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Non-invasive imaging of amyloid deposits in a mouse model of AGel using 99mTc-modified nanobodies and SPECT/CT

(2016) MOLECULAR IMAGING AND BIOLOGY. 18(6). p.887-897
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Abstract
Gelsolin amyloidosis (AGel), also known as familial amyloidosis, Finnish type (FAF), is an autosomal, dominant, incurable disease caused by a point mutation (G654A/T) in the gelsolin (GSN) gene. The mutation results in loss of a Ca2+-binding site in the second gelsolin domain. Subsequent incorrect folding exposes a cryptic furin cleavage site, leading to the formation of a 68-kDa C-terminal cleavage product (C68) in the trans-Golgi network. This C68 fragment is cleaved by membrane type 1-matrix metalloproteinase (MT1-MMP) during secretion into the extracellular environment, releasing 8- and 5-kDa amyloidogenic peptides. These peptides aggregate and cause disease-associated symptoms. We set out to investigate whether AGel-specific nanobodies could be used to monitor amyloidogenic gelsolin buildup. Three nanobodies (FAF Nb1-3) raised against the 8-kDa fragment were screened as AGel amyloid imaging agents in WT and AGel mice using Tc-99m-based single-photon emission computed tomography (SPECT)/X-ray tomography (CT), biodistribution analysis, and immunofluorescence (IF). The quantitative characteristics were analyzed in a follow-up study with a Nb11-expressing mouse model. All three nanobodies possess the characteristics desired for a Tc-99m-based SPECT/CT imaging agent, high specificity and a low background signal. FAF Nb1 was identified as the most potent, based on its superior signal-to-noise ratio and signal specificity. As a proof of concept, we implemented Tc-99m-FAF Nb1 in a follow-up study of the Nb11-expressing AGel mouse model. Using biodistribution analysis and immunofluorescence, we demonstrated the validity of the data acquired via Tc-99m-FAF Nb1 SPECT/CT. These findings demonstrate the potential of this nanobody as a non-invasive tool to image amyloidogenic gelsolin deposition and assess the therapeutic capacity of AGel therapeutics currently under development. We propose that this approach can be extended to other amyloid diseases, thereby contributing to the development of specific therapies.
Keywords
AGel, VHH, Nanobody, SPECT, Gelsolin, SINGLE-DOMAIN ANTIBODY, BLOOD-BRAIN-BARRIER, FAMILIAL AMYLOIDOSIS, GELSOLIN AMYLOIDOSIS, ANTI-HER2 NANOBODY, BREAST-CARCINOMA, FINNISH TYPE, HEAVY-CHAIN, IN-VITRO, FRAGMENTS

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Chicago
Verhelle, Adriaan, Wouter Van Overbeke, Cindy Peleman, Rebecca De Smet, Olivier Zwaenepoel, Tony Lahoutte, Jo Van Dorpe, Nick Devoogdt, and Jan Gettemans. 2016. “Non-invasive Imaging of Amyloid Deposits in a Mouse Model of AGel Using 99mTc-modified Nanobodies and SPECT/CT.” Molecular Imaging and Biology 18 (6): 887–897.
APA
Verhelle, A., Van Overbeke, W., Peleman, C., De Smet, R., Zwaenepoel, O., Lahoutte, T., Van Dorpe, J., et al. (2016). Non-invasive imaging of amyloid deposits in a mouse model of AGel using 99mTc-modified nanobodies and SPECT/CT. MOLECULAR IMAGING AND BIOLOGY, 18(6), 887–897.
Vancouver
1.
Verhelle A, Van Overbeke W, Peleman C, De Smet R, Zwaenepoel O, Lahoutte T, et al. Non-invasive imaging of amyloid deposits in a mouse model of AGel using 99mTc-modified nanobodies and SPECT/CT. MOLECULAR IMAGING AND BIOLOGY. 2016;18(6):887–97.
MLA
Verhelle, Adriaan et al. “Non-invasive Imaging of Amyloid Deposits in a Mouse Model of AGel Using 99mTc-modified Nanobodies and SPECT/CT.” MOLECULAR IMAGING AND BIOLOGY 18.6 (2016): 887–897. Print.
@article{7214101,
  abstract     = {Gelsolin amyloidosis (AGel), also known as familial amyloidosis, Finnish type (FAF), is an autosomal, dominant, incurable disease caused by a point mutation (G654A/T) in the gelsolin (GSN) gene. The mutation results in loss of a Ca2+-binding site in the second gelsolin domain. Subsequent incorrect folding exposes a cryptic furin cleavage site, leading to the formation of a 68-kDa C-terminal cleavage product (C68) in the trans-Golgi network. This C68 fragment is cleaved by membrane type 1-matrix metalloproteinase (MT1-MMP) during secretion into the extracellular environment, releasing 8- and 5-kDa amyloidogenic peptides. These peptides aggregate and cause disease-associated symptoms. We set out to investigate whether AGel-specific nanobodies could be used to monitor amyloidogenic gelsolin buildup. 
Three nanobodies (FAF Nb1-3) raised against the 8-kDa fragment were screened as AGel amyloid imaging agents in WT and AGel mice using Tc-99m-based single-photon emission computed tomography (SPECT)/X-ray tomography (CT), biodistribution analysis, and immunofluorescence (IF). The quantitative characteristics were analyzed in a follow-up study with a Nb11-expressing mouse model. 
All three nanobodies possess the characteristics desired for a Tc-99m-based SPECT/CT imaging agent, high specificity and a low background signal. FAF Nb1 was identified as the most potent, based on its superior signal-to-noise ratio and signal specificity. As a proof of concept, we implemented Tc-99m-FAF Nb1 in a follow-up study of the Nb11-expressing AGel mouse model. Using biodistribution analysis and immunofluorescence, we demonstrated the validity of the data acquired via Tc-99m-FAF Nb1 SPECT/CT. 
These findings demonstrate the potential of this nanobody as a non-invasive tool to image amyloidogenic gelsolin deposition and assess the therapeutic capacity of AGel therapeutics currently under development. We propose that this approach can be extended to other amyloid diseases, thereby contributing to the development of specific therapies.},
  author       = {Verhelle, Adriaan and Van Overbeke, Wouter and Peleman, Cindy and De Smet, Rebecca and Zwaenepoel, Olivier and Lahoutte, Tony and Van Dorpe, Jo and Devoogdt, Nick and Gettemans, Jan},
  issn         = {1536-1632},
  journal      = {MOLECULAR IMAGING AND BIOLOGY},
  language     = {eng},
  number       = {6},
  pages        = {887--897},
  title        = {Non-invasive imaging of amyloid deposits in a mouse model of AGel using 99mTc-modified nanobodies and SPECT/CT},
  url          = {http://dx.doi.org/10.1007/s11307-016-0960-y},
  volume       = {18},
  year         = {2016},
}

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