5) Mapping of β-sheet structure ratios in the prion-infected and the normal brain tissues: The percentages of β-sheet component calculated for the FT-IR 400 spectra were plotted against the mapped area of 180 χ 180 μπι which included both prion-infected and normal hippocampus brain tissues as shown in Figure 4. In the normal (upper) side, the α-helix component percentages were higher than those in the infected brain tissue (lower) side as shown the left side in Figure 1 : the higher the percentage the warmer the color (brighter) in the left image. On the other hand, the β-sheet component percentages in the infected brain (lower) side were higher (brighter) than those in the normal one (upper side) as shown in the right side image (the contour representation) of Figure 4, respectively. These images are the first example of an imaging application for prion-infected brain tissues. 2



Figure 4. Mapping images of% α-helix and β-sheet components in both sides of the normal and the infected brain tissue of mice



50

Stages Figure 5. The % β-sheet component in infected brain tissus of hamster and mouse at each stages

(6) β-sheet component percentage (%) in the infected stages of hamster and mice: The average value of the highest percentages for the 10 images (hamster and mouse) of the β-sheet component were plotted against each of the infected stages as shown in Figure 5. The Y-axis scale was expanded from 20 to 55 %. These percentages increased exponentially with the affected stages in both cases. Although the infected speed was different, the relative β-sheet component in hamster brain was lower than that in the mouse one against the stages. Hopefully the combination of infrared spectroscopic techniques and IR SSE software used in this study will prove useful in future, studies for the detection (14) of prion diseases.

References 1. 2.

Kneipp, J.; Beekes, M . ; Lasch, P.; Naumann, D. J. Neurosci. 2002, 22, 2989-2997. Yamada, T.; Miyoshi, N.; Ogawa, T.; Akao, K.; Fukuda, M; Ogasawara, T.; Kitagawa, Y . ; Sano, K. Clinical Cancer Res. 2002, 8, 2010-2014.


51 3. 5. 6. 7.


8. 9. 10. 11. 12. 13. 14.

Shinagawa, M . ; Takahashi, Κ.; Sasaki, S.; Doi, S.; Goto, H . ; Sato, G . Microbiol. Immunol. 1985, 29, 543-551. J. Infect. Dis. 1975, 131, 104-110. Manoharan, R.; Baraga, J.J.; Rava, R.P.; Dasari, R.R.; Fitzmaurice, M.; Feld, M.S. Atherosclerosis, 1993, 103, 181-193. Rigas, B.; Wong, P.T.T. Cancer Res. 1992, 52, 84-88. Wong, P.T.T.; Goldstein, S . M . ; Grekin, R.C.; Godwin, T.A.; Pivik, C.; Rigas, B . Cancer Res. 1993, 53, 762-765. Rigas, B . ; Morgello, S.; Goldman, I.S. Wong, P.T.T. Proc. Natl. Acad Sci. USA. 1990, 87, 8140-8144. Dong, Α.; Huang, P.; Caugjey, W. S. Biochem. 1990, 29, 3303-3308. Akao, K . JASCO Report 2002, 44, 54-57. Sarver, RW.Jr.; Krueger, W.C. Anal. Biochem. 1991, 194, 89-100. Caughey, B.W.; Dong, Α.; Bhat, K.S.; Ernst, D.; Hayes, S.F.; Caughey, W.S. Biochem. 1991, 30, 7672-7680. Murakami, K . ; Irie, K . ; Morimoto, Α.; Ohigashi, H . ; Shindo, M.; Nagao, M . ; Shimizu, T.; Shirasawa, T. J. Biol. Chem. 2003, 278, 46179-46187. Martin, T.C.; Moecks, J.; Belooussov, Α.; Cawthraw, S.; Dolenko, B . ; Eiden, M.; von Frese, J.;Kohler, W.; Schmitt, J.; Somorjai, R.; Udelhoven, T.; Verzakov, S.; Petrich, W. Analyst, 2004, 129, 897-901.


New Approaches in Biomedical Spectroscopy - American Chemical

Recommend Documents