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      Precise determination of the diversity of a combinatorial antibody library gives insight into the human immunoglobulin repertoire.

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          Abstract

          Antibody repertoire diversity, potentially as high as 10(11) unique molecules in a single individual, confounds characterization by conventional sequence analyses. In this study, we present a general method for assessing human antibody sequence diversity displayed on phage using massively parallel pyrosequencing, a novel application of Kabat column-labeled profile Hidden Markov Models, and translated complementarity determining region (CDR) capture-recapture analysis. Pyrosequencing of domain amplicon and RCA PCR products generated 1.5 x 10(6) reads, including more than 1.9 x 10(5) high quality, full-length sequences of antibody variable fragment (Fv) variable domains. Novel methods for germline and CDR classification and fine characterization of sequence diversity in the 6 CDRs are presented. Diverse germline contributions to the repertoire with random heavy and light chain pairing are observed. All germline families were found to be represented in 1.7 x 10(4) sequences obtained from repeated panning of the library. While the most variable CDR (CDR-H3) presents significant length and sequence variability, we find a substantial contribution to total diversity from somatically mutated germline encoded CDRs 1 and 2. Using a capture-recapture method, the total diversity of the antibody library obtained from a human donor Immunoglobulin M (IgM) pool was determined to be at least 3.5 x 10(10). The results provide insights into the role of IgM diversification, display library construction, and productive germline usages in antibody libraries and the humoral repertoire.

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          Author and article information

          Journal
          Proc Natl Acad Sci U S A
          Proceedings of the National Academy of Sciences of the United States of America
          Proceedings of the National Academy of Sciences
          1091-6490
          0027-8424
          Dec 01 2009
          : 106
          : 48
          Affiliations
          [1 ] Research Informatics, Rinat-Pfizer Inc., 230 East Grand Avenue, South San Francisco, CA 94080, USA.
          Article
          0909775106
          10.1073/pnas.0909775106
          2787155
          19875695
          c8a92196-183c-4933-94d2-a0eecc088257
          History

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