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      Application of a synthetic human proteome to autoantigen discovery through PhIP-Seq

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          Abstract

          In this study, we improve on current autoantigen discovery approaches by creating a synthetic representation of the complete human proteome, the T7 “peptidome” phage display library (T7-Pep), and use it to profile the autoantibody repertoires of individual patients. We provide methods for 1) designing and cloning large libraries of DNA microarray-derived oligonucleotides encoding peptides for display on bacteriophage, and 2) analysis of the peptide libraries using high throughput DNA sequencing. We applied phage immunoprecipitation sequencing (PhIP-Seq) to identify both known and novel autoantibodies contained in the spinal fluid of three patients with paraneoplastic neurological syndromes. We also show how our approach can be used more generally to identify peptide-protein interactions and point toward ways in which this technology will be further developed in the future. We envision that PhIP-Seq can become an important new tool in autoantibody analysis, as well as proteomic research in general.

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          Most cited references27

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          A genecentric Human Protein Atlas for expression profiles based on antibodies.

          An attractive path forward in proteomics is to experimentally annotate the human protein complement of the genome in a genecentric manner. Using antibodies, it might be possible to design protein-specific probes for a representative protein from every protein-coding gene and to subsequently use the antibodies for systematical analysis of cellular distribution and subcellular localization of proteins in normal and disease tissues. A new version (4.0) of the Human Protein Atlas has been developed in a genecentric manner with the inclusion of all human genes and splice variants predicted from genome efforts together with a visualization of each protein with characteristics such as predicted membrane regions, signal peptide, and protein domains and new plots showing the uniqueness (sequence similarity) of every fraction of each protein toward all other human proteins. The new version is based on tissue profiles generated from 6120 antibodies with more than five million immunohistochemistry-based images covering 5067 human genes, corresponding to approximately 25% of the human genome. Version 4.0 includes a putative list of members in various protein classes, both functional classes, such as kinases, transcription factors, G-protein-coupled receptors, etc., and project-related classes, such as candidate genes for cancer or cardiovascular diseases. The exact antigen sequence for the internally generated antibodies has also been released together with a visualization of the application-specific validation performed for each antibody, including a protein array assay, Western blot analysis, immunohistochemistry, and, for a large fraction, immunofluorescence-based confocal microscopy. New search functionalities have been added to allow complex queries regarding protein expression profiles, protein classes, and chromosome location. The new version of the protein atlas thus is a resource for many areas of biomedical research, including protein science and biomarker discovery.
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            CTdatabase: a knowledge-base of high-throughput and curated data on cancer-testis antigens

            The potency of the immune response has still to be harnessed effectively to combat human cancers. However, the discovery of T-cell targets in melanomas and other tumors has raised the possibility that cancer vaccines can be used to induce a therapeutically effective immune response against cancer. The targets, cancer-testis (CT) antigens, are immunogenic proteins preferentially expressed in normal gametogenic tissues and different histological types of tumors. Therapeutic cancer vaccines directed against CT antigens are currently in late-stage clinical trials testing whether they can delay or prevent recurrence of lung cancer and melanoma following surgical removal of primary tumors. CT antigens constitute a large, but ill-defined, family of proteins that exhibit a remarkably restricted expression. Currently, there is a considerable amount of information about these proteins, but the data are scattered through the literature and in several bioinformatic databases. The database presented here, CTdatabase (http://www.cta.lncc.br), unifies this knowledge to facilitate both the mining of the existing deluge of data, and the identification of proteins alleged to be CT antigens, but that do not have their characteristic restricted expression pattern. CTdatabase is more than a repository of CT antigen data, since all the available information was carefully curated and annotated with most data being specifically processed for CT antigens and stored locally. Starting from a compilation of known CT antigens, CTdatabase provides basic information including gene names and aliases, RefSeq accession numbers, genomic location, known splicing variants, gene duplications and additional family members. Gene expression at the mRNA level in normal and tumor tissues has been collated from publicly available data obtained by several different technologies. Manually curated data related to mRNA and protein expression, and antigen-specific immune responses in cancer patients are also available, together with links to PubMed for relevant CT antigen articles.
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              Autoantibody signatures in prostate cancer.

              New biomarkers, such as autoantibody signatures, may improve the early detection of prostate cancer. With a phage-display library derived from prostate-cancer tissue, we developed and used phage protein microarrays to analyze serum samples from 119 patients with prostate cancer and 138 controls, with the samples equally divided into training and validation sets. A phage-peptide detector that was constructed from the training set was evaluated on an independent validation set of 128 serum samples (60 from patients with prostate cancer and 68 from controls). A 22-phage-peptide detector had 88.2 percent specificity (95 percent confidence interval, 0.78 to 0.95) and 81.6 percent sensitivity (95 percent confidence interval, 0.70 to 0.90) in discriminating between the group with prostate cancer and the control group. This panel of peptides performed better than did prostate-specific antigen (PSA) in distinguishing between the group with prostate cancer and the control group (area under the curve for the autoantibody signature, 0.93; 95 percent confidence interval, 0.88 to 0.97; area under the curve for PSA, 0.80; 95 percent confidence interval, 0.71 to 0.88). Logistic-regression analysis revealed that the phage-peptide panel provided additional discriminative power over PSA (P<0.001). Among the 22 phage peptides used as a detector, 4 were derived from in-frame, named coding sequences. The remaining phage peptides were generated from untranslated sequences. Autoantibodies against peptides derived from prostate-cancer tissue could be used as the basis for a screening test for prostate cancer. Copyright 2005 Massachusetts Medical Society.
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                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nat. Biotechnol.
                Nature biotechnology
                1087-0156
                1546-1696
                30 March 2011
                22 May 2011
                June 2011
                19 September 2014
                : 29
                : 6
                : 535-541
                Affiliations
                [1 ]Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
                [2 ]Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
                [3 ]Department of Genetics, Harvard University Medical School, and Division of Genetics, Howard Hughes Medical Institute, Brigham and Women's Hospital, Boston, MA, USA
                [5 ]Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
                [6 ]Department of Genetics, Harvard University Medical School, Boston, MA, USA
                [7 ]Division of Neuro-Oncology, Department of Neurosciences, U.C. San Diego, Moores Cancer Center, La Jolla, CA, USA
                [8 ]Agilent Technologies, Genomics, Santa Clara, CA, USA
                Author notes
                [4]

                Present address: Biogen Idec, Cambridge, Massachusetts, USA

                AUTHOR CONTRIBUTIONS

                S.J.E. conceived the project, which was supervised by N.L.S. and S.J.E. Z.Z. designed the DNA sequences for synthesis. Oligo libraries were constructed by E.M.L. Cloning was performed by M.Z.L., M.A.M.G, and N.L.S. The T7-Pep, T7-NPep, and T7-CPep phage libraries were constructed by N.L.S. and characterized by N.L.S. and H.B.L. The PhIP-Seq protocol was developed and implemented by H.B.L. Clinical evaluations and patient sample acquisitions were performed by S.K. Statistical analysis of PhIP-Seq data was conceived by U.L. under supervision of G.M.C. and implemented by H.B.L. PhIP-Seq candidates were confirmed by H.B.L. RPA2 IP was performed by A.C. The manuscript was prepared by H.B.L. and edited by N.L.S. and S.J.E.

                Article
                NIHMS284271
                10.1038/nbt.1856
                4169279
                21602805
                158f6ebd-b8fb-40eb-988e-a85e9e35ad77

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                History
                Categories
                Article

                Biotechnology
                synthetic biology,proteomics,phage display,humoral autoimmunity,paraneoplastic neurological disorder,protein-protein interactions

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