Abstract
Objective
Glycosylation plays a critical role in mediating several antibody (mainly immunoglobulin G; IgG) immunological functions, including antibody-dependent cell-mediated cytotoxicity (ADCC), and anti-inflammatory activities. We investigated whether IgG glycosylation and immune profile patterns are differentially modulated in mono and dual infection using samples from untreated hepatitis C virus (HCV) infected individuals with and without co-infection with antiretroviral therapy (ART)-suppressed human immunodeficiency virus (HIV).
Design
IgG glycosylation, immune subsets, natural killer cell function, and liver enzymes were assessed in 14 HCV mono-infected and 27 ART-suppressed HIV/HCV co-infected participants naïve to HCV treatment. Historic IgG glycosylation data from 23 ART-suppressed chronically HIV-infected individuals were also used for comparisons.
Methods
Plasma IgG glycosylation was assessed using capillary electrophoresis. Whole blood was used for immune subset characterization by flow cytometry. Peripheral blood mononuclear cells were used to measure constitutive and interferon-α-induced K562 target cell lysis. Statistical analysis was performed using R (3.5.0).
Results
HIV/HCV had lower levels of pro-ADCC-associated non-fucosylated glycans when compared to HIV [e.g. di-sialylated A2 percent (%): p=0.04], and higher levels of T and myeloid cells activation/exhaustion when compared to HCV (e.g. CD3+CD8+CD38+ %: p<0.001). Finally, in HCV high levels of the anti-inflammatory galactosylated and sialylated glycans were associated with low plasma levels of aspartate aminotransferase (AST), low CD8+ T cell activation, and high CD8+ T cell exhaustion.
Conclusion
HCV modulates IgG glycosylation profile in HIV co-infected individuals on suppressive ART. These results could inform on the modulation of IgG glycans in other mono and dual infections.
Keywords: HCV, HIV/HCV, NK, ADCC, IgG glycosylation
Introduction
Glycans on circulating antibodies (Abs) participate in cell-cell [1] and cell-pathogen interactions [2], direct Ab functionality and immune functions regulation [3]. Higher levels of Ab galactosylation and lower levels of fucosylation have been associated with higher Ab-dependent cell-mediated cytotoxicity (ADCC) [4–6], while Ab sialylation and galactosylation have been linked to strong anti-inflammatory responses [7–10].
Human immunodeficiency virus (HIV), and hepatitis C virus (HCV) mono-infection lead to immune activation and gradual loss of immune function [11–13]. Antiretroviral therapy (ART) partially restores these effects in HIV [14, 15]. HIV/HCV co-infection is common, with HIV and HCV affecting each other [16–18].
We evaluated how IgG glycosylation and immune profile patterns are modulated in single or dual infections using samples from HCV and ART-suppressed HIV, and HIV/HCV individuals.
Methods
Study Participants
Fourteen HCV and 27 ART-suppressed HIV/HCV untreated for HCV were evaluated for IgG glycans, and clinical/immune variables. Inclusion criteria are shown in Supplemental Table 1. IgG glycans data were also available from 23 ART-suppressed chronically HIV (historic HIV: CD4+ T cell count >400 cells/mm3, HIV RNA <500 copies/ml for ≥6 months, <50 copies/ml at study entry). Informed consent was obtained from all participants. The study protocol and informed consent procedures were approved by the Institutional Review Boards of the authors’ institutions.
IgG isolation and N-glycan analysis
Bulk IgG was purified from cryopreserved plasma using Pierce™ Protein G Spin Plates (ThermoFisher Scientific). N-glycans were released using peptide-N-glycosidase F (PNGase F) and labeled with 8-aminopyrene-1,3,6-trisulfonic acid (APTS) using the GlycanAssure APTS Kit (ThermoFisher Scientific). Labelled N-glycans were analyzed using a 3500 Genetic Analyzer capillary electrophoresis system. Relative abundance of N-glycan structures was quantified by calculating the area under the curve (AUC) of each glycan structure divided by total glycans using the Applied Biosystems GlycanAssure Data Analysis Software Version 2.0 (Supplemental Figure 1).
Clinical and immune variables assessment
Clinical parameters [complete blood count with differential, CD4 count, HCV and HIV viral load (VL), alanine aminotransferase (ALT), and aspartate aminotransferase (AST)] were assessed by Quest Diagnostics (NJ, USA).
Flow cytometry was performed on fresh blood as previously described [19, 20]. Combinations of fluorochrome-conjugated monoclonal Abs targeted activation/exhaustion, costimulatory and apoptosis markers as shown in Supplemental Table 2. All Abs were from Becton Dickinson Biosciences (San Diego, CA, USA) except blood dendritic cells antigen (BDCA) 2-allophycocyanin (APC), BDCA4-APC and IgG1-APC (Miltenyi Biotec, San Diego, CA, USA). T cells were defined as CD3+CD8−, CD3+CD8+, dendritic cells (DC) [21] as BDCA2+BDCA4+ (plasmacytoid DC, PDC), and CD19−BDCA1+CD11c+ (myeloid DC, MDC)], monocytes as CD14+, and NK cells as CD3−CD56dimCD16−, CD3−CD56dimCD16+, CD3−CD56bright, and CD3−CD56−CD16+ [22–25].
NK function assessment
Constitutive and in vitro IFN-α-induced NK cell-mediated cytotoxicity were measured using a standard 51Cr release assay with fresh PBMC serving as effectors against the 51Cr-labelled erythroblastoid MHC-null cell line K562 [20, 26]. Effectors and K562 targets were cultured in triplicate for each Effector/Target ratio (E/T: 50:1, 25:1, 12.5:1, and 6.25:1). Results were expressed for each condition as AUC.
NK function in individual NK subsets (i.e. Lin3−CD56−CD16+, Lin3−CD56dimCD16−, Lin3−CD56dimCD16+, and Lin3−CD56bright with Lin3 consisting of CD3, CD14, CD19, and CD20) was assessed using flow cytometry as previously described [20] by measuring constitutive and target-induced cytokine production (IFN-γ) in the presence or absence of IFN-α stimulation and K562 cells.
Statistical Analysis
Data were described as medians, and interquartile ranges (IQR). Kruskal-Wallis test with post-hoc two by two comparisons with Wilcoxon rank sum test was used for group comparisons and Spearman’s correlation test for associations within each group. Unadjusted p-values that were less than 0.05 along with multiple testing adjusted p-values were assessed. Multiple testing adjustment was applied using the Benjamini and Hochberg False discovery rate (FDR) method, with a cutoff of 10%. FDR adjusted p-values that were less than 0.1 are reported. Fisher’s exact test was used to test the null hypothesis that there is no significant difference in race and sex amongst groups. To evaluate if age, race or sex would affect the observed significant differences amongst groups, linear regression model was applied using glycans and clinical/immune variables as dependent variables, and including study group, age, race or sex as independent variables. To evaluate if age, race or sex would affect the observed significant associations within each group, linear regression model was applied using glycans as dependent variables, and including clinical/immune variables, age, race or sex as independent variables, and the interaction term between clinical/immune variables and race or sex. The R version 3.5.0 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria) was used.
Results
Demographics
Study participants demographic and clinical characteristics are described in Supplemental Table 3. A significant difference was found amongst groups for sex and race. CD4+ T cell count and age were higher in HCV when compared to HIV/HCV and HIV, while no difference was found for these variables between HIV/HCV and HIV.
HCV decreases the levels of pro-ADCC-associated glycans
Comparison of 27 IgG glycans and 144 clinical/immune variables amongst groups showed difference after multiple testing adjustment in the expression levels of 9/27 IgG glycans (Fig. 1) and 8/144 immune variables (Supplemental Figure 2). Assessment of the effect of age, race or sex on these differences showed an effect in 4/9 IgG glycans (Fig. 1) and 1/8 immune variables (Supplemental Figure 2).
Fig. 1. IgG glycans distribution.
(a) Non-fucosylated mono-sialylated A1 glycans (%) in total IgG glycome, non-fucosylated di-sialylated A2 glycans (%) in total IgG glycome, mono-galactosylated G1 glycans (%) in total IgG glycome; (b) total fucosylated glycans (%) in total IgG glycome, fucosylated G1Fp glycans (%) in total IgG glycome; (c) agalactosylated G0 glycans (%) in total IgG glycome; (d) total galactosylated glycans (%) in total IgG glycome, total di-galactosylated G2 glycans (%) in total IgG glycome, total sialylated glycans (%) in total IgG glycome. Data in panels are shown for HIV, HCV mono-infected and HIV/HCV co-infected individuals with median and interquartile range, and significant values after multiple testing adjustment. Yellow shaded square indicates variable difference amongst groups (i.e. non-fucosylated di-sialylated A2 glycans) that was found to be higher in black (comparison white/other versus black: p=0.007, estimate of mean difference=−0.15). Blue shaded squares indicate variables differences amongst groups that were affected by sex. More precisely, non-fucosylated mono-sialylated A1 and mono-galactosylated G1 glycans were higher in females (comparison females versus males: p=0.03, estimate of mean difference=−0.29 and p=0.003, estimate of mean difference=−0.63 respectively), while total fucosylated glycans were lower in females (comparison females versus males: p=0.005, estimate of mean difference=1.53). Kruskal-Wallis test with post-hoc two by two comparisons with Wilcoxon rank sum test was used for group comparisons. Reported p values were assessed as follows: First, unadjusted p-values that were less than 0.05 were assessed. Subsequently, false discovery rate (FDR, Benjamini and Hochberg) adjustment was applied to adjust multiple tests and adjusted p values less than 0.1 were considered as significant. To evaluate if age, race or sex would affect the observed significant differences amongst groups, linear regression model was applied using glycans and clinical/immune variables as dependent variables, and including study group, age, race or sex as independent variables.
Briefly, comparison of IgG glycans amongst HCV, HIV/HCV and historic HIV showed lower levels of the pro-ADCC-associated non-fucosylated glycans (non-fucosylated mono-sialylated A1, or di-sialylated A2) or mono-galactosylated G1 along with higher levels of the anti-ADCC-associated fucosyated glycans (e.g. total fucosylated, fucosylated G1Fp) in HCV when compared to HIV/HCV or HIV. Interestingly, same differences were observed for these glycans levels between HIV/HCV and HIV supporting retained modulation of glycans levels by HCV in the context of HIV co-infection (Fig. 1a–b).
A difference was also detected between HCV and HIV for inflammation-associated glycans as shown by the lower levels of anti-inflammatory glycans (e.g. total galactosylated, total di-galactosylated G2, total sialylated) and the higher levels of the pro-inflammatory agalactosylated G0 glycans in HCV when compared to HIV (Fig. 1c–d). No difference was detected for these glycans levels between HCV and HIV/HCV.
As documented in Supplemental Figure 2, markers of immune activation [e.g. CD3+CD8+CD38+ percent (%) of CD8+ T cells], exhaustion [e.g. CD3+CD8+ B and T lymphocyte attenuator (BTLA)+CD160+ % of CD8+ T cells], and apoptosis [e.g. tumor necrosis factor-alpha-related apoptosis-inducing ligand (TRAIL)+ % of monocytes] were higher in HIV/HCV when compared to HCV. In contrast, no significant difference was observed for plasma levels of the biomarkers of liver status ALT and AST.
Association of glycans with liver status and immune variables
Correlation of IgG glycans with clinical/immune variables within each group, resulted after multiple testing adjustment in 21 associations in HCV and 2 associations in HIV/HCV (Table 1, Supplemental Figure 3). Assessment of the effect of age, race or sex on the observed associations showed an effect of age in 4/21 associations observed in HCV (Table 1).
Table 1.
Associations of IgG glycans with markers of liver status and immune function.
| Group | Glycans | Liver status or immune markers | N | Rho | p (FDR by Benjamini and Hochberg) |
|---|---|---|---|---|---|
| HCV | Total galactosylated | AST | 13 | −0.5824 | 0.08521 |
| Total di-galactosylated G2 | AST | 13 | −0.5879 | 0.08521 | |
| Total sialylated | AST | 13 | −0.5385 | 0.09164 | |
| agalactosylated G0 | AST | 13 | 0.5824 | 0.08521 | |
| Total fucosylated | IFN-α-induced NK cytotoxicity | 12 | −0.7145 | 0.08122 | |
| Total galactosylated | constitutive NK cytotoxicity | 12 | 0.6643 | 0.06648 | |
| Total di-galactosylated G2 | constitutive NK cytotoxicity | 12 | 0.6783 | 0.06648 | |
| Total galactosylated | CD3+CD4−CD62L−CD45RA+CD28−% CD3+CD4−T cells | 12 | −0.6993 | 0.03271 | |
| Total di-galactosylated G2 | CD3+CD4−CD62L−CD45RA+CD28− % CD3+CD4− T cells | 12 | −0.5874 | 0.0628 | |
| Total sialylated | CD3+CD4−CD62L−CD45RA+CD28− % CD3+CD4− T cells | 12 | −0.6643 | 0.03597 | |
| agalactosylated G0 | CD3+CD4−CD62L−CD45RA+CD28− % CD3+CD4− T cells | 12 | 0.6993 | 0.03271 | |
| Total galactosylated | CD3+CD8+HLA-DR+ % CD3+CD8+ T cells | 12 | −0.6713 | 0.07773 | |
| Total di-galactosylated G2 | CD3+CD8+HLA-DR+ % CD3+CD8+ T cells | 12 | −0.6014 | 0.08855 | |
| Total sialylated | CD3+CD8+HLA-DR+ % CD3+CD8+ T cells | 12 | −0.5664 | 0.08855 | |
| agalactosylated G0 | CD3+CD8+HLA-DR+ % CD3+CD8+ T cells | 12 | 0.6713 | 0.07773 | |
| Total di-galactosylated G2 | MFI of BTLA on CD3+CD8+ T cells | 12 | 0.6364 | 0.06776 | |
| Total sialylated | MFI of BTLA on CD3+CD8+ T cells | 12 | 0.7203 | 0.04959 | |
| Total galactosylated | MFI of CTLA4 on CD3+CD8+ T cells | 12 | 0.5874 | 0.07327 | |
| Total di-galactosylated G2 | MFI of CTLA4 on CD3+CD8+ T cells | 12 | 0.6294 | 0.07289 | |
| Total sialylated | MFI of CTLA4 on CD3+CD8+ T cells | 12 | 0.6573 | 0.07194 | |
| agalactosylated G0 | MFI of CTLA4 on CD3+CD8+ T cells | 12 | −0.5874 | 0.07327 | |
| HIV/HCV | |||||
| Total fucosylated | CD56dimCD16− % lymphocytes | 27 | −0.5779 | 0.01434 | |
| Total di-galactosylated G2 | CD56dimCD16− % lymphocytes | 27 | 0.3877 | 0.0567 |
FDR, false discovery rate; HCV, hepatitis C virus; AST, aspartate aminotransferase; NK, natural killer cell; MFI, mean fluorescent intensity; BTLA, B and T lymphocyte attenuator; CTLA4, cytotoxic T lymphocyte-associated protein 4; HIV, human immunodeficiency virus.
Spearman’s correlation test for associations between study variables within each group. Reported p values were assessed as follows: First, unadjusted p-values that were less than 0.05 were assessed. Subsequently, false discovery rate (FDR, Benjamini and Hochberg) adjustment was applied to adjust multiple tests and adjusted p values less than 0.1 were considered as significant. To evaluate if age, race or sex would affect the observed significant associations within each group, linear regression model was applied using glycans as dependent variables, and including clinical/immune variables, age, race or sex as independent variables, and the interaction term between clinical/immune variables and race or sex. The following associations were found to be affected by age: association of total di-galactosylated G2 with MFI of BTLA on CD3+CD8+ T cells (p=0.005, estimate of age=−0.6409); association of total sialylated glycans with MFI of BTLA on CD3+CD8+ T cells (p=0.0117, estimate of age=−0.3162); association of total galactosylated with MFI of CTLA4 on CD3+CD8+ T cells (p=0.03915, estimate of age=−0.8161); association of agalactosylated G0 glycans with MFI of CTLA4 on CD3+CD8+ T cells (p=0.03915, estimate of age=0.8161).
Briefly, in HCV consistent with an expected association of liver status or immune activation with inflammation, liver status (i.e. AST) was negatively associated with the anti-inflammatory total galactosylated glycans, total di-galactosylated G2 glycans, and total sialylated glycans, and positively associated with the pro-inflammatory agalactosylated G0 glycans. In addition, the frequencies of CD3+CD8+HLA-DR+ and of effector terminal CD8+ T cells were negatively associated with anti-inflammatory (i.e. total galactosylated, total di-galactosylated G2, total sialylated), and positively associated with pro-inflammatory (i.e. agalactosylated G0) glycans. Finally, consistent with the previously described lower activation potential of CD8+ T cells expressing BTLA and cytotoxic T-lymphocyte-associated protein 4 (CTLA4), these markers were positively associated with anti-inflammatory (i.e. total galactosylated, total di-galactosylated G2, and total sialylated), and negatively associated with pro-inflammatory (i.e. agalactosylated G0) glycans.
Consistent with an expected association of direct NK cytotoxicity with ADCC or inflammation, in HCV IFN-α-induced NK cytotoxicity was negatively associated with the anti-ADDC total fucosylated glycans, while constitutive NK cytotoxicity was positively associated with the anti-inflammatory total galactosylated and total di-galactosylated G2 glycans. In agreement with this finding, in HIV/HCV CD56dimCD16− NK cells were positively associated with the anti-inflammatory total di-galactosylated G2 glycans, and negatively with the anti-ADDC total fucosylated glycans.
Discussion
We evaluated the modulation of IgG glycosylation in single and dual infections using samples from HIV and HCV, and HIV/HCV. We show that HCV modulates IgG glycosylation in HIV/HCV by decreasing the levels of glycans that are associated with higher NK-mediated ADCC compared to HIV. We also show that in HCV, inflammation-modulating IgG glycans are associated with biomarkers of liver status, and immune activation/exhaustion.
We confirmed data from us [20] and others [27, 28] suggesting a greater T cell dysfunction (e.g activation/exhaustion) in co-infection with HIV despite ART-mediated suppression. Importantly, in HCV the correlations found between AST and IgG glycans, suggest that IgG glycans can signal for inflammation and liver damage. The lack of significant difference in the levels of biomarkers of liver status between HCV and HIV/HCV despite difference in activation, could be attributed to independent factors driving liver damage or immune activation in HIV/HCV or to the exclusion of individuals with established non-compensated liver cirrhosis.
Lower fucosylation and higher antibody galactosylation have been associated with higher ADCC [4–6]. Our results support these data as in HCV and HIV/HCV NK cytotoxicity or NK cell subsets frequency respectively were positively associated with the pro-ADCC-associated di-galactosylated glycans, and negatively associated with the anti-ADCC-associated fucosylated glycans suggesting an association between these glycans and NK function. Comparison of IgG glycosylation between these groups showed higher levels of fucosylated glycans in HCV and higher levels of non-fucosylated glycans in HIV/HCV. Furthermore, higher levels of non-fucosylated and lower levels of fucosylated glycans were observed in HIV when compared to HCV or HIV/HCV. These findings suggest that suppressive ART could result in higher ADCC in HIV. This in turn could account for higher ADCC in HIV/HCV when compared to HCV, although co-infection with HCV could restrict this beneficial effect.
Usage of fresh samples eliminated any influence of cryopreservation on the cell subsets and functions studied. While our study suggests modulation of ADCC-associated glycans, the lack of a specific HCV antigen that could be used to test ADCC necessitated the usage of direct NK cytotoxicity as a surrogate marker of NK function. Due to the small sample size, our study did not address the possible confounding effects of different antiretroviral drug combinations on IgG glycomic signatures.
Overall, our data suggest that a) ART-suppressed HIV/HCV co-infection may restrict the beneficial effect of ART-mediated immune reconstitution by decreasing the levels of glycans that are associated with higher NK-mediated ADCC, and increasing the levels of glycans that are associated with higher inflammation and immune activation; b) in contrast to HIV/HCV, IgG glycan profiles may be more informative in HCV where we found an association between levels of ADCC or inflammation-associated IgG glycans and markers of liver status, NK cytotoxicity, or immune activation/exhaustion. Future studies should validate our findings in other mono and dual infections.
Supplementary Material
Supplemental Table 1. Inclusion criteria.
Supplemental Figure 1. Analysis of IgG N-glycans. IgG N-glycans were separated into 19 peaks. Relative abundance of N-glycan structures was quantified by calculating the area under the curve of each glycan structure divided by the total glycans using the Applied Biosystems GlycanAssure Data Analysis Software Version 2.0.
Supplemental Figure 2. Immune markers distribution. (a) CD3+CD8+CD38+ % of CD8+ T cells, MFI of CD38 on CD8+ T cells, CD3+CD4+CD38+ % of CD4+ T cells, MFI of CD38 on CD4+ T cells, CD3+CD8+HLA-DR+ % of CD8+ T cells, CD3+CD8+BTLA+CD160+ % of CD8+ T cells; (b) CD14+TRAIL+ % of CD14+ cells, MFI of TRAIL on CD14+ cells. Data in panels (a)-(b) are shown for HCV mono-infected and HIV/HCV co-infected with median and interquartile range, and significant values after multiple testing adjustment. Orange shaded square indicates variable difference amongst groups (i.e. CD3+CD8+HLA-DR+ % of CD8+ T cells) that was found to be increased with age (p=0.008, estimate of mean difference=0.079). Kruskal-Wallis test with post-hoc two by two comparisons with Wilcoxon rank sum test was used for group comparisons. Reported p values were assessed as follows: First, unadjusted p-values that were less than 0.05 were assessed. Subsequently, false discovery rate (FDR, Benjamini and Hochberg) adjustment was applied to adjust multiple tests and adjusted p values less than 0.1 were considered as significant. To evaluate if age, race or sex would affect the observed significant differences amongst groups, linear regression model was applied using glycans and clinical/immune variables as dependent variables, and including study group, age, race or sex as independent variables.
Supplemental Figure 3. Associations of IgG glycans with markers of liver status and immune function in HCV and HIV/HCV. Circus plot showing associations of IgG glycosylation with markers of liver status and markers of immunological and inflammatory functions in HCV mono-infected and HIV/HCV co-infected participants. IgG glycosyation in the HCV mono-infected and the HIV/HCV co-infected group is shown with orange and pink boxes respectively. Markers of liver status and of immune functions are shown in the HCV mono-infected group with green and in the HIV/HCV co-infected group with brown boxes. Correlations with False discovery rate (FDR)>0.1 and rho ≥0.4 (for positive correlations) or ≤−0.4 (for negative correlations) are shown with red or blue arrows respectively. Spearman’s correlation test was used for associations between study variables. Reported p values were assessed as follows: First, unadjusted p-values that were less than 0.05 were assessed. Subsequently, false discovery rate (FDR, Benjamini and Hochberg) adjustment was applied to adjust multiple tests and adjusted p values less than 0.1 were considered as significant.
Supplemental Table 2. Flow cytometry staining panel.
Supplemental Table 3. Demographic and clinical characteristics per study group.
Acknowledgements
We thank the study participants and their providers.
Leila B. Giron, Mohammad Damra, Brian N. Ross, Matthew Fair, and Amanda C. Sciorillo performed experimental work. Karam Mounzer, Jay R. Kostman, Jeffrey M. Jacobson, and Kenneth M. Lynn selected and recruited patients. Xiangfan Yin and Qin Liu performed the statistical analysis. Emmanouil Papasavvas, Livio Azzoni, Mohamed Abdel-Mohsen, and Luis J. Montaner designed the study, evaluated the results, and wrote the manuscript. We also acknowledge technical support for this work by Griffin Reynolds, Natalie Opsitnick, Charity Calloway, Jocelin Joseph, and Maxwell Pistilli. All participants have read and approved the manuscript.
CONFLICTS OF INTEREST AND SOURCE OF FUNDING
This work was supported by a grant to Luis J. Montaner by the National Institutes of Health (NIH) (R01AI073219), the Philadelphia Foundation (Robert I. Jacobs Fund), Ken Nimblett and The Summerhill Trust. Glycomic analysis were supported by funds to Mohamed Abdel-Mohsen: NIH grants (R01 DK123733, R01 AG062383, R21 AI143385, R21 AI129636, and R21 NS106970) and The Foundation for AIDS Research (amfAR) impact grant # 109840-65-RGR. This publication was also made possible through core services and support from the Penn Center for AIDS Research (Grant P30 AI 045008), and the BEAT-HIV Delaney Collaboratory, supported by NIH UM1AI126620, co-funded by NIAID, NIMH, NINDS, and NIDA. The funding sources had no involvement in the study design; collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. The authors declare that no conflict of interest exists.
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Associated Data
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Supplementary Materials
Supplemental Table 1. Inclusion criteria.
Supplemental Figure 1. Analysis of IgG N-glycans. IgG N-glycans were separated into 19 peaks. Relative abundance of N-glycan structures was quantified by calculating the area under the curve of each glycan structure divided by the total glycans using the Applied Biosystems GlycanAssure Data Analysis Software Version 2.0.
Supplemental Figure 2. Immune markers distribution. (a) CD3+CD8+CD38+ % of CD8+ T cells, MFI of CD38 on CD8+ T cells, CD3+CD4+CD38+ % of CD4+ T cells, MFI of CD38 on CD4+ T cells, CD3+CD8+HLA-DR+ % of CD8+ T cells, CD3+CD8+BTLA+CD160+ % of CD8+ T cells; (b) CD14+TRAIL+ % of CD14+ cells, MFI of TRAIL on CD14+ cells. Data in panels (a)-(b) are shown for HCV mono-infected and HIV/HCV co-infected with median and interquartile range, and significant values after multiple testing adjustment. Orange shaded square indicates variable difference amongst groups (i.e. CD3+CD8+HLA-DR+ % of CD8+ T cells) that was found to be increased with age (p=0.008, estimate of mean difference=0.079). Kruskal-Wallis test with post-hoc two by two comparisons with Wilcoxon rank sum test was used for group comparisons. Reported p values were assessed as follows: First, unadjusted p-values that were less than 0.05 were assessed. Subsequently, false discovery rate (FDR, Benjamini and Hochberg) adjustment was applied to adjust multiple tests and adjusted p values less than 0.1 were considered as significant. To evaluate if age, race or sex would affect the observed significant differences amongst groups, linear regression model was applied using glycans and clinical/immune variables as dependent variables, and including study group, age, race or sex as independent variables.
Supplemental Figure 3. Associations of IgG glycans with markers of liver status and immune function in HCV and HIV/HCV. Circus plot showing associations of IgG glycosylation with markers of liver status and markers of immunological and inflammatory functions in HCV mono-infected and HIV/HCV co-infected participants. IgG glycosyation in the HCV mono-infected and the HIV/HCV co-infected group is shown with orange and pink boxes respectively. Markers of liver status and of immune functions are shown in the HCV mono-infected group with green and in the HIV/HCV co-infected group with brown boxes. Correlations with False discovery rate (FDR)>0.1 and rho ≥0.4 (for positive correlations) or ≤−0.4 (for negative correlations) are shown with red or blue arrows respectively. Spearman’s correlation test was used for associations between study variables. Reported p values were assessed as follows: First, unadjusted p-values that were less than 0.05 were assessed. Subsequently, false discovery rate (FDR, Benjamini and Hochberg) adjustment was applied to adjust multiple tests and adjusted p values less than 0.1 were considered as significant.
Supplemental Table 2. Flow cytometry staining panel.
Supplemental Table 3. Demographic and clinical characteristics per study group.