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. Author manuscript; available in PMC: 2023 Apr 1.
Published in final edited form as: AIDS. 2022 Apr 1;36(5):621–630. doi: 10.1097/QAD.0000000000003146

Effect Of HIV Suppression on The Cytokine Network in Blood and Seminal Plasma: A Longitudinal Study

Stephen A Rawlings 1, Felix Torres 1, Alan Wells 1, Andrea Lisco 2, Wendy Fitzgerald 3, Leonid Margolis 3, Sara Gianella 1, Christophe Vanpouille 3,*
PMCID: PMC8957508  NIHMSID: NIHMS1761475  PMID: 34873090

Abstract

Objective:

HIV infection disrupts the cytokine network and this disruption is not completely reversed by antiretroviral therapy (ART). Characterization of cytokine changes in blood and genital secretions is important for understanding HIV pathogenesis and the mechanisms of HIV sexual transmission. Here, we characterized the cytokine network in individuals longitudinally sampled before they began ART and after achieving suppression of HIV RNA.

Methods:

We measured concentrations of 34 cytokine/chemokines using multiplex bead-based assay in blood and seminal plasma of 19 men with HIV-1 prior to and after viral suppression. We used Partial Least Squares Discriminant Analysis (PLS-DA) to visualize the difference in cytokine pattern between the time points. Any cytokines with VIP scores exceeding 1 were deemed important in predicting suppression status and were subsequently tested using Wilcoxon Signed Rank Tests.

Results:

PLS-DA projections in blood were fairly similar before and after viral suppression. In contrast, the difference in PLS-DA projection observed in semen emphasizes that the immunological landscape and immunological needs are very different before and after ART in the male genital compartment. When tested individually, four cytokines were significantly different across time points in semen (MIG, IL-15, IL-7, I-TAC), and two in blood (MIG and IP-10).

Conclusions:

Viral suppression with ART impacts the inflammatory milieu in seminal plasma. In contrast, the overall effect on the network of cytokines in blood was modest, but consistent with prior analyses. These results identify specific changes in the cytokine networks in semen and blood as the immune system acclimates to chronic, suppressed HIV infection.

Keywords: cytokine, semen, blood, HIV suppression

INTRODUCTION

With the advent of antiretroviral therapy (ART), infection by HIV has become a chronic disease, and mortality from AIDS has been greatly reduced [1]. However, the initial immunological events after HIV acquisition include a profound disruption of the cytokine/chemokine network in blood and semen [2]. Many of these changes persist despite ART [3–5] and either directly contribute to—or are markers of—persistent inflammation during HIV infection, which is associated with accelerated disease progression, end-organ diseases, and increased mortality [6–8].

In the male genital tract, the cytokine/chemokine network reflects the functional status of resident immune cells, and it is in this milieu that HIV genital shedding occurs, setting the stage for possible sexual transmission [9–13]. Indeed, in individuals with HIV, several cytokines/chemokines are known to be upregulated in genital secretions [10, 11, 14–16], leading to increased local HIV replication and dissemination with potential implications for sexual transmission of HIV [17].

In addition to modulating concentrations of individual cytokines/chemokines, HIV infection reorganizes the cytokine/chemokine networks, thereby rebalancing and often imposing a rigidity in what was otherwise a carefully balanced and inter-dependent homeostasis. As a result of this perturbed homeostasis—which is seen in both blood and semen—the immune system is thought to be less able to respond to microbial challenges, and transmission of different sexually transmitted infections (STIs), including HIV itself, is likely affected [2, 13].

Changes in levels of individual cytokines/chemokines during HIV infection have been previously described [10, 11, 18, 19]. Here, we assess how ART-mediated viral suppression affects the cytokine network, both in blood and semen, in a longitudinal cohort of acutely infected study participants with early HIV infections. To develop such analysis, we measured the concentrations of 34 cytokine/chemokines and characterized their networks in longitudinal blood and seminal plasma samples from 19 men before and after viral suppression. Because of complicated interdependences between various signaling molecules in these networks, we used the Partial Least Squares Discriminant Analysis (PLS-DA) statistical method to expand the analysis beyond comparative measurement of individual cytokines in order to visualize the difference in cytokine pattern between the two time points and to rank the relative importance of cytokines associated with HIV-suppression status.

METHODS

Study Participants

Nineteen (n = 19) individuals recruited in the Primary Infection Research Consortium (PIRC) at UC San Diego (UCSD) [20] were identified on the basis of the availability of paired blood and GS samples before and after HIV suppression. The suppression date was defined as the first date of two consecutive undetectable viral loads. Semen was collected by masturbation and processed as previously described [21]. CD4+ T-lymphocyte subsets and HIV RNA in blood were respectively quantified with flow-cytometry (LabCorp) and with the Amplicor HIV Monitor Test (Roche Molecular Systems Inc.). HIV RNA was quantified in semen as previously described [22]. The studies were conducted with written subject consent and were approved by the Human Research Protections Program at UCSD.

Multiplex Bead Array Assay for Cytokines/Chemokines Quantification

There were 34 cytokines/chemokines involved in different immunological functions that were measured in blood and semen from the above 19 participants at two time points [10]. The National Institutes of Health laboratory that performed Luminex measurements is part of the Microbicide Quality Assurance Program [23]. See Supplemental Methods for additional details of cytokine selection and measurement.

Statistical Analysis

Cytokine levels were reported as continuous variables by median and interquartile ranges. Any undetectable cytokine value was replaced by half its lower limit of detection. Any cytokine that was undetectable at both time points in greater than 70% of participants was excluded from the blood or GS analysis, respectively. This resulted in 29 cytokines for analysis in semen, and 28 in blood (see Supplemental Table 1). In preparation for analysis, values of the remaining cytokines were log-transformed and normalized. To investigate the difference in cytokine profiles before and after virologic suppression, we used partial least squares discriminant analysis (PLS-DA). This analysis allowed us to visualize separation between pre- and post-suppression cytokine profiles and generated VIP scores (Variable Importance in Projection) for each cytokine. For cytokines with VIP>1, we tested the difference between pre- and post-suppression levels with the Wilcoxon signed-rank test. Additionally, we tested the difference in the distribution of pre- and post-suppression cytokines using the E-statistic for a two-sample difference in the multivariate normal distribution [24]. Bootstrap replicates numbering 500,000 were used to generate p values for the E-statistic. Analyses were performed using R 3.5, the E-test was performed using the “energy” package, and the PLS-DA was performed using the “mixOmics” package.

RESULTS

Study participants’ demographics and clinical data

Paired semen and blood samples from 19 men who have sex with men (MSM) were analyzed for this longitudinal study on the effect of ART on HIV-1 viral load and cytokine/chemokine expression (Table 1). The participants were white (73.7%), Hispanic (10.5%) and Other/Multiracial (15.8%). Their median age was 33 years (IQR, 27–41), with a median CD4+ T-cell count at the study entry of 710/μL (range, 204–997). All participants had an Estimated Date of Infection (EDI) of less than 70 days (early HIV infection) with 7 (36.8%) of them testing negative for HIV antibodies at the first time-point collection (acute infection). The time from EDI to ART initiation was 12.9 weeks (IQR, 2.7–401.4), while the time from ART initiation to HIV suppression was 5.4 weeks (IQR, 1.1–22.0). The average time between analyzed timepoints was 29.1 weeks (range, 3.1–515.1). The median pre-ART HIV viral load in blood was log10 4.9 (IQR, 3.4–6.6), and the Nadir CD4+ T -cell count was 547 (IQR, 143–881). The median HIV viral load in seminal plasma at the pre-ART timepoint was 1,361 copies/mL (range, 0 – 172,770), and the median HIV viral load in seminal plasma at the post-suppression timepoint was 0 copies/mL (range, 0–1,592).

Table 1.

Characteristics of participants analyzed in the cohort.

Gender/Orientation; n (%) MSM 19 (100.0)
Age; median in years (IQR) 33 (27–41)
Race/Ethnicity; n (%) White (non-hispanic) 14 (73.7)
Hispanic/Latino 2 (10.5)
Other/Multiracial 3 (15.8)
HIV Diagnosis Timeline; n (%) Acute (Ab neg) 7 (36.8)
Early (< 70 days) 12 (63.2)
HIV Treatment Timeline; median (range) EDI to ART Initiation (wk) 12.9 (2.7–401.4)
ART Initiation to Suppression (wk) 5.4 (1.1–22.0)
Study Timeline; median (range) Weeks between time points 29.1 (3.1–515.1)
Blood Plasma Viral RNA (log10 copies/mL); median (range) Peak Viral RNA 5.67 (3.77–7.44)
Viral RNA at Pre-ART tp 4.90 (3.42–6.58)
Seminal Plasma Viral RNA (copies/mL); median (range) Pre-ART tp 1361 (0–172,770)
Post-suppression tp 0 (0–1,592)
Lymphocytes (cells/μL); median (range) Nadir CD4 count 547 (143–881)
CD4 at Pre-ART tp 710 (204–997)
CD4 at Post-Supp tp 787 (397–1,075)
Days to suppression; median (range) EDI to suppression 141 (38–2,909)
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Abbreviations: IQR, interquartile range; wk, week; EDI, estimated date of infection; ART, antiretroviral therapy; tp, timepoint;

Effect of HIV suppression on the concentration of each chemokine/cytokine in blood and seminal plasma

To investigate the net effect (downregulation or upregulation) of ART on the cytokine network in blood and in semen, the concentrations of each individual chemokine/cytokine were log10-transformed and the differences pre- vs. post-HIV suppression were plotted in Figure 1. Among 28 detectable cytokines in blood plasma, 6 were downregulated (decrease > 0.1 log10) (in descending order, MIG, IP-10, I-TAC, IL-18, IL-16, and IL-21), 16 remained mostly unchanged (decrease or increase < 0.1 log10) (TNF-α, IL-1α, M-CSF, MIP-1α, IL-12, GM-CSF, IL-7, CalA, MIP-3α, TGF-β, IL-4, IL-15, IL-6, MCP-1, cmv-IL-10, and IFN-γ), and 6 were upregulated (increase > 0.1 log10) in ascending order (GRO-α, RANTES, IL-1β, Eotaxin, IL-22, and MIP-1β). In seminal plasma, the overall picture was quite different. Among 29 detectable cytokines in seminal plasma, 16 were downregulated (MIG, IL-15, I-TAC, IL-7, Eotaxin, IL-21, IP-10, RANTES, M-CSF, IL-4, TGF-β, MCP-1, MIP-3α, GM-CSF, MIP-1α, and TNF-α), 6 remained unchanged (IL-1α, IL-18, IL-17, IL-6, IL-16, and GRO-α), while 7 increased (IL-8, CalA, MIP-1β, IL-1β, IFN-γ, cmv-IL-10, and IL-12). Only three chemokine/cytokines (IP-10, MIG, and IL-21) were downregulated in both blood and seminal plasma, while only IL-1β increased in both blood and plasma.

Figure 1: Effect of ART on chemokine/cytokine in blood and seminal plasma.

Figure 1:

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Shown is the difference of the concentrations of 34 chemokine/cytokines (log10 normalized) in PWH before and after antiretroviral therapy. Chemokine/cytokine concentrations in blood plasma (A) or seminal plasma (B) were log10-transformed and plotted as boxplots. For each cytokine and each boxplot, the box represent the interquartile range (IQR), the middle line represents the median, while the dots are outliers. A negative boxplot value reflects a decrease of the concentration of a given chemokine/cytokine following ART initiation, while a positive value reflects an upregulation of a given chemokine/cytokine.

To assess possible relationships between absolute cytokine levels and viral loads, we also calculated the Spearman rank-based correlation between viral loads and cytokine concentrations at the pre-suppression time point for both blood and semen. None of the correlations was significant after FDR p-value adjustment (data not shown). We also visualized individual cytokine levels versus time before and after viral suppression (Fig. 2).

Figure 2: Visual comparison of blood cytokine level and time before/after viral suppression.

Figure 2:

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Shown are graphical representations of log-transformed cytokine levels before (orange) and after (blue) HIV suppression. X-axis is shown without numbers, but is in weeks.

Visualizing pre- and post-suppression multivariate cytokine profiles in two dimensions

Multilevel Partial Least Squares-Discriminant Analysis (PLS-DA) was used here to create linear combinations based on latent variables (LVs) to maximize the separation between paired pre-suppression and post-suppression cytokine profiles. Although many LVs can be generated, we found that two were optimal both in semen and blood samples to compare cytokine profiles, with the first LV (LV1) explaining a higher proportion of the variance than the second LV (LV2). For both LVs, each cytokine was assigned a weight or loading. Using these loadings, a VIP score was calculated to determine how useful a cytokine was for classification.

In blood, the PLS projections showed a somewhat distinct separation between the cytokine profiles pre- and post-suppression. The separation between the two groups, measured as the energy (E) statistic, was not statistically different (E-statistic = 5.83, p = 0.54). The two LVs from PLS-DA accounted for 48% of the variation in cytokine expression (LV1: 14%; LV2: 34%) (Fig. 3A). In our analysis, any cytokines with VIP scores exceeding 1 were deemed important in predicting suppression status. As a result, in blood plasma, the most important cytokines in discriminating the two groups (from more to less important) were IP-10, MIG, IL-18, IL-16, I-TAC, MIP-1β, and MCP-1 (Fig. 4A, Table 2). Upon suppression of HIV, study participants exhibited lower mean concentrations of IP-10, MIG, IL-18, IL-16, and I-TAC in blood plasma and higher concentrations of MCP-1 and MIP-1β. IP-10 and MIG were the two cytokines with the biggest decrease in blood, while MIP-1β was the cytokine with the highest increase post-suppression (Fig. 1).

Figure 3: Two-dimensional PLS projections in blood and seminal plasma.

Figure 3:

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Shown are PLS-DA projections in two LVs with ellipses representing Hotelling’s 2-samples T2 with 95% confidence intervals in blood plasma (A) and in seminal plasma (B). The E-statistic was used to test the statistical differences in the separation between the cytokine profiles of the pre- and post-suppression groups. The multivariate distance between pre-suppression and post-suppression observations was significant in semen (p = 0.04) but not in blood (p = 0.54).

Figure 4: Latent variables LV1, LV2, and VIP scores of each cytokine in PLS-DA projections.

Figure 4:

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Shown are variable importance in projection (VIP) score plots for all cytokines in blood plasma (A) and in seminal plasma (B) colored by importance (red = higher VIP, grey = lower). The VIP score gives an estimate of the contribution of each cytokine to the PLS-DA regression model. Only the names of those cytokines with VIP above 1 are shown. The VIP scores derived by the latent variable loadings showed that more cytokines in semen (n = 9) were important in determining group classification than in blood (n = 7).

Table 2.

Results of Wilcoxon Signed Rank Test for cytokines with VIP > 1 in each respective sample type. We adjusted p using the false discovery rate correction by Benjamini and Hochberg, and verified the results using the Romano-Wolf rejection for family-wise error rates. Four cytokines were significantly different across time points in semen and two were significantly different in blood.

Sample Type Cytokine VIP W p Adjusted p Romano-Wolf Rejection (Y/N)
Genital Secretions IL-15 1.86 182 <0.001* <0.001* Y
IL-7 1.79 180 <0.001* <0.001* Y
MIG 1.76 184 <0.001* <0.001* Y
ITAC 1.50 159 0.008* 0.019* Y
IL-12 1.56 34 0.045 0.081 N
cmvIL-10 1.49 39 0.141 0.158 N
M-CSF 1.33 130 0.169 0.169 N
Eotaxin 1.24 117 0.056 0.084 N
IP-10 1.04 138 0.087 0.112 N
Blood MIG 2.15 169 0.002* 0.006* Y
IP-10 2.14 169 0.002* 0.006* Y
ITAC 1.77 101 0.093 0.164 N
IL-18 1.40 142 0.06 0.14 N
MCP-1 1.29 53 0.464 0.464 N
IL-16 1.22 131 0.156 0.182 N
MIP-1β 1.00 57 0.134 0.182 N
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In semen, the two-dimensional PLS-DA projections showed even more separation (E-statistic = 10.86, p = 0.04). The two LVs from PLS-DA accounted for 36% of the variation in cytokine expression (LV1, 22%; LV2, 14%) (Fig. 3B). The most important cytokines in discriminating the two groups (from more to less important) were MIG, IL-15, IL-7, I-TAC, IP-10, M-CSF, eotaxin, cmvIL-10, and IL-12 (Fig. 4B, Table 2). Upon suppression of HIV, study participants exhibited lower mean concentrations of MIG, IL-15, IL-7, I-TAC, IP-10, M-CSF, and Eotaxin in seminal plasma and higher concentrations of cmvIL-10 and IL-12. MIG, IL-15, IL-7, I-TAC, and eotaxin were the cytokines with the biggest decrease in semen while cmvIL-10 and IL-12 were the two cytokines with the highest increase post-suppression (Fig. 1).

Cytokines deemed important in predicting suppression status were different in blood and seminal plasma

Each cytokine was assigned a VIP score according to its importance in the difference of the cytokine profiles pre- and post-HIV suppression. As shown in Figure 5, the profiles of the VIP scores of each cytokine were, overall, different between blood and seminal plasma. Considering only the cytokines with VIP>1, which are the best predictors of HIV suppression status, we found that nine were important in semen and seven in blood. More importantly, among these cytokines, only three of them (IP-10, MIG and I-TAC) were common to blood and semen. Moreover, the VIP scores of these three cytokines were consistently superior in blood compared with those in semen. Other cytokines had a positive VIP score in one compartment but not in the other (IL-8, IL-17, IL-22).

Figure 5: Visual comparison of VIP scores between blood and seminal plasma.

Figure 5:

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Shown is a graphical representation of the VIP scores for each cytokine in blood (blue) and semen (orange). Cytokines with the highest VIP scores were the best predictors of suppression status. Only three cytokines (IP-10, MIG, and I-TAC) had VIP>1 both in blood and semen.

Subsequently, we tested any cytokines with VIP scores exceeding 1 individually for difference across time points using Wilcoxon Signed Rank Tests (Table 2). We adjusted the p-values of these multiple comparisons using a False Discovery Rate (FDR) [25] and verified results using Romano Wolf-controlled error rates for multiple testing, a method that takes into account the dependence structure of individual test statistics.

Tested individually, four cytokines were significantly different across time points in semen (MIG, p < 0.001; IL-15, p < 0.001; IL-7, p < 0.001; I-TAC, p = 0.019), while only two were significantly different across time points in blood (MIG, p = 0.006; IP-10, p = 0.006) at α = 0.05. The results of the Romano-Wolf correction for multiple family-wise comparisons were entirely consistent with these results (Table 2).

DISCUSSION

Cytokine/chemokines are intercellular signaling molecules that regulate a variety of physiological and pathological functions in the body. Acting as a coordinating, complex network, they play a major role in regulating our immune system in response to infectious pathogens, including HIV [26]. Numerous studies have shown that the acute/early stages of HIV infection are characterized by an intense cytokine storm [27–31] with profound disruptions in the cytokine network evident in blood and semen [10, 11, 13, 16, 18]. These disruptions at the earliest stage of HIV infection have immunopathological consequences, promoting immune activation, viral replication, and CD4+ T-cell loss, thus contributing to HIV pathogenesis and HIV disease progression (reviewed in [7, 32, 33]). For example, it has been reported that rapid disease progressors have earlier and more robust cytokine storms, compared with slow disease progressors [28].

Although ART dramatically reduces HIV viral load and improves the life span and quality of life for most patients, people with HIV (PWH) are more likely to develop serious non-AIDS comorbidities in part due to recurrent immune activation and dysregulation of the cytokine network (reviewed in [34]). In this study, we compared the expression of 34 cytokine/chemokines in blood and semen of 19 acute/early individuals with HIVfollowed longitudinally before and after HIV suppression. We analyzed the differences of cytokine/chemokine profiles using the multivariate statistical technique PLS-DA.

In blood, the PLS-DA analysis revealed that seven cytokines with a VIP score >1 were the best predictors of suppression status. These cytokines were IP-10, MIG, I-TAC, IL-18, IL-16, MCP-1, and MIP-1β. Although PLS projections of the cytokine profiles pre- and post-suppression were overall not statistically significant, the decrease in the concentrations of two cytokines (IP-10 and MIG) was statistically different between the two groups, suggesting that the virus directly or indirectly drives the production of IP-10 and MIG or vice-versa. Both cytokines have been reported to strongly correlate with HIV viral load and disease progression [13, 16, 19, 30, 31, 35–42]. In particular, IP-10 is recognized as an early biomarker that predicts the severity of various diseases (reviewed in [43]) and has been considered as a screening tool to optimize HIV RNA monitoring in resource-limited settings [37].

The decrease of IP-10 and MIG in blood of PWH treated in the acute/early phase of HIV infection is in agreement with previous reports [31, 36, 41]. IP-10, MIG, and I-TAC (which was actually the cytokine with the third greatest decrease post-HIV suppression in our study) are all interferon-gamma-inducible proteins and are ligands for the chemokine receptor CXCR3. CXCR3 is upregulated on activated T cells and is expressed preferentially on Th1 cells, facilitating trafficking of Th1 CD4 and effector CD8 T cells to peripheral sites of inflammation [39]. These findings are consistent with the robust Th1 response observed in the acute/early phase of HIV infection, which is then abated by ART-mediated suppression of viral replication [44]. The significant decrease of CXCR3 ligands is consistent with the systemic control of HIV-1 RNA replication resulting in reduced trafficking and recruitment of HIV-specific effector cells in lymphoid tissues as well as peripheral sites.

In semen, the PLS-DA analysis revealed that nine cytokine/chemokines (MIG, IL-15, IL-7, I-TAC, IP-10, M-CSF, Eotaxin; cmvIL-10, and IL-12) with a VIP score >1 were the best predictors of suppression status. Only 3 of these 9 cytokine/chemokines also had a VIP>1 in blood (IP-10, MIG, and I-TAC). In semen, the PLS projections between the cytokine profiles pre- and post-suppression were more pronounced than in blood and were statistically significant, confirming that blood and semen are two different immunological compartments, as previously demonstrated in individuals without HIV [11, 16] or in PWH [2, 11, 12, 16, 18]. The Wilcoxon signed-rank test at the individual level was significant for MIG, IL-15, I-TAC, and IL-7. IL-7 and IL-15 are cytokines of the gamma-chain family involved in T-cell homeostasis and expansion [45, 46], supporting the findings of the PLS analysis. They both have been reported to be upregulated upon HIV infection in semen [10, 11, 18, 47], most likely as they try to maintain immunological homeostasis in response to the progressive HIV-mediated loss of T cells. IL-15 has been shown to set HIV viral load and to accelerate disease progression [48–50], while IL-7 has been shown to favor HIV replication [17]. The decreased concentrations of IL-7 and IL-15 observed post-suppression in our study are likely due to increased T-cell count following viral suppression. Similarly to what was observed in blood, the decrease of MIG and I-TAC in semen is likely related to the reduced need for a strong Th1 response following HIV suppression and the need to maintain immunological homeostasis.

In contrast to our findings reported here, other studies have found no difference in the cytokine profiles between pre- and post-ART treatment in blood or in semen [16, 51, 52]. Although these were cross-sectional and not longitudinal studies, the most important aspect may be that treatment started in the chronic phase of HIV in these prior studies, highlighting the benefits of early treatment. Although early ART does not cure HIV disease, in part because of rapid viral reservoir establishment, benefits have been clearly established for long-term health in PWH [53, 54], and maintaining ART is a powerful mechanism for preventing forward transmission of HIV [55, 56]. Early initiation of ART has a beneficial impact on the recovery of helper CD4+ T cells [57] and Th17 cells [58], but also on circulating B cells [59] and even eosinophils and basophils [41]. When ART is initiated late, the benefits may not be the same, in part because the network of cytokines becomes more rigid, establishing new strong correlations and imposing a higher rigidity between them [2, 28, 60].

There are a number of limitations to the current study. First, we did not have access to samples pre-HIV infection to investigate whether the concentrations of cytokines return to baseline post-treatment. However, it is striking to see that cytokines downregulated in our study upon early ART were the same cytokines found to be upregulated in numerous studies investigating the effect of acute/early HIV infection on cytokines. Second, PLS-DA projections on a small sample size—as in our study—tend to exaggerate differences between groups. Third, our study cohort was entirely MSM and may not be completely applicable to other men or individuals of other genders because of the relative higher frequency of sexual encounters and STIs [13]. Finally, we did not quantify extracellular vesicle (EV)-associated cytokines [61, 62]. Biologically active cytokines can be find in soluble form as well as inside (encapsulated) or outside (membrane-bound) EVs. In the present work, membrane-bound and soluble cytokines were measured together. Although EV-encapsulated cytokines may play an important role in physiology, they are generally less represented than other forms of cytokines and were not measured here [62]. Similar to the release of accumulated viral products in EVs following HIV treatment [63], EV-entrapment may be a mechanism to dispose of cytokines when they are over-produced. Also, it may protect releasing cells from an autocrine effect and facilitate cytokine delivery to distant target cells. Future work may evaluate the changes in all three types of cytokines in HIV infection.

In summary, despite these limitations, the main strength of our study is to report on the effect of early ART on cytokine profiles in both blood and plasma in a longitudinal setting. Our findings provide further support to the benefits of earlier ART initiation, since the nature of the early cytokine profile may set the tempo for later immune-driven pathologies [41].

Supplementary Material

Supplementary Data

ACKNOWLEDGEMENTS

We are grateful to all the study participants and all the nurses at all the enrollment sites.

SR, FT, AW and SG: data collection. SR, FT, AW, SG and CV: data analysis and interpretation. WF: development of the luminex assay. SR, FT, AW, AL, WF, LM, SG and CV: original draft writing, review and final editing.

Conflicts of Interest and Source of Funding:

Authors declare no conflict of interest. SAR was supported by NIH training grant T32 AI 007384–28. This work was performed with the support of the Translational Virology Core at the San Diego Center for AIDS Research (P30 AI036214) and The James B. Pendleton Charitable Trust. SG was supported by AI147821 (Gianella-CMV Replication on HIV Persistence) and DA051915 (Gianella-Avenir).

Abbreviations:

MSM
PLS-DA
HIV

References

  • 1.Gianella S, Letendre S. Cytomegalovirus and HIV: A Dangerous Pas de Deux. J Infect Dis 2016; 214 Suppl 2:S67–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Lisco A, Introini A, Munawwar A, Vanpouille C, Grivel JC, Blank P, et al. HIV-1 imposes rigidity on blood and semen cytokine networks. Am J Reprod Immunol 2012; 68(6):515–521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Alfano M, Crotti A, Vicenzi E, Poli G. New players in cytokine control of HIV infection. Curr HIV/AIDS Rep 2008; 5(1):27–32. [DOI] [PubMed] [Google Scholar]
  • 4.Catalfamo M, Le Saout C, Lane HC. The role of cytokines in the pathogenesis and treatment of HIV infection. Cytokine Growth Factor Rev 2012; 23(4–5):207–214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Reuter MA, Pombo C, Betts MR. Cytokine production and dysregulation in HIV pathogenesis: lessons for development of therapeutics and vaccines. Cytokine Growth Factor Rev 2012; 23(4–5):181–191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Deeks SG. HIV infection, inflammation, immunosenescence, and aging. Annu Rev Med 2011; 62:141–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Hunt PW. HIV and inflammation: mechanisms and consequences. Curr HIV/AIDS Rep 2012; 9(2):139–147. [DOI] [PubMed] [Google Scholar]
  • 8.Ipp H, Zemlin A. The paradox of the immune response in HIV infection: when inflammation becomes harmful. Clin Chim Acta 2013; 416:96–99. [DOI] [PubMed] [Google Scholar]
  • 9.Hoffman JC, Anton PA, Baldwin GC, Elliott J, Anisman-Posner D, Tanner K, et al. Seminal plasma HIV-1 RNA concentration is strongly associated with altered levels of seminal plasma interferon-gamma, interleukin-17, and interleukin-5. AIDS Res Hum Retroviruses 2014; 30(11):1082–1088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Lisco A, Munawwar A, Introini A, Vanpouille C, Saba E, Feng X, et al. Semen of HIV-1-infected individuals: local shedding of herpesviruses and reprogrammed cytokine network. J Infect Dis 2012; 205(1):97–105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Olivier AJ, Masson L, Ronacher K, Walzl G, Coetzee D, Lewis DA, et al. Distinct cytokine patterns in semen influence local HIV shedding and HIV target cell activation. J Infect Dis 2014; 209(8):1174–1184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Politch JA, Tucker L, Bowman FP, Anderson DJ. Concentrations and significance of cytokines and other immunologic factors in semen of healthy fertile men. Hum Reprod 2007; 22(11):2928–2935. [DOI] [PubMed] [Google Scholar]
  • 13.Vanpouille C, Frick A, Rawlings SA, Hoenigl M, Lisco A, Margolis L, et al. Cytokine Network and Sexual HIV Transmission in Men Who Have Sex With Men. Clin Infect Dis 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Berlier W, Bourlet T, Levy R, Lucht F, Pozzetto B, Delezay O. Amount of seminal IL-1beta positively correlates to HIV-1 load in the semen of infected patients. J Clin Virol 2006; 36(3):204–207. [DOI] [PubMed] [Google Scholar]
  • 15.Sheth PM, Danesh A, Shahabi K, Rebbapragada A, Kovacs C, Dimayuga R, et al. HIV-specific CD8+ lymphocytes in semen are not associated with reduced HIV shedding. J Immunol 2005; 175(7):4789–4796. [DOI] [PubMed] [Google Scholar]
  • 16.Vanpouille C, Introini A, Morris SR, Margolis L, Daar ES, Dube MP, et al. Distinct cytokine/chemokine network in semen and blood characterize different stages of HIV infection. AIDS 2016; 30(2):193–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Introini A, Vanpouille C, Lisco A, Grivel JC, Margolis L. Interleukin-7 facilitates HIV-1 transmission to cervico-vaginal tissue ex vivo. PLoS Pathog 2013; 9(2):e1003148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Anderson JA, Ping LH, Dibben O, Jabara CB, Arney L, Kincer L, et al. HIV-1 Populations in Semen Arise through Multiple Mechanisms. PLoS Pathog 2010; 6(8):e1001053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Cheret A, Durier C, Melard A, Ploquin M, Heitzmann J, Lecuroux C, et al. Impact of early cART on HIV blood and semen compartments at the time of primary infection. PLoS One 2017; 12(7):e0180191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gianella S, Strain MC, Rought SE, Vargas MV, Little SJ, Richman DD, et al. Associations between virologic and immunologic dynamics in blood and in the male genital tract. J Virol 2012; 86(3):1307–1315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Rawlings SA, Ignacio C, Porrachia M, Du P, Smith DM, Chaillon A. No Evidence of SARS-CoV-2 Seminal Shedding Despite SARS-CoV-2 Persistence in the Upper Respiratory Tract. Open Forum Infect Dis 2020; 7(8):ofaa325. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Vargas-Meneses MV, Massanella M, Ignacio CC, Gianella S. Quantification of HIV RNA and Human Herpesvirus DNA in Seminal Plasma. Bio Protoc 2015; 5(9). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Fichorova RN, Richardson-Harman N, Alfano M, Belec L, Carbonneil C, Chen S, et al. Biological and technical variables affecting immunoassay recovery of cytokines from human serum and simulated vaginal fluid: a multicenter study. Anal Chem 2008; 80(12):4741–4751. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Székely GJ, Rizzo ML. Energy statistics: A class of statistics based on distances. Journal of Statistical Planning and Inference 2013; 143(8):1249–1272. [Google Scholar]
  • 25.Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological) 1995; 57(1):289–300. [Google Scholar]
  • 26.Kakar S Cytokines evolution: Role in various diseases. Current Medicine Research and Practice 2015; 5(4):176–182. [Google Scholar]
  • 27.Kedzierska K, Crowe SM. Cytokines and HIV-1: interactions and clinical implications. Antivir Chem Chemother 2001; 12(3):133–150. [DOI] [PubMed] [Google Scholar]
  • 28.Huang X, Liu X, Meyers K, Liu L, Su B, Wang P, et al. Cytokine cascade and networks among MSM HIV seroconverters: implications for early immunotherapy. Sci Rep 2016; 6:36234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Katsikis PD, Mueller YM, Villinger F. The cytokine network of acute HIV infection: a promising target for vaccines and therapy to reduce viral set-point? PLoS Pathog 2011; 7(8):e1002055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Stacey AR, Norris PJ, Qin L, Haygreen EA, Taylor E, Heitman J, et al. Induction of a striking systemic cytokine cascade prior to peak viremia in acute human immunodeficiency virus type 1 infection, in contrast to more modest and delayed responses in acute hepatitis B and C virus infections. J Virol 2009; 83(8):3719–3733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Teigler JE, Leyre L, Chomont N, Slike B, Jian N, Eller MA, et al. Distinct biomarker signatures in HIV acute infection associate with viral dynamics and reservoir size. JCI Insight 2018; 3(10). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Klatt NR, Funderburg NT, Brenchley JM. Microbial translocation, immune activation, and HIV disease. Trends Microbiol 2013; 21(1):6–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Moir S, Chun TW, Fauci AS. Pathogenic mechanisms of HIV disease. Annu Rev Pathol 2011; 6:223–248. [DOI] [PubMed] [Google Scholar]
  • 34.Lederman MM, Funderburg NT, Sekaly RP, Klatt NR, Hunt PW. Residual immune dysregulation syndrome in treated HIV infection. Adv Immunol 2013; 119:51–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Durudas A, Chen HL, Gasper MA, Sundaravaradan V, Milush JM, Silvestri G, et al. Differential innate immune responses to low or high dose oral SIV challenge in Rhesus macaques. Curr HIV Res 2011; 9(5):276–288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Gay C, Dibben O, Anderson JA, Stacey A, Mayo AJ, Norris PJ, et al. Cross-sectional detection of acute HIV infection: timing of transmission, inflammation and antiretroviral therapy. PLoS One 2011; 6(5):e19617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Pastor L, Casellas A, Ruperez M, Carrillo J, Maculuve S, Jairoce C, et al. Interferon-gamma-Inducible Protein 10 (IP-10) as a Screening Tool to Optimize Human Immunodeficiency Virus RNA Monitoring in Resource-Limited Settings. Clin Infect Dis 2017; 65(10):1670–1675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Ploquin MJ, Madec Y, Casrouge A, Huot N, Passaes C, Lecuroux C, et al. Elevated Basal Pre-infection CXCL10 in Plasma and in the Small Intestine after Infection Are Associated with More Rapid HIV/SIV Disease Onset. PLoS Pathog 2016; 12(8):e1005774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Sarkar S, Kalia V, Murphey-Corb M, Montelaro RC, Reinhart TA. Expression of IFN-gamma induced CXCR3 agonist chemokines and compartmentalization of CXCR3+ cells in the periphery and lymph nodes of rhesus macaques during simian immunodeficiency virus infection and acquired immunodeficiency syndrome. J Med Primatol 2003; 32(4–5):247–264. [DOI] [PubMed] [Google Scholar]
  • 40.Lee S, Chung YS, Yoon CH, Shin Y, Kim S, Choi BS, et al. Interferon-inducible protein 10 (IP-10) is associated with viremia of early HIV-1 infection in Korean patients. J Med Virol 2015; 87(5):782–789. [DOI] [PubMed] [Google Scholar]
  • 41.Muema DM, Akilimali NA, Ndumnego OC, Rasehlo SS, Durgiah R, Ojwach DBA, et al. Association between the cytokine storm, immune cell dynamics, and viral replicative capacity in hyperacute HIV infection. BMC Med 2020; 18(1):81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Jiao Y, Zhang T, Wang R, Zhang H, Huang X, Yin J, et al. Plasma IP-10 is associated with rapid disease progression in early HIV-1 infection. Viral Immunol 2012; 25(4):333–337. [DOI] [PubMed] [Google Scholar]
  • 43.Liu M, Guo S, Hibbert JM, Jain V, Singh N, Wilson NO, et al. CXCL10/IP-10 in infectious diseases pathogenesis and potential therapeutic implications. Cytokine Growth Factor Rev 2011; 22(3):121–130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Clerici M, Shearer GM. A TH1-->TH2 switch is a critical step in the etiology of HIV infection. Immunol Today 1993; 14(3):107–111. [DOI] [PubMed] [Google Scholar]
  • 45.Ma A, Koka R, Burkett P. Diverse functions of IL-2, IL-15, and IL-7 in lymphoid homeostasis. Annu Rev Immunol 2006; 24:657–679. [DOI] [PubMed] [Google Scholar]
  • 46.Rochman Y, Spolski R, Leonard WJ. New insights into the regulation of T cells by gamma(c) family cytokines. Nat Rev Immunol 2009; 9(7):480–490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Camus C, Matusali G, Bourry O, Mahe D, Aubry F, Bujan L, et al. Comparison of the effect of semen from HIV-infected and uninfected men on CD4+ T-cell infection. AIDS 2016; 30(8):1197–1208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Manganaro L, Hong P, Hernandez MM, Argyle D, Mulder LCF, Potla U, et al. IL-15 regulates susceptibility of CD4(+) T cells to HIV infection. Proc Natl Acad Sci U S A 2018; 115(41):E9659–E9667. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Mueller YM, Do DH, Altork SR, Artlett CM, Gracely EJ, Katsetos CD, et al. IL-15 treatment during acute simian immunodeficiency virus (SIV) infection increases viral set point and accelerates disease progression despite the induction of stronger SIV-specific CD8+ T cell responses. J Immunol 2008; 180(1):350–360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Swaminathan S, Qiu J, Rupert AW, Hu Z, Higgins J, Dewar RL, et al. Interleukin-15 (IL-15) Strongly Correlates with Increasing HIV-1 Viremia and Markers of Inflammation. PLoS One 2016; 11(11):e0167091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Chew CS, Cherry CL, Kamarulzaman A, Yien TH, Aghafar Z, Price P. A longitudinal study of the effects of ART on plasma chemokine levels in Malaysian HIV patients. Dis Markers 2011; 31(5):303–309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Ondoa P, Gautam R, Rusine J, Lutter R, Jurriaans S, Kootstra N, et al. Twelve-Month Antiretroviral Therapy Suppresses Plasma and Genital Viral Loads but Fails to Alter Genital Levels of Cytokines, in a Cohort of HIV-Infected Rwandan Women. PLoS One 2015; 10(5):e0127201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Lundgren JD, Borges AH, Neaton JD. Serious Non-AIDS Conditions in HIV: Benefit of Early ART. Curr HIV/AIDS Rep 2018; 15(2):162–171. [DOI] [PubMed] [Google Scholar]
  • 54.Sandler NG, Sereti I. Can early therapy reduce inflammation? Curr Opin HIV AIDS 2014; 9(1):72–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, et al. Antiretroviral Therapy for the Prevention of HIV-1 Transmission. N Engl J Med 2016; 375(9):830–839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Rodger AJ, Cambiano V, Bruun T, Vernazza P, Collins S, van Lunzen J, et al. Sexual Activity Without Condoms and Risk of HIV Transmission in Serodifferent Couples When the HIV-Positive Partner Is Using Suppressive Antiretroviral Therapy. JAMA 2016; 316(2):171–181. [DOI] [PubMed] [Google Scholar]
  • 57.Dong KL, Moodley A, Kwon DS, Ghebremichael MS, Dong M, Ismail N, et al. Detection and treatment of Fiebig stage I HIV-1 infection in young at-risk women in South Africa: a prospective cohort study. Lancet HIV 2018; 5(1):e35–e44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Schuetz A, Deleage C, Sereti I, Rerknimitr R, Phanuphak N, Phuang-Ngern Y, et al. Initiation of ART during early acute HIV infection preserves mucosal Th17 function and reverses HIV-related immune activation. PLoS Pathog 2014; 10(12):e1004543. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Moir S, Buckner CM, Ho J, Wang W, Chen J, Waldner AJ, et al. B cells in early and chronic HIV infection: evidence for preservation of immune function associated with early initiation of antiretroviral therapy. Blood 2010; 116(25):5571–5579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Shebl FM, Yu K, Landgren O, Goedert JJ, Rabkin CS. Increased levels of circulating cytokines with HIV-related immunosuppression. AIDS Res Hum Retroviruses 2012; 28(8):809–815. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Konadu KA, Chu J, Huang MB, Amancha PK, Armstrong W, Powell MD, et al. Association of Cytokines With Exosomes in the Plasma of HIV-1-Seropositive Individuals. J Infect Dis 2015; 211(11):1712–1716. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Fitzgerald W, Freeman ML, Lederman MM, Vasilieva E, Romero R, Margolis L. A System of Cytokines Encapsulated in ExtraCellular Vesicles. Sci Rep 2018; 8(1):8973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.DeMarino C, Pleet ML, Cowen M, Barclay RA, Akpamagbo Y, Erickson J, et al. Antiretroviral Drugs Alter the Content of Extracellular Vesicles from HIV-1-Infected Cells. Sci Rep 2018; 8(1):7653. [DOI] [PMC free article] [PubMed] [Google Scholar]

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