Chapter title |
A field-proven yeast two-hybrid protocol used to identify coronavirus-host protein-protein interactions.
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Chapter number | 18 |
Book title |
Coronaviruses
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Published in |
Methods in molecular biology, February 2015
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DOI | 10.1007/978-1-4939-2438-7_18 |
Pubmed ID | |
Book ISBNs |
978-1-4939-2437-0, 978-1-4939-2438-7
|
Authors |
Pierre-Olivier Vidalain, Yves Jacob, Marne C Hagemeijer, Louis M Jones, Grégory Neveu, Jean-Pierre Roussarie, Peter J M Rottier, Frédéric Tangy, Cornelis A M de Haan, Marne C. Hagemeijer, Louis M. Jones, Peter J. M. Rottier, Cornelis A. M. de Haan, Vidalain, Pierre-Olivier, Jacob, Yves, Hagemeijer, Marne C., Jones, Louis M., Neveu, Grégory, Roussarie, Jean-Pierre, Rottier, Peter J. M., Tangy, Frédéric, de Haan, Cornelis A. M., Haan, Cornelis A. M. |
Editors |
Helena Jane Maier, Erica Bickerton, Paul Britton |
Abstract |
Over the last 2 decades, yeast two-hybrid became an invaluable technique to decipher protein-protein interaction networks. In the field of virology, it has proven instrumental to identify virus-host interactions that are involved in viral embezzlement of cellular functions and inhibition of immune mechanisms. Here, we present a yeast two-hybrid protocol that has been used in our laboratory since 2006 to search for cellular partners of more than 300 viral proteins. Our aim was to develop a robust and straightforward pipeline, which minimizes false-positive interactions with a decent coverage of target cDNA libraries, and only requires a minimum of equipment. We also discuss reasons that motivated our technical choices and compromises that had to be made. This protocol has been used to screen most non-structural proteins of murine hepatitis virus (MHV), a member of betacoronavirus genus, against a mouse brain cDNA library. Typical results were obtained and are presented in this report. |
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Unknown | 2 | 100% |
Demographic breakdown
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 29 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Professor > Associate Professor | 4 | 14% |
Researcher | 4 | 14% |
Student > Master | 3 | 10% |
Student > Ph. D. Student | 3 | 10% |
Other | 2 | 7% |
Other | 6 | 21% |
Unknown | 7 | 24% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 6 | 21% |
Nursing and Health Professions | 3 | 10% |
Medicine and Dentistry | 3 | 10% |
Agricultural and Biological Sciences | 3 | 10% |
Neuroscience | 2 | 7% |
Other | 4 | 14% |
Unknown | 8 | 28% |