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Deciphering the Latency‐associated Sugar‐code to Detect and Eliminate Latent Reservoir

There is no surface biomarker that can reliably detect latently infected cells in vivo 1. Identification of such a marker would be advantageous to both identification of latent reservoirs (LR) and potentially to guiding potent therapies to specifically target latently infected cells. We propose a high risk/high impact study to address two areas of interest: #1 biomarkers or molecular signatures that can distinguish the reservoir from uninfected cells in vivo during suppressive ART, and #2 mechanisms to induce cell death specifically in HIV-infected cells in vivo.

Glycosylated proteins (glycoproteins) are ubiquitous on the cell surface and commonly serve as targets to detect and eliminate specific cell populations. Nearly all current US Food and Drug Administration (FDA)-approved cancer biomarkers are glycoproteins, and these often serve as therapeutic targets for cancer cure strategies 2. Strategies that have proven especially successful include targeting human epidermal growth factor receptor 2 (HER2) with the FDA-approved monoclonal antibody trastuzumab in breast cancer, targeting PD-1/PD-L1 in multiple cancers, and targeting multidrug resistance–associated protein-1 (MRP1) with vincristine to eradicate latent human cytomegalovirus 3, 4. The identification of aberrantly-expressed cell surface glycoproteins on cells that are latently infected with HIV could prove central to detect/eradicate the LR. Yet, to date the cell surface glycoproteome of latently infected cells in HIV remains unknown, but we believe it is extremely relevant to a cure. The challenge to define the glycoproteome of LR has been rooted in the lack of a technology able to comprehensively characterize the glycoproteome of cells, especially in a scenario with a small number of cells such as is found in the LR in existing in vivo models of latent infection.

To address the technological challenge, we have engineered a chemical-enzymatic technology to comprehensively characterize the glycoproteome using state-of-the-art mass spectrometry (MS) and innovative analytical software, named GPQuest 5, 6, 7. Our high-throughput MS technology directly reports abundance of thousands glycoproteins with detailed information on glycosylation sites, occupancy (percentage of glycosylation at a specific glycosylation site) and glycans that are impractical to obtain using proteomics, genetics and antibody-based approaches 5. It is well-appreciated that analysis of the glycoproteome is very challenging because glycoproteins are often membrane-bound and in relatively low abundance. Moreover, combination of hundreds of structurally-different glycans together with thousands of glycosylation sites generates tremendous heterogeneity of site-specific protein glycosylation that is difficult to analyze using currently available computational power and false discovery rate calculations 6, 7. Our methodology has solved the problems and allows the simultaneous large-scale profiling and monitoring of intact glycopeptides (peptide with glycan attached), glycans and glycosylation sites by a single method 5. The analytical platform has not been applied in HIV research and represents the most advanced, high-throughput and comprehensive methodology to characterize the glycoproteome in the specimens. The glycoproteomic platform is fully developed and validated in the study of cancer cells 5, 6, 7. Deciphering the otherwise unknown cell surface glycoproteome of the LR can identify novel targets for therapeutics and may lead to a cure strategy.



1.         Spina CA, Anderson J, Archin NM, Bosque A, Chan J, Famiglietti M, et al. An in-depth comparison of latent HIV-1 reactivation in multiple cell model systems and resting CD4+ T cells from aviremic patients. PLoS pathogens 2013, 9(12): e1003834.


2.         Meany DL, Chan DW. Aberrant glycosylation associated with enzymes as cancer biomarkers. Clinical proteomics 2011, 8(1): 7.


3.         O'Sullivan Coyne G, Gulley JL. Adding fuel to the fire: immunogenic intensification. Human vaccines & immunotherapeutics 2014, 10(11): 3306-3312.


4.         Weekes MP, Tan SY, Poole E, Talbot S, Antrobus R, Smith DL, et al. Latency-associated degradation of the MRP1 drug transporter during latent human cytomegalovirus infection. Science 2013, 340(6129): 199-202.


5.         Sun S, Shah P, Eshghi ST, Yang W, Trikannad N, Yang S, et al. Comprehensive analysis of protein glycosylation by solid-phase extraction of N-linked glycans and glycosite-containing peptides. Nat Biotechnol 2016, 34(1): 84-88.


6.         Toghi Eshghi S, Shah P, Yang W, Li X, Zhang H. GPQuest: A Spectral Library Matching Algorithm for Site-Specific Assignment of Tandem Mass Spectra to Intact N-glycopeptides. Analytical chemistry 2015, 87(10): 5181-5188.


7.         Yang W, Shah P, Toghi Eshghi S, Yang S, Sun S, Ao M, et al. Glycoform analysis of recombinant and human immunodeficiency virus envelope protein gp120 via higher energy collisional dissociation and spectral-aligning strategy. Analytical chemistry 2014, 86(14): 6959-6967.

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