HIV latently infected cells are very rare in HIV+ individuals on suppressive anti-retroviral therapy (ART). To target these latently infected cells without latency-reactivation, it is critical to identify specific cell surface targets associated wth the latent infection. Compelling reports from studying proteins such as CD2, PD-1, LAG-3 and TIGIT suggest that there is a distinct profile of cell surface proteins for enrichment of latently infected cells ex vivo. The specific changes of these proteins during the establishment of latent infection remain to be elucidated. Interestingly, all of the above proteins are glycosylated (glycoproteins). It is estimated that there are over 1,000 cell surface glycoproteins that form a complex and dynamic cell surface phenotype. Interrogating the cell surface glycoproteome will maximize the chances to identify the specific cell surface glycoproteins to target latently infecte cells. Toward this goal, we have developed integrated glycoproteomics that can quantify the glycoproteome using chemical-enzymatic approaches for glycoprotein isolation followed by quantitative analysis using state-of-the-art mass spectrometry (MS). Our innovative method acquires a direct readout from the cell surface glycoproteome. The workflow is coupled with high throughput screen/validation assays using parallel reaction monitoring based mass spectrometry (PRM-MS) to monitor the otherwise complex surface glycoproteome of latently infected cells. We therefore propose to screen the cell surface glycoproteins in an in vitro-generated latently infected primary cell model to reveal latency- associated cell surface glycoproteins using glycoproteomics and PRM-MS in the R21 phase and target latently infected cells ex vivo using antibodies in the R33 phase. In the R21 phase, we will aim to discover the dynamic change of the glycoproteome during establishment of an in vitro-generated latently infected primary cell model (aim 1). The latency-associated glycoproteins will be validated using parallel reaction monitoring mass spectrometry (PRM-MS), a targeted mass spectrometry technique that can validate a large number of changed glycoproteins (aim 2). The analytical methods are highly innovative and have the capability to uncover the specific glycoprotein changes from thousands of cell surface glycoproteins for targeting latently infected cells. The R33 phase will be undertaken only if the well-defined milestones are achieved. We propose three studies to determine the roles of specific glycoprotein changes on latently infected cell surface ex vivo (aim 3). (1) The correlation of glycoproteins to latent infection will be determine to uncover latency-associated glycoproteins. (2) Glycoprotein-specific antibody will be used to capture latently infected cells. (3) A panel of glycoproteins will be selected to improve specificiy and/or coverage of targeting latently infected cells. The overall goal of this study is to determin the specific cell surface glycoprotein(s), which enable targeting the rare latently infected cells x vivo without the need to reactivate HIV gene expression.

Public Health Relevance

It is essential to identify cell surface proteins associated with HIV latency in primary cells to enable these cells to be depleted or selectively targeted with therapeutics. Compelling reports suggest that latently infected primary cells have a distinct profile of cell surface glycoproteins. This application will use innovative and integrated glycoproteomic technologies to uncover the specific cell surface glycoproteins, which enable targeting latently infected primary cells without the need to reactivate HIV gene expression.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21AI122382-01
Application #
9050031
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Kuo, Lillian S
Project Start
2015-12-01
Project End
2017-11-30
Budget Start
2015-12-01
Budget End
2016-11-30
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Pathology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
Yang, Shuang; Hu, Yingwei; Sokoll, Lori et al. (2017) Simultaneous quantification of N- and O-glycans using a solid-phase method. Nat Protoc 12:1229-1244
Yang, Weiming; Shah, Punit; Hu, Yingwei et al. (2017) Comparison of Enrichment Methods for Intact N- and O-Linked Glycopeptides Using Strong Anion Exchange and Hydrophilic Interaction Liquid Chromatography. Anal Chem 89:11193-11197
Yang, Shuang; Clark, David; Liu, Yang et al. (2017) High-throughput analysis of N-glycans using AutoTip via glycoprotein immobilization. Sci Rep 7:10216
Yang, Shuang; Zhang, Lei; Thomas, Stefani et al. (2017) Modification of Sialic Acids on Solid Phase: Accurate Characterization of Protein Sialylation. Anal Chem 89:6330-6335
Zhou, Jianliang; Yang, Weiming; Hu, Yingwei et al. (2017) Site-Specific Fucosylation Analysis Identifying Glycoproteins Associated with Aggressive Prostate Cancer Cell Lines Using Tandem Affinity Enrichments of Intact Glycopeptides Followed by Mass Spectrometry. Anal Chem 89:7623-7630
Zarif, Jelani C; Yang, Weiming; Hernandez, James R et al. (2017) The Identification of Macrophage-enriched Glycoproteins Using Glycoproteomics. Mol Cell Proteomics 16:1029-1037
Yang, Shuang; Höti, Naseruddin; Yang, Weiming et al. (2017) Simultaneous analyses of N-linked and O-linked glycans of ovarian cancer cells using solid-phase chemoenzymatic method. Clin Proteomics 14:3
Toghi Eshghi, Shadi; Yang, Weiming; Hu, Yingwei et al. (2016) Classification of Tandem Mass Spectra for Identification of N- and O-linked Glycopeptides. Sci Rep 6:37189
Jia, Xingwang; Chen, Jing; Sun, Shisheng et al. (2016) Detection of aggressive prostate cancer associated glycoproteins in urine using glycoproteomics and mass spectrometry. Proteomics 16:2989-2996
Yang, Weiming; Jackson, Brooks; Zhang, Hui (2016) Identification of glycoproteins associated with HIV latently infected cells using quantitative glycoproteomics. Proteomics 16:1872-80

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