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Glycoproteomic analysis identifies human glycoproteins secreted from HIV latently infected T cells and reveals their presence in HIV+ plasma

Abstract

Glycoproteins secreted into plasma from T cells infected with human immunodeficiency virus (HIV) latent infection may provide insight into understanding the host response to HIV infection in vivo. Glycoproteomics, which evaluates the level of the glycoproteome, remains a novel approach to study this host response to HIV. In order to identify human glycoproteins secreted from T cells with latent HIV infection, the medium from cultured HIV replication-competent T cells was compared with the medium from cultured parental A3.01 cells via solid phase extraction of glycopeptides (SPEG) and high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Using these methods, 59 human glycoproteins were identified as having significantly different abundance levels between the media from these two cell lines. The relevance of these 59 proteins to HIV infection in vivo was assessed in plasma from HIV+ and HIV- subjects. Comparison between T cell and plasma revealed that six glycoproteins (galectin-3-binding protein, L-selectin, neogenin, adenosine deaminase CECR1, ICOS ligand and phospholipid transfer protein) were significantly elevated in the HIV+ T cells and plasma studies. These findings suggest that the response of T cells harboring latent HIV infection contributed, in part, to the glycoprotein changes in HIV+ plasma. These proteins, once validated, could provide insight into host-HIV interaction.

Introduction

In 2011, there were approximately 34 million individuals living with HIV [1]. In the same year, it is estimated that there were approximately 2.5 million new infections and 1.7 million AIDS related deaths [1]. Although tremendous efforts have been devoted to prevent HIV transmission, to date, no effective HIV vaccine is available [2]. Instead, highly active antiretroviral therapy (HAART) is routinely used to treat patients infected with HIV and it has been shown to be effective in the prevention of HIV transmission [3]. HAART has greatly improved the lifespan and quality of life of the HIV infected population [4]. However, HIV related illnesses such as neurodegenerative diseases, cancers, and bacterial infections have emerged as important issues in HIV+ patients [58]. To identify proteins in a complex sample for the (early) detection, diagnosis, prognosis, treatment prediction and the elucidation of the pathogenesis of these diseases, proteomic studies have demonstrated their applicability in a high throughput fashion [911]. However, the urgent need for the identification of biomarkers of HIV and its related diseases remains unmet [8].

HIV is a lentivirus which belongs to the retrovirus family [12]. It primarily infects immune cells containing CD4 receptors, such as helper T cells, dendritic cells (DC) and macrophages, and establishes latent infection in these cells [13]. An inevitable step in the viral replication cycle is the integration of viral DNA into the T cell genome prior to either latent infection or active production of virions [14]. The infection of the immune cells, which reside in the blood and draining lymph tissues, results in the atypical secretion and shedding of disease-related proteins into the peripheral blood [15]. Thus, the blood of patients infected with HIV is considered to be an excellent source to identify proteins that are associated with HIV infection. Studies using proteomic approaches that aim to discover new biomarkers of HIV and its related diseases have identified protein abundance changes in plasma and serum [9, 10, 1619].

Glycoproteomics, a sub-proteomic approach that specifically investigates the levels of glycoproteins in a complex sample, has been widely applied for cancer biomarker discovery [2022]. This approach is promising because glycoproteins are ubiquitous in extracellular secretion, are known to be dysregulated in cancerous cells, and are likely to enter the bloodstream, making them attractive candidates for cancer biomarkers [23]. The significance of glycoproteins as biomarkers has shown great promise in that most of the U.S. Food and Drug Administration (FDA) approved cancer biomarkers are glycoproteins. These biomarkers include prostate-specific antigen (PSA) for prostate cancer, CA125 for ovarian cancer, and CA15-3 for breast cancer [24, 25]. In HIV infection, the host systemic reaction to infection of immune cells, inflammation, chronic activation, repeated evasion-recognition and reconstitution of immune system likely triggers the secretion or shedding of a large number of glycoproteins into the bloodstream [26, 27]. It has been observed that the global and HIV-specific antibody-glycosylation profile is altered during HIV infection [28]. It is possible that the aberrant presence of glycoproteins other than antibodies may be specifically associated with HIV infection given the unique immune cell tropism of the virus [13]. However, the change in glycoproteins caused by HIV infection has not been determined in previous studies and remains unclear.

In the blood, N-linked glycosylated proteins are particularly interesting because N-linked glycosylation is a designated signal for the extracellular secretion of proteins [15, 22]. To study the N-linked glycoproteins in a high throughput manner, we previously developed a method for the hydrazide chemistry-based solid phase capture of glycopeptides and coupled it with the use of PNGase F for the specific release of ‘formerly’ N-glycosylated peptides (only the peptides originally attached to glycans) [2931]. The isolated formerly N-glycosylated peptides are analyzed by LC separation followed by tandem mass spectrometry (MS/MS), which allows the high throughput identification and quantification of a large number of glycoproteins [3234]. This feature of the technique provides a unique opportunity to detect protein changes in plasma-derived immune cells [35, 36].

In this study, LC-MS/MS based quantitative glycoproteomics was used to examine the altered levels of glycoproteins associated with HIV latent infection in T cells (ACH-2 cell lines) and plasma from HIV+ patients [29, 30]. The advantage of label-free LC-MS-MS/MS based quantitative glycoproteomics is the ability to compare an unlimited number of samples with a deeper coverage of identification compared to strategies that incorporate stable isotope labeling [37, 38]. The current study started with the identification of dysregulated glycoproteins from the media of T cell lines with HIV latent infection to determine putative glycoprotein candidates, followed by the use of HIV+ plasma to identify the glycoprotein changes that could be influenced by the aberrant secretion of glycoproteins from the T cells harboring HIV DNA in their genomes. To our knowledge, this is the first study to identify glycoproteins that are potentially associated with HIV infection in plasma by high throughput glycoproteomics using HIV+ T cells and plasma.

Methods

Chemicals

The bicinchoninic acid (BCA) protein assay kit was purchased from Pierce (Rockford, IL); hydrazide support and sodium periodate were purchased from Bio-Rad (Hercules, CA); sequencing-grade trypsin was purchased from Promega (Madison, WI); PNGase F was purchased from New England Biolabs (Ipswich, MA); Sep-Pak C18 1 cc Vac Cartridges were purchased from Waters (Milford, MA); and acetonitrile (ACN), trifluoroacetic acid (TFA), formic acid, urea, tris(2-carboxyethyl)phosphine (TCEP), iodoacetamide, phorbol 12-myristate 13-acetate (PMA), 5 M NaCl, sodium dodecyl sulfate (SDS), 10× phosphate buffered saline buffer (PBS) pH 7.4 and 1 M Tris–HCl pH 8.0 buffer were purchased from Sigma-Aldrich (St. Louis, MO).

Patient samples

Plasma was tested for HIV antibody and HIV RNA viral loads were determined if the plasma tested positive for HIV. The plasma was aliquoted and immediately stored at -80°C until use (Table 1). Prior to the study, samples were de-identified. The Johns Hopkins Medicine Institutional Review Board (IRB) approved the use of the samples.

Table 1 Demographics and viral loads of patients

Cell culture

The HIV-1 latently infected T cell line ACH-2 and its parental cell line A3.01 were obtained from Dr. Thomas Folks through the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH [3941]. The ACH-2 cell line is a sub-clone of the human A3.01 T cell line derived from acute infection with the LAV strain of HIV-1. The HIV DNA is integrated into the genome of the T cell line. Low levels of supernatant RT and p24 antigen are constitutively produced in ACH-2 cells. HIV-1 replication can be induced with phorbol 12-myristate 13-acetate (PMA) or IL-2 to produce high levels of infectious HIV-1. Thus, the cell line is HIV replication competent. However, we did not stimulate the cells with PMA or IL2 in the study. ACH-2 and A3.01 cells were cultured in RPMI 1640 supplemented with 2 mm L-glutamine (Gibco Laboratories, Grand Island, NY), 100 U/ml penicillin, 100 g/ml streptomycin (Invitrogen, Carlsbad, CA) and 10% (v/v) heat-inactivated fetal calf serum (FCS, Hyclone Laboratories, Logan, UT). In this study, cells were pelleted and washed in serum free medium 5 times and resuspended in serum free medium. After 48 hours of incubation at 37°C in 5% CO2, the cells were pelleted by low speed centrifugation and the medium was harvested, filtered through a 0.22 μm filter unit and concentrated using a 3 kDa cut-off centrifugal filter unit (Millipore, Bedford, MA). The concentrated medium was diluted in 800 μl of 8 M urea in 0.2 M Tris–HCl with 0.1% (w/v) SDS solution (pH 8.0). Protein concentration was determined by BCA assay.

N-linked glycopeptide isolation

Cell culture media (1 mg protein) and plasma (20 μl containing approximately 1.6 mg protein) were added to 8 M urea in 0.2 M Tris–HCl with 0.1% (w/v) SDS solution (pH 8.0). Proteins were reduced with 10 mM TCEP in Tris–HCl pH 8.0 at 37°C for 1 hour. Proteins were alkylated with 10 mM iodoacetamide and incubated at room temperature (RT) in the dark with shaking for 30 min. Samples were diluted into 6 ml 0.2 M Tris–HCl pH 8.0 to decrease the urea concentration to less than 2 M and to decrease the protein concentration. Trypsin (0.5 μg/μl, enzyme/protein ratio of 1/40 w/w) was added to digest the proteins at 30°C overnight. After digestion, 5 μg of sample was resolved on a tricine gel and silver staining was used to visualize the digestion products. The disappearance of high molecular bands and the appearance of a smear of bands with a molecular weight less than 10 kDa indicated the completion of digestion. The peptides were de-salted using a C18 cartridge according to the manufacturer’s instructions and then the peptides were oxidized by incubation with 10 mM sodium periodate in 80% ACN (v/v) 0.1% TFA (v/v) for 1 hour at RT in the dark. The oxidized peptides were purified by C18 cartridge, eluted in 80% ACN (v/v) 0.1% TFA (v/v) and mixed with 50 μl of (50% slurry) hydrazide support prewashed with 1 ml deionized water. The mixture was incubated with gentle shaking at RT overnight for the coupling reaction. The hydrazide support was washed three times with 800 μl of 1.5 M NaCl followed by three times with 800 μl of water to remove non-coupled peptides. The support was finally washed twice with 200 μl of 1× G7 buffer and incubated with 3 μl of PNGase F in 50 μl of 1× G7 buffer at 37°C to specifically release formerly N-glycosylated peptides which were purified by a C18 cartridge. Then, the peptides were dried in a speed-vac and re-suspended in 40 μl of 0.1% formic acid prior to MS analysis. For pooled glycopeptides, an equal volume (1 μl) of the formerly N-glycosylated peptides from each sample was pooled into either the HIV infected or uninfected pools.

LC-MS/MS analysis

The formerly N-glycosylated peptides (1.5 μg) were separated using a Dionex Ultimate 3000 RELC nano system (Thermo Scientific) with a 75 μm × 15 cm Acclaim PepMap100 analytical column (Thermo Scientific) protected by a 2 cm guard column (Thermo Scientific). The mobile phase flow rate was 300 nl/min with 0.1% formic acid in water (A) and 0.1% formic acid/95% acetonitrile (B). The gradient profile was set as follows: 4-35% B for 70 min, 35-95% B for 5 min, 95% B for 10 min and equilibration at 4% B for 15 min. MS analysis was performed using an Orbitrap Velos Pro mass spectrometer (Thermo Scientific). The spray voltage was set at 2.2 kV. Orbitrap MS1 spectra (AGC 1×106) were acquired from 400–1800 m/z at 60 K resolution followed by data-dependent HCD MS/MS (7,500 resolution, collision energy 45%, activation time 0.1 ms) of the ten most abundant ions using an isolation width of 2.0 Da. Charge state screening was enabled to reject unassigned and singly charged ions. A dynamic exclusion time of 35 sec was used to discriminate against previously selected ions.

Protein identification and label-free quantitation

The quality of the Raw files from the LTQ-Orbitrap Velos Pro was evaluated by the Trans-Proteomic Pipeline (TPP) and searched against a human protein database (the International Protein Index human protein database, version 3.87). The precursor mass tolerance was 20 ppm and the MS/MS tolerance was 0.06 Da. The modification parameters of the database search were as follows: oxidized methionine (addition of 15.995 Da to Met), PNGase F-catalyzed conversion of Asn to Asp (addition of 0.984 Da to Asn) and Cys modification (addition of 57.021 to Cys). A maximum of two missed tryptic cleavage sites were used. The assigned peptides were evaluated by INTERACT and PeptideProphet with a minimum probability score of 0.7 [4244]. Label-free quantitation was performed using MaxQuant ver. 1.3.0.5 and searched against the same human protein IPI database ver. 3.87 containing a total of 91,464 entries [38]. The default parameters with a 1% false discovery rate (FDR) were used except for the following: enzyme: trypsin; variable modifications: oxidation (M) and deamidation (NQ); and filter labeled amino acid unselected. The output peptide.txt file containing the identified peptide sequences and corresponding MS peak intensities were used for the quantitation.

Data analysis

The output data from MaxQuant were converted to Microsoft Excel files. First, peptides that were considered contaminants, peptides that were identified in the reverse database search and peptides lacking N deamination were deleted. Second, peptides without an NXS/T (where X is any amino acid except proline) motif were deleted. The intensities of the remaining glycopeptides were normalized by dividing the total intensity of the identified glycopeptides in each LC-MS/MS run. The mean intensity of all the glycopeptides from a glycoprotein that had intensities greater than zero was used for inter-sample protein comparison. Using pooled samples, a cut-off ratio of 2-fold was used while a Student’s t-test with a p-value ≤ 0.05 was used to analyze the individual samples.

Results

Quantitative secretion of glycoproteins from T cells with HIV latent infection

To address whether HIV latent infection was associated with aberrant secretion of glycoproteins from T cells, serum-free culture media of HIV-1 latently infected T-lymphocyte ACH-2 cells and the parental cell line A3.01 cells were studied through the use of label free glycoproteomics. To evaluate the viral production of these two cultured cell lines, Western blotting-based immunoreactivity of gp120 and gp41 in ACH-2 cells stimulated by PMA was observed, but no immunoreactivity was detected in A3.01 cells was evident, demonstrating the status of latent infection in ACH-2 cells (Additional file 1: Figure S1). Serum-free medium of T cells was collected and processed using a SPEG-based method to isolate formerly N-glycosylated peptides as described in the Methods section. Subsequently, the reconstituted formerly glycosylated peptides from each sample were analyzed by three LC-MS/MS technical replicates. The quality of the reproducible LC-MS/MS data was inspected using Pep3D images generated from Trans Proteomic Pipeline (TPP) to evaluate retention time consistency and MS2 coverage (Additional file 2: Figure S2). TPP identified 829 unique glycopeptides from 411 unique glycoproteins with N deamidation within the consensus N-linked glycosylation motif (NXS/T, where X is any amino acid except proline). The quality of the data that were evaluated by TPP was adequate for downstream label-free comparison. Accordingly, MaxQuant was used to calculate the peak intensities, resulting in the identification of 671 glycopeptides from 375 glycoproteins. After normalization, 326 proteins could be quantitatively compared between the samples based on their fold-change and p- value (Figure 1). Using criteria of a fold-change of 3 and a p- value < 0.05, 59 glycoproteins were identified as being significantly differentially expressed (Figure 1B & Table 2).

Figure 1
figure1

Plots of the 326 proteins for label-free quantitative comparison between ACH-2 and A3.01 cell media. A) Log2 fold-change plot of the proteins after normalization. B) Volcano plot of the proteins. Proteins with greater than a 3-fold change and p- value < 0.05 are colored in red and green. Green squares indicate proteins that were also increased in the HIV+ plasma sample.

Table 2 Differentially expressed proteins in the media of ACH-2 and A3.01 cells

Elevated plasma glycoprotein might be influenced by HIV latently infected T cells

To determine whether proteins secreted by HIV latently infected T cells could be detected in HIV+ plasma, 20 plasma samples from 10 HIV+ and 10 HIV- patients were evaluated. Although the time since HIV infection was not known, it was possible to assume that all the HIV+ samples were not recently infected according to the clinical observation in the HPTN lab. In theory, these plasma samples could harbor proteins secreted from T cells with HIV latent infection. A schematic flow chart of the approach used to study the changes in glycoprotein relative abundance is shown in Figure 2. The 20 plasma samples were processed individually using SPEG-glycoproteomics and pooled as described in the Methods section. The pooled samples were used to screen the glycoproteins between these two groups. Label-free quantification was used to analyze data from 4 consecutive LC-MS/MS runs for the HIV+ pool and 3 LC-MS/MS runs for the HIV- pool. The quality of the LC-MS/MS data was again evaluated using Pep3D images (Additional file 3: Figure S3). Eight hundred fifty unique glycopeptides from 221 unique glycoproteins were identified by TPP (data not shown). MaxQuant analysis identified 399 unique glycopeptides from 183 glycoproteins (data not shown). Of these proteins, 103 glycoproteins were quantitatively compared between the HIV+ and HIV- groups using a cut-off ratio of 1.3-fold (data not shown). To determine whether these proteins were quantitatively different in individuals from each group, an equal volume (1 μl per LC-MS/MS injection) of the 20 individual plasma samples was analyzed by LC-MS/MS with 3 technical replicates for each plasma sample followed again by TPP quality evaluation and MaxQuant analysis (588 unique glycopeptides from 253 glycoproteins were identified). This step semi-verified and reduced the total number of differentially expressed proteins from 103 to 38 using a p- value < 0.05 and a cut-off ratio of 1.5-fold (Table 3). A comparison between the differential 38 proteins in the plasma and the 59 proteins detected in the T cell study revealed that four proteins (galecin-3-binding protein, L-selectin, neogenin and adenosine deaminase CECR1) were identified in both analyses as having a greater than 3-fold increased relative abundance in the medium of ACH-2 cells and a 2-fold increased relative abundance in HIV+ plasma (Table 3). Noticeably, compared to A3.01 cells, galectin-3-binding protein demonstrated the most significant upregulation which was more than 45-fold increased compared to the level that was detected in the medium of ACH-2 cells (Table 3). In addition, when the 38 proteins in plasma were searched against the 326 proteins observed in the T cell medium study, two proteins, phospholipid transfer protein and ICOS ligand, were found to be increased by 5.1- and 2.6-fold, respectively, although the former had a p- value slightly greater than 0.05 in the T cell study (Table 3). The elevated levels of these six proteins in the medium of ACH-2 cells and HIV+ plasma suggested that their up-regulation in the HIV+ plasma might be partially due to HIV latently infected T cells.

Figure 2
figure2

Strategy used for the identification of differentially expressed glycoproteins in HIV + human plasma.

Table 3 N-linked glycoprotein changes in the plasma of HIV + and HIV - patients and their changed levels in T cells with HIV latent infection

Discussion

The identification of protein changes in the plasma of patients infected with HIV could lead to the identification of biomarkers that have roles in pathogenesis and that have clinical applications. However, as an important area of the proteomic study of post translational modifications (PTM), glycoproteomics has not been applied in HIV studies. One of the main concerns is whether the identified change of protein abundance in the patients is specific to HIV infection. In an attempt to address this question, we studied in vitro T-cells with latent HIV (i.e. T cells with HIV DNA integrated into the genome of the cell but not producing virions) to identify the glycoproteins that are preferentially secreted from this type of T cells. The stage of HIV DNA integration in the viral life cycle is particularly important because it occurs prior to latent and productive infection. Presumably, the productive infection of HIV to a large extent would trigger more intensive host responses than latent infection. However, the intracellular material from dying cells and the intense host response due to productive infection might result in the detection of an abundance of dysregulated proteins. In contrast, fewer dysregulated proteins would, presumably, be secreted silently during the latent stage of T cells. These secreted proteins could constantly modulate the host immune system due to the longer survival time of the cells. These cells also remain HIV replication-competent for potential invasion of the immune system.

One drawback of using a transformed cell line is that it does not always function similarly to native infected CD4+ primary cells that would commonly be used for future data validation. The identified glycoproteins from T cells media provide a list of protein candidates that may have a regulatory role in host-HIV interaction. The association between the T cell-secreted glycoproteins and HIV infection in vivo was then analyzed using HIV+ plasma. Based on the viral load data (Table 1), the patient population likely represented latent and active viral replication. It is worth noting that HIV latent infection in T cells persisted even when viral replication was active. The purpose of using patient plasma was to determine whether the glycoproteins identified from the T cell medium could be detected and whether they were differentially expressed in HIV infection in general. This study of HIV infected patient plasma resulted in the identification of six proteins that were also present in the T cell media. Although the specificity of the molecules to HIV infection is not conclusive and requires further determination, this first application of glycoproteomics to HIV latently infected T cells and HIV+ plasma elucidated significantly higher levels of glycoproteins that could aid future studies that are designed to understand host-HIV interaction in vivo.

HIV latent infection could affect the protein T cell protein secretion, and the secreted proteins could be potent immuno-modulators. We hypothesized that the secretion or leaking of protein from these HIV infected T cells might contribute to the dysregulation of glycoproteins in the plasma of HIV-infected patients. To evaluate this possibility, ACH-2 and its parental A3.01 cell line were studied because of their ability to mimic the latent infection of HIV, although these cell lines behave differently from canonical peripheral blood mononuclear cell (PBMC) in many aspects. Nevertheless, the T cell lines were homogeneous and they provided direct evidence supporting the proposed hypothesis. Our glycoproteomic study of the media from these T cells identified 59 glycoproteins with at least a 3-fold change and p- value < 0.05. These glycoproteins could be of interest because their increased levels might have a regulatory effect in the host response. Next, to determine whether any of the changes in protein levels in the T cell media were also detected in the plasma from HIV+ patients, we employed a strategy that used pooled samples for the initial screen and individual samples for the downstream high throughput verification. The use of this strategy is thought to solve some of the issues associated with ELISA testing (i.e. relatively poor sensitivity of detection and lack of cost effectiveness in evaluating a large number of proteins). Although the strategy limited the sample size here, it is noteworthy that a strategy could be developed that begins with the analysis of pooled samples and is then followed by verification using a predetermined number of randomly selected samples from the pools.

The analysis of pooled samples identified 103 glycoproteins between the HIV+ and HIV-group with a cut-off ratio of 1.3-fold. The low cut-off ratio retained as many proteins as possible in the list for subsequent MS verification. The 20 samples were then analyzed individually by three technical replicates and the results indicated that 38 proteins were differentially expressed between groups. The analysis of individual samples permitted the statistical evaluation (p- value ≤ 0.05) of each protein in the list. A fold change in the same trend observed in the analysis of the pooled and individual samples provided a means to verify these proteins. These 38 proteins accounting for 36.9% of the total 103 proteins suggested that HIV infection has a moderate effect on glycoproteins, although it was not clear whether the effect was on protein expression or the glycosylation level of the changed glycoproteins.

Among these 38 proteins, four proteins – galectin-3-binding protein, L-selectin, neogenin, and phospholipid transfer protein – were determined to have an increase of at least 3-fold in the medium of virus latently infected T cells and a 2-fold increase in HIV+ plasma. Galectin-3-binding protein was the most significantly up-regulated protein in the medium of ACH-2 cells. Although galectin-3-binding protein has been reported to be elevated in HIV infection and cancer [45], the cause of its elevation in either disease is unclear in vivo. The up-regulation of galectin-3-binding protein in the medium of ACH-2 cells suggested that T cells latently infected with HIV could contribute to the elevation of galectin-3-binding protein that was observed in the plasma of HIV+ patients. Interestingly, an increase of L-selectin in HIV infection has been reported to function in the homing of T lymphocytes to the peripheral lymph nodes for apoptosis [4648]. This mechanism possibly explains the massive loss of these cells without obvious intracellular HIV replication [4648]. Shedding of L-selectin is reported to be induced through the engagement of HIV to both CD4 and CXCR4 [48]. Our results indicate that HIV latency could result in the secretion of L-selectin as well. It is more likely that the dysregulation of proteins in the plasma is a consequence of an orchestrated series of host responses.

Four proteins – neogenin, adenosine deaminase CECR1, ICOS ligand and phospholipid transfer protein – may also participate in host-HIV interaction. Neogenin, which belongs to the immunoglobulin superfamily, is a multifunctional transmembrane receptor. In neogenin-/- animals, significant attenuation of induced inflammation has been observed, and, interestingly, direct injection of anti-neogenin antibody in animals also significantly reduces inflammation [49, 50]. These studies suggest that neogenin may play an important role in chronic inflammation induced by HIV infection. Elevated levels of neogenin were observed by MS analysis in our study. Hence, functional regulation of its level may have a therapeutic value in HIV+ patients. Adenosine deaminase CECR1 functions to degrade extracellular adenosine, and it has been reported to increase the proliferation rate of CD4 T cells and macrophages independent of its enzyme activity [51]. The increased proliferation of these cell populations with a portion of them harboring HIV latency may result in the expansion of the HIV reservoir. ICOS ligand and the ICOS pathway function to trigger effector T cell responses and generate follicular helper T cells [52]. HIV infection severely affects the effector T cell population [53]. Prolonged activation of effector T cells by ICOS ligand could lead to progressive exhaustion. In addition, a recent study reported that the main CD4 T cell compartment for HIV is follicular helper T cells [54]. Thus, excessive ICOS ligand may stimulate the generation of such cells whose increase in number supports future HIV infection and replication. Phospholipid transfer protein functions in lipoprotein metabolism and lipid transport in the vascular compartment. Its levels could be changed in different human diseases and conditions [55]. Our finding of elevated phospholipid transfer protein levels could be a result of overall immune activation or inflammatory reaction triggered by HIV chronic infection. Aside from the four proteins mentioned above, other proteins among those listed in Tables 2 & 3 may be of interest in regard to their potentially important roles in HIV infection. A careful selection of the proteins merits future investigation.

Conclusion

To our knowledge, this study is the first application of glycoproteomics in HIV latently infected T cell medium and HIV+ plasma. We identified several glycoproteins with differential expression in the medium of an HIV infected T cell model and HIV+ plasma. The correlation of the proteins observed in the medium of HIV+ T cells and plasma was not clear in the study. To specifically associate the changed glycoproteins with HIV infection, the analysis of clearly defined patient cohorts merits future glycoproteomic investigation.

References

  1. 1.

    UNAIDS: UNAIDS World AIDS Day Report 2012. 2012, Geneva: UNAIDS,

  2. 2.

    Burton DR, Ahmed R, Barouch DH, Butera ST, Crotty S, Godzik A, Kaufmann DE, McElrath MJ, Nussenzweig MC, Pulendran B, Scanlan CN, Schief WR, Silvestri G, Streeck H, Walker BD, Walker LM, Ward AB, Wilson IA, Wyatt R: A blueprint for HIV vaccine discovery. Cell Host Microbe. 2012, 12: 396-407. 10.1016/j.chom.2012.09.008

  3. 3.

    Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, Hakim JG, Kumwenda J, Grinsztejn B, Pilotto JH, Godbole SV, Mehendale S, Chariyalertsak S, Santos BR, Mayer KH, Hoffman IF, Eshleman SH, Piwowar-Manning E, Wang L, Makhema J, Mills LA, de Bruyn G, Sanne I, Eron J, Gallant J, Havlir D, Swindells S, Ribaudo H, Elharrar V, Burns D: Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011, 365: 493-505. 10.1056/NEJMoa1105243

  4. 4.

    Harrison KM, Song R, Zhang X: Life expectancy after HIV diagnosis based on national HIV surveillance data from 25 states, United States. J Acquir Immune Defic Syndr. 2010, 53: 124-130. 10.1097/QAI.0b013e3181b563e7

  5. 5.

    Grulich AE, Jin F, Poynten IM, Vajdic CM: HIV, cancer, and aging. Sex Health. 2011, 8: 521-525. 10.1071/SH11048

  6. 6.

    Hogan C, Wilkins E: Neurological complications in HIV. Clin Med. 2012, 11: 571-575.

  7. 7.

    Pawlowski A, Jansson M, Skold M, Rottenberg ME, Kallenius G: Tuberculosis and HIV co-infection. PLoS Pathog. 2012, 8: e1002464- 10.1371/journal.ppat.1002464

  8. 8.

    Luetkemeyer AF, Havlir DV, Currier JS: Complications of HIV disease and antiretroviral therapy. Top Antivir Med. 2012, 20: 48-60.

  9. 9.

    Varnum SM, Webb-Robertson BJ, Hessol NA, Smith RD, Zangar RC: Plasma biomarkers for detecting Hodgkin’s lymphoma in HIV patients. PLoS One. 2011, 6: e29263- 10.1371/journal.pone.0029263

  10. 10.

    Wiederin J, Rozek W, Duan F, Ciborowski P: Biomarkers of HIV-1 associated dementia: proteomic investigation of sera. Proc Natl Acad Sci USA. 2009, 7: 8-

  11. 11.

    Laspiur JP, Anderson ER, Ciborowski P, Wojna V, Rozek W, Duan F, Mayo R, Rodriguez E, Plaud-Valentin M, Rodriguez-Orengo J, Gendelman HE, Meléndez LM: CSF proteomic fingerprints for HIV-associated cognitive impairment. J Neuroimmunol. 2007, 192: 157-170. 10.1016/j.jneuroim.2007.08.004

  12. 12.

    Weiss RA: How does HIV cause AIDS?. Science. 1993, 260: 1273-1279. 10.1126/science.8493571

  13. 13.

    Eisele E, Siliciano RF: Redefining the viral reservoirs that prevent HIV-1 eradication. Immunity. 2012, 37: 377-388. 10.1016/j.immuni.2012.08.010

  14. 14.

    Craigie R, Bushman FD: HIV DNA integration. Cold Spring Harb Perspect Med. 2012, 2: a006890-

  15. 15.

    Zhou Y, Aebersold R, Zhang H: Isolation of N-linked glycopeptides from plasma. Anal Chem. 2007, 79: 5826-5837. 10.1021/ac0623181

  16. 16.

    Shetty V, Jain P, Nickens Z, Sinnathamby G, Mehta A, Philip R: Investigation of plasma biomarkers in HIV-1/HCV mono- and coinfected individuals by multiplex iTRAQ quantitative proteomics. OMICS. 2011, 15: 705-717. 10.1089/omi.2011.0004

  17. 17.

    Zhang L, Zhang X, Ma Q, Zhou H: Host proteome research in HIV infection. Genomics Proteomics Bioinformatics. 2010, 8: 1-9. 10.1016/S1672-0229(10)60001-0

  18. 18.

    Pottiez G, Jagadish T, Yu F, Letendre S, Ellis R, Duarte NA, Grant I, Gendelman HE, Fox HS, Ciborowski P: Plasma proteomic profiling in HIV-1 infected methamphetamine abusers. PLoS One. 2012, 7: e31031- 10.1371/journal.pone.0031031

  19. 19.

    Zhang L, Jia X, Zhang X, Cao J, Yang P, Qiu C, Shen Y, Ma F, Liu L, Sun J, Shen F, Yin L, Liu L, Yao Y, Lu H: Alpha-1 antitrypsin variants in plasma from HIV-infected patients revealed by proteomic and glycoproteomic analysis. Electrophoresis. 2010, 31: 3437-3445. 10.1002/elps.201000153

  20. 20.

    Li QK, Gabrielson E, Zhang H: Application of glycoproteomics for the discovery of biomarkers in lung cancer. Proteomics Clin Appl. 2012, 6: 244-256. 10.1002/prca.201100042

  21. 21.

    Pan S, Chen R, Aebersold R, Brentnall TA: Mass spectrometry based glycoproteomics--from a proteomics perspective. Mol Cell Proteomics. 2010, 10: R110 003251-

  22. 22.

    Tian Y, Zhang H: Glycoproteomics and clinical applications. Proteomics Clin Appl. 2010, 4: 124-132. 10.1002/prca.200900161

  23. 23.

    Roth J: Protein N-glycosylation along the secretory pathway: relationship to organelle topography and function, protein quality control, and cell interactions. Chem Rev. 2002, 102: 285-303. 10.1021/cr000423j

  24. 24.

    Meany DL, Chan DW: Aberrant glycosylation associated with enzymes as cancer biomarkers. Clin Proteomics. 2011, 8: 7- 10.1186/1559-0275-8-7

  25. 25.

    Zeng X, Hood BL, Sun M, Conrads TP, Day RS, Weissfeld JL, Siegfried JM, Bigbee WL: Lung cancer serum biomarker discovery using glycoprotein capture and liquid chromatography mass spectrometry. J Proteome Res. 2010, 9: 6440-6449. 10.1021/pr100696n

  26. 26.

    Rabinovich GA, van Kooyk Y, Cobb BA: Glycobiology of immune responses. Ann N Y Acad Sci. 2012, 1253: 1-15. 10.1111/j.1749-6632.2012.06492.x

  27. 27.

    Rudd PM, Elliott T, Cresswell P, Wilson IA, Dwek RA: Glycosylation and the immune system. Science. 2001, 291: 2370-2376. 10.1126/science.291.5512.2370

  28. 28.

    Ackerman ME, Crispin M, Yu X, Baruah K, Boesch AW, Harvey DJ, Dugast AS, Heizen EL, Ercan A, Choi I, Streeck H, Nigrovic PA, Bailey-Kellogg C, Scanlan C, Alter G: Natural variation in Fc glycosylation of HIV-specific antibodies impacts antiviral activity. J Clin Invest. 2013, 123: 2183-2192. 10.1172/JCI65708

  29. 29.

    Tian Y, Zhou Y, Elliott S, Aebersold R, Zhang H: Solid-phase extraction of N-linked glycopeptides. Nat Protoc. 2007, 2: 334-339. 10.1038/nprot.2007.42

  30. 30.

    Zhang H, Li XJ, Martin DB, Aebersold R: Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat Biotechnol. 2003, 21: 660-666. 10.1038/nbt827

  31. 31.

    Zhang H, Aebersold R: Isolation of glycoproteins and identification of their N-linked glycosylation sites. Methods Mol Biol. 2006, 328: 177-185.

  32. 32.

    Tian Y, Esteva FJ, Song J, Zhang H: Altered expression of sialylated glycoproteins in breast cancer using hydrazide chemistry and mass spectrometry. Mol Cell Proteomics. 2012, 11: M111-011403,

  33. 33.

    Zhang H, Yi EC, Li XJ, Mallick P, Kelly-Spratt KS, Masselon CD, Camp DG, Smith RD, Kemp CJ, Aebersold R: High throughput quantitative analysis of serum proteins using glycopeptide capture and liquid chromatography mass spectrometry. Mol Cell Proteomics. 2005, 4: 144-155.

  34. 34.

    Tian Y, Kelly-Spratt KS, Kemp CJ, Zhang H: Mapping tissue-specific expression of extracellular proteins using systematic glycoproteomic analysis of different mouse tissues. J Proteome Res. 2010, 9: 5837-5847. 10.1021/pr1006075

  35. 35.

    Tian Y, Zhang H: Characterization of disease-associated N-linked glycoproteins. Proteomics. 2013, 13: 504-511. 10.1002/pmic.201200333

  36. 36.

    Hua S, An HJ: Glycoscience aids in biomarker discovery. BMB Rep. 2012, 45: 323-330. 10.5483/BMBRep.2012.45.6.132

  37. 37.

    Li Z, Adams RM, Chourey K, Hurst GB, Hettich RL, Pan C: Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos. J Proteome Res. 2012, 11: 1582-1590. 10.1021/pr200748h

  38. 38.

    Cox J, Mann M: MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008, 26: 1367-1372. 10.1038/nbt.1511

  39. 39.

    Buttke TM, Folks TM: Complete replacement of membrane cholesterol with 4, 4′, 14-trimethyl sterols in a human T cell line defective in lanosterol demethylation. J Biol Chem. 1992, 267: 8819-8826.

  40. 40.

    Clouse KA, Powell D, Washington I, Poli G, Strebel K, Farrar W, Barstad P, Kovacs J, Fauci AS, Folks TM: Monokine regulation of human immunodeficiency virus-1 expression in a chronically infected human T cell clone. J Immunol. 1989, 142: 431-438.

  41. 41.

    Folks TM, Clouse KA, Justement J, Rabson A, Duh E, Kehrl JH, Fauci AS: Tumor necrosis factor alpha induces expression of human immunodeficiency virus in a chronically infected T-cell clone. Proc Natl Acad Sci USA. 1989, 86: 2365-2368. 10.1073/pnas.86.7.2365

  42. 42.

    Keller A, Nesvizhskii AI, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem. 2002, 74: 5383-5392. 10.1021/ac025747h

  43. 43.

    Nesvizhskii AI, Keller A, Kolker E, Aebersold R: A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003, 75: 4646-4658. 10.1021/ac0341261

  44. 44.

    Li XJ, Pedrioli PG, Eng J, Martin D, Yi EC, Lee H, Aebersold R: A tool to visualize and evaluate data obtained by liquid chromatography-electrospray ionization-mass spectrometry. Anal Chem. 2004, 76: 3856-3860. 10.1021/ac035375s

  45. 45.

    Natoli C, Dianzani F, Mazzotta F, Balocchini E, Pierotti P, Antonelli G, Iacobelli S: 90K protein: a new predictor marker of disease progression in human immunodeficiency virus infection. J Acquir Immune Defic Syndr. 1993, 6: 370-375.

  46. 46.

    Wang L, Robb CW, Cloyd MW: HIV induces homing of resting T lymphocytes to lymph nodes. Virology. 1997, 228: 141-152. 10.1006/viro.1996.8397

  47. 47.

    Chen JJ, Huang JC, Shirtliff M, Briscoe E, Ali S, Cesani F, Paar D, Cloyd MW: CD4 lymphocytes in the blood of HIV(+) individuals migrate rapidly to lymph nodes and bone marrow: support for homing theory of CD4 cell depletion. J Leukoc Biol. 2002, 72: 271-278.

  48. 48.

    Wang J, Marschner S, Finkel TH: CXCR4 engagement is required for HIV-1-induced L-selectin shedding. Blood. 2004, 103: 1218-1221.

  49. 49.

    Mirakaj V, Jennewein C, Konig K, Granja T, Rosenberger P: The guidance receptor neogenin promotes pulmonary inflammation during lung injury. FASEB J. 2011, 26: 1549-1558.

  50. 50.

    Konig K, Gatidou D, Granja T, Meier J, Rosenberger P, Mirakaj V: The axonal guidance receptor neogenin promotes acute inflammation. PLoS One. 2012, 7: e32145- 10.1371/journal.pone.0032145

  51. 51.

    Zavialov AV, Gracia E, Glaichenhaus N, Franco R, Lauvau G: Human adenosine deaminase 2 induces differentiation of monocytes into macrophages and stimulates proliferation of T helper cells and macrophages. J Leukoc Biol. 2010, 88: 279-290. 10.1189/jlb.1109764

  52. 52.

    de la Fuente H, Cibrian D, Sanchez-Madrid F: Immunoregulatory molecules are master regulators of inflammation during the immune response. FEBS Lett. 2012, 586: 2897-2905. 10.1016/j.febslet.2012.07.032

  53. 53.

    Sauce D, Elbim C, Appay V: Monitoring cellular immune markers in HIV infection: from activation to exhaustion. Curr Opin HIV AIDS. 2013, 8: 125-131. 10.1097/COH.0b013e32835d08a9

  54. 54.

    Perreau M, Savoye AL, De Crignis E, Corpataux JM, Cubas R, Haddad EK, De Leval L, Graziosi C, Pantaleo G: Follicular helper T cells serve as the major CD4 T cell compartment for HIV-1 infection, replication, and production. J Exp Med. 2012, 210: 143-156.

  55. 55.

    Albers JJ, Vuletic S, Cheung MC: Role of plasma phospholipid transfer protein in lipid and lipoprotein metabolism. Biochim Biophys Acta. 2011, 1821: 345-357.

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Acknowledgements

We thank Dr. Xiao-Fang Yu who provided the biosafety level-3 lab at the Johns Hopkins Bloomberg School of Public Health. We gratefully acknowledge the support from the Mass Spectrometry Core Facility at the Johns Hopkins University. We gratefully acknowledge Dr. Stefani N. Thomas and Robert Harlan for proofreading. This research was supported by a National Institutes of Health - National Heart Lung and Blood Institute grant (Program of Excellence in Glycosciences, PEG, P01HL107153) to Dr. Hui Zhang and a National Institutes of Health - National Institute of Allergy and Infectious Diseases grant for the JHU-Guangxi, China Clinical Trials Unit (1U01 AI69482-01) to Dr. Jay Brooks Jackson.

Author information

Correspondence to Hui Zhang.

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Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

WY and SS carried out the cell culture and sample preparation. JZ carried out the mass spectrometry. WY, LC, MA and PA analyzed the MS data. AS prepared the HIV clinical sample and information. WY, HZ and JBJ designed the experiment and drafted the manuscript. All authors read and approved the final manuscript.

Electronic supplementary material

Additional file 1: Figure S1: Confirmation of HIV latent infection in ACH-2 cells stimulated by PMA for 48 hours. (DOC 208 KB)

Additional file 2: Figure S2: Pep3D image of LC-MS replicate analyses of ACH-2 and A3.01 media. (DOC 786 KB)

Additional file 3: Figure S3: Pep3D image of LC-MS replicate analyses of HIV+ and HIV- pooled samples. (DOC 1 MB)

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Keywords

  • Glycoproteomics
  • SPEG
  • HIV
  • T cells
  • Medium
  • Plasma