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ID 61504
フルテキストURL
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著者
Ishii, Hiroko GSP Enterprise, Inc.
Zahra, Maram H. Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University
Takayanagi, Atushi GSP Enterprise, Inc.
Seno, Masaharu Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University ORCID Kaken ID publons researchmap
抄録
Cripto-1 is a member of the EGF-CFC/FRL1/Cryptic family and is involved in embryonic development and carcinogenesis. We designed a novel anti-Cripto-1 artificial antibody and assessed the recognition to the antigen and the potential to suppress the growth of cancer stem cells. First, single chain antibody clones were isolated by bio-panning with the affinity to recombinant Cripto-1 protein from our original phage-display library. Then, the variable regions of heavy chain VH and light chain VL in each clone were fused to constant regions of heavy chain CH and light chain CL regions respectively. These fused genes were expressed in ExpiCHO-S cells to produce artificial humanized antibodies against Cripto-1. After evaluation of the expression levels, one clone was selected and the anti-Cripto-1 antibody was produced and purified. The purified antibody showed affinity to recombinant Cripto-1 at 1.1 pmol and immunoreactivity to cancer tissues and cell lines. The antibody was available to detect the immunoreactivity in tissue microarrays of malignant tumors as well as in Cripto-1 overexpressing cells. Simultaneously, the antibody exhibited the potential to suppress the growth of human colon cancer derived GEO cells overexpressing Cripto-1 with IC50 at approximately 110 nM. The artificially humanized antibody is proposed to be a good candidate to target cancer cells overexpressing Cripto-1.
キーワード
phage display library
artificial humanized antibody
Cripto-1
anti-Cripto-1 antibody
tissue-micro array
cell growth inhibition
発行日
2021-02-08
出版物タイトル
International Journal of Molecular Sciences
22巻
4号
出版者
MDPI
開始ページ
1709
ISSN
1422-0067
NCID
AA12038549
資料タイプ
学術雑誌論文
言語
英語
OAI-PMH Set
岡山大学
著作権者
© 2021 by the authors.
論文のバージョン
publisher
PubMed ID
DOI
Web of Science KeyUT
関連URL
isVersionOf https://doi.org/10.3390/ijms22041709
ライセンス
http://creativecommons.org/licenses/by/4.0/