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Mol Cell Proteomics. 2017 Apr;16(4 suppl 1):S277-S289. doi: 10.1074/mcp.M116.065953. Epub 2017 Feb 21.

Identification of Antibody Targets for Tuberculosis Serology using High-Density Nucleic Acid Programmable Protein Arrays.

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From the ‡The Virginia G Piper Center for Personalized Diagnostics, The Biodesign Institute, Arizona State University, Tempe, Arizona, 85287.
§State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Radiation Medicine, Beijing, 102206, China.
¶Engineering Arts LLC, Tempe, Arizona 85287.
‖Department of Internal Medicine, Edendale Hospital, University of KwaZulu-Natal, Pietermaritzburg, South Africa.
**Departments of Medicine and.
From the ‡The Virginia G Piper Center for Personalized Diagnostics, The Biodesign Institute, Arizona State University, Tempe, Arizona, 85287;
**Departments of Medicine and
‡‡Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York 10461.


Better and more diverse biomarkers for the development of simple point-of-care tests for active tuberculosis (TB), a clinically heterogeneous disease, are urgently needed. We generated a proteomic Mycobacterium tuberculosis (Mtb) High-Density Nucleic Acid Programmable Protein Array (HD-NAPPA) that used a novel multiplexed strategy for expedited high-throughput screening for antibody responses to the Mtb proteome. We screened sera from HIV uninfected and coinfected TB patients and controls (n = 120) from the US and South Africa (SA) using the multiplex HD-NAPPA for discovery, followed by deconvolution and validation through single protein HD-NAPPA with biologically independent samples (n = 124). We verified the top proteins with enzyme-linked immunosorbent assays (ELISA) using the original screening and validation samples (n = 244) and heretofore untested samples (n = 41). We identified 8 proteins with TB biomarker value; four (Rv0054, Rv0831c, Rv2031c and Rv0222) of these were previously identified in serology studies, and four (Rv0948c, Rv2853, Rv3405c, Rv3544c) were not known to elicit antibody responses. Using ELISA data, we created classifiers that could discriminate patients' TB status according to geography (US or SA) and HIV (HIV- or HIV+) status. With ROC curve analysis under cross validation, the classifiers performed with an AUC for US/HIV- at 0.807; US/HIV+ at 0.782; SA/HIV- at 0.868; and SA/HIV+ at 0.723. With this study we demonstrate a new platform for biomarker/antibody screening and delineate its utility to identify previously unknown immunoreactive proteins.

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