Molecular display of synthetic oligonucleotide libraries and their analysis with high throughput DNA sequencing

High throughput methods in molecular biology have changed the landscape of biomedical research. In particular, advances in massively parallel DNA sequencing and synthesis technologies are defining our genomes and the products they encode. In the first part of this thesis, we have constructed a rationally designed antibody library and analysis platform optimized for use with deep sequencing technologies. Libraries of fully defined oligonucleotides encode three complementarity determining regions (CDRs; L3 from the light chain, H2 and H3 from the heavy chain), and were combinatorially cloned into a synthetic single chain variable fragment (scFv) framework for molecular display. Our novel CDR sequence design utilized a hidden Markov model (HMM) that was trained on all antibody-antigen co-crystal complexes present in the Protein Data Bank. The resultant ~10¹² member library has been produced in ribosome display format, and was comprehensively analyzed over four rounds of antigen selections by multiplex paired-end Illumina sequencing. The HMM library generated multiple antibodies against an emerging cancer antigen and is the basis of a next generation antibody production platform. In a second application of these technologies, we have created a synthetic representation of the complete human proteome, which has been engineered for display on bacteriophage. We use this library together with deep DNA sequencing methods to profile the autoantibody repertoires of individuals with autoimmune disease in a procedure called phage immunoprecipitation sequencing (PhIP-Seq). In a proof-of-concept study, this method identified both known and novel autoantibodies contained in the spinal fluid of a control patient with paraneoplastic neurological syndrome. The study was then expanded to include a large scale automated screen of 289 independent antibody repertoires, including those from a large number of healthy donors, multiple sclerosis patients, rheumatoid arthritis patients, and type 1 diabetics. Our data describes each individual's unique "autoantibodyome", and defines a small set of recurrently targeted peptides in health and disease.

Department dc:contributor.department

Harvard--MIT Program in Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Larman, Harry Benjamin

Advisor dc:contributor.advisor

• Stephen J. Elledge.

• Harvard--MIT Program in Health Sciences and Technology.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/72912

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/72912