Abstract

Monoclonal antibodies (MAbs) are widely applied in basic research, medicine, and the pharmaceutical industry. Recently, applications and generations of MAbs have been increasingly attracting attention in many research areas since MAbs could be produced in large quantities with the development of genetic technology and antibody engineering. On the other hand, in recent years, phage display system has been developed for high-throughput isolation and generation of novel MAbs that have high affinity with various antigens. This technology is capable of constructing “Library” containing billions of phage repertoires displaying various antibody fragments, and rapid selection of a specific MAb from this phage library. Additionally, this technology has a great advantage that MAbs can be generated without immunization to animals. However, there are still relatively few reports confirming that useful MAbs can be derived from non-immune antibody libraries. The latter, as undertaken by current methods, seem unable to achieve the high quality required to produce useful MAbs for any desired antigen because cloning of antibody gene from non-immune donors is inefficient. This problem is caused by the fact that their RT-PCR primer sets, PCR conditions, and efficiency of subcloning through construction of antibody gene library cannot encompass all the antibody diversity. In an attempt to overcome some of these earlier problems, here we describe an optimized method to establish a high quality, non-immune library from mouse bone-marrow and spleen, and assess its diversity in terms of content of multiple antibodies for a wide antigenic repertoire. As an example of the application of the methodology, we describe the selection of specific MAbs binding to Luciferase and identify at least 18 different clones. Using this non-immune mouse antibody library, we also obtained MAbs for VEGF, VEGF receptor 2, TNF-α, and Pseudomonas Exotoxin, confirming the high quality of the library and its suitability for this application.

Full Text
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