Abstract
Introduction:
Rubella virus (RV) was eliminated in the United States in 2004, although a small portion of the population fails to develop long-term immunity against RV even after two doses of the measles-mumps-rubella (MMR) vaccine. We hypothesized that inherent biological differences in cytokine and chemokine signaling likely govern an individual’s response to a third dose of the vaccine.
Methods:
Healthy young women (n=97) were selected as study participants if they had either low or high extremes of RV-specific antibody titer after two previous doses of MMR vaccine. We measured cytokine and chemokine secretion from RV-stimulated PBMCs before and 28 days after they received a third dose of MMR vaccine and assessed correlations with humoral immune response outcomes.
Results:
High and low antibody vaccine responders exhibited a strong pro-inflammatory cellular response, with an underlying Th1-associated signature (IL-2, IFN-γ, MIP-1β, IP-10) and suppressed production of most Th2-associated cytokines (IL-4, IL-10, IL-13). IL-10 and IL-4 exhibited significant negative associations with neutralizing antibody titers and memory B cell ELISpot responses among low vaccine responders.
Conclusion:
IL-4 and IL-10 signaling pathways may be potential targets for understanding and improving the immune response to rubella vaccination or for designing new vaccines that induce more durable immunity.
Keywords: Rubella; Measles-Mumps-Rubella; MMR; Cytokine; Chemokine; Immunity, Innate; Immunity, Cellular; Immunity, Humoral
1. Introduction
Rubella is typically a mild, self-limiting viral disease that may present asymptomatically but can result in serious complications if contracted during the first trimester of pregnancy. Pregnant women infected with rubella virus (RV) are at an increased risk for miscarriage or stillbirth, and, even if the child survives, infection of the developing fetus often results in a series of developmental birth defects collectively termed congenital rubella syndrome (CRS) [1–3]. While cases of rubella are rare in the US (~10 per year) [4], RV remains endemic to many areas of the world, and more than 100,000 cases of CRS are still estimated to occur globally each year [3, 5].
Current US rubella vaccines are highly effective (> 95% seroconversion) [3, 6, 7], yet several studies have suggested that long-term immunity to RV may be suboptimal [8, 9]. Sero-surveillance studies have found that rates of seronegativity (i.e., RV-specific IgG titers < 10 IU/mL) range from 1.8–17% among vaccinated populations [8–10], suggestive of either waning immunity or poor initial vaccine responses. Humoral immunity following rubella vaccination has been well-studied [11, 12], but detailed studies of RV-specific cellular immunity and its influence on vaccine response outcomes are lacking. Cell-mediated immune responses are critical for orchestrating both innate and adaptive immunity through the secretion of cytokine and chemokine molecules [13–15]. We previously identified single-nucleotide polymorphisms (SNPs) in genes coding for cytokines and their receptors that were associated with RV-specific humoral and cellular immune responses [16–22], suggesting that intrinsic differences in cellular signaling may influence the response to rubella vaccination. A number of host factors—such as nutrition and genetics—have also been demonstrated to influence the development and durability of immune responses following vaccination [23–27]. The strong associations between host genetics and RV-specific immune response outcomes suggest that individuals who fail to maintain or generate long-term protective immune responses following vaccination will respond similarly to subsequent doses of the same vaccine. Furthermore, validation of these associations between inherent host biology and immune response could inform the practice of personalized vaccinology, whereby vaccine regimens could be tailored based on the presence (or absence) of causal genetic variants [28].
In this study, we recruited a cohort of 97 female subjects based on their baseline antibody titers to evaluate the association between humoral and cellular immunity to RV following a third MMR vaccine dose while accounting for prior vaccine response. We specifically selected individuals from the extremes of the antibody response spectrum to assess the inherent biology underlying differences in vaccine immune response outcomes to the third dose. To accomplish this, we assessed cytokine and chemokine production by peripheral blood mononuclear cells (PBMCs) in response to RV stimulation before and after vaccination.
2. Methods
The methods outlined are the same or similar to those described in our previous publications [12, 29, 30].
2.1. Human Subjects
The study cohort consisted of 97 female subjects (20–45 years of age) recruited from Olmsted County, MN, and the surrounding area. In a prior rubella surveillance study, 1,117 serum samples from the Mayo Clinic Biobank were screened using rubella-specific IgG ELISA test kits (Zeus Scientific, Inc.; Branchburg, NJ) [30]; from these existing samples, subjects were selected for the current study if their baseline antibody titers were in the top 30% or bottom 30% of antibody titers. All study subjects had two previously documented doses of MMR-II® vaccine. Study subjects participated in a baseline blood draw (prior to vaccination) and at 28 days post-vaccination. Serum and PBMC samples were processed for cryopreservation at each timepoint using previously established protocols [29]. Measurements of height and weight were collected to calculate body mass index (BMI). All study participants provided written informed consent, and all procedures were approved by the Mayo Clinic Institutional Review Board.
2.2. Rubella-specific Cytokine/Chemokine Assays
Rubella virus-specific cytokine and chemokine responses were measured in PBMC culture supernatants under optimized conditions using multiplex electrochemiluminescence-based kits (Meso Scale Diagnostics, LLC; Rockville, MD). Briefly, PBMCs (Baseline and Day 28) were thawed from cryogenic storage and 2×105 cells were plated in each well of a 96-well round bottom plate. Cells were incubated at 37°C for 48 h with media alone (unstimulated), rubella virus (W-Therien strain; MOI=0.5), or phytohemagglutinin (PHA; 5 μg/mL) as a positive control. Cell culture supernatants were subsequently harvested and stored at −80°C until analysis. Samples were assayed in duplicate by the Mayo Clinic Immunochemical Core Laboratory for the following: interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, interferon (IFN)-γ, IFN-α2a, tumor necrosis factor (TNF)-α, eotaxin-1, eotaxin-3, interferon gamma-induced protein (IP)-10, monocyte chemoattractant protein (MCP)-1, MCP-4, macrophage inflammatory protein (MIP)-1α, MIP-1β, macrophage-derived chemokine (MDC), and thymus and activation regulated chemokine (TARC). The coefficient of variation for each analyte assay was below 20%.
2.3. Rubella-specific Humoral Immune Response Assays
Rubella-specific total IgG antibody titer, neutralizing antibody titer, antibody avidity and B cell ELISpot response were all previously measured and reported [12].
2.4. Statistical Analysis
Correlations were assessed between cytokine and chemokine outcomes and RV-specific humoral response outcomes (B-cell ELISpot, neutralizing antibody titer, IgG antibody titer, antibody avidity), which was previously reported [12]. Each correlation was performed separately for low and high responders at each timepoint (Baseline and Day 28) using Spearman’s rank correlation. Spearman’s rank correlation was also performed for the measure of Day 28 – Baseline to evaluate the change in the response between timepoints for low and high responders. Univariate modeling was performed using each cytokine and chemokine as a predictor and neutralizing antibody as the outcome. For these models, an integer value of 1 was added to each neutralizing antibody reading and subsequently transformed on a log2 scale. False discovery rates (FDRs) were calculated using the Benjamini-Hochberg method.
3. Results
3.1. Subject Demographics and Prior Immune Response Variables
Study cohort demographics and variables related to prior antibody status are summarized in Table 1. The study cohort was comprised of 97 female subjects with a median age of 34.5 years (IQR=30.5 – 39.9) at the time of enrollment. The majority of study subjects (99 %) were White/Non-Hispanic or Latino. Subjects in the study cohort were classified as low responders (n=53) or high responders (n=44) based on their RV-specific IgG screening titer prior to enrollment. The responder groups were well-matched in age, with a median age of 35.7 years (IQR=31.3 – 39.6) for the low responder group and 33.1 years (IQR=30.0 – 40.6) for the high responder group. The median RV-specific IgG titer for the low responder group was 18.3 IU/mL (IQR=11.2 – 22.8) and 94.0 IU/mL (IQR=80.7 – 117.6) for the high responder group. The median BMI was 27.3 for both the low responder group (IQR=22.3 – 34.5) and the high responder group (IQR=24.4 – 32.0).
Table 1.
Study Cohort Demographics and Clinical Variables.
| Low Responders (n=53) | High Responders (n=44) | Total (n=97) | |
|---|---|---|---|
| Race | |||
| White | 53 (100.0%) | 43 (97.7%) | 96 (99.0%) |
| Other | 0 (0.0%) | 1 (2.3%) | 1 (1.0%) |
| Age (Years) | |||
| Mean (SD) | 34.99 (5.88) | 34.44 (6.47) | 34.74 (6.13) |
| Median | 35.69 | 33.11 | 34.53 |
| Q1, Q3a | 31.28, 39.57 | 29.99, 40.62 | 30.47, 39.94 |
| Range | 21.52 – 44.95 | 20.15 – 45.17 | 20.15 – 45.17 |
| RV Serum Titer (IU/mL) | |||
| Mean (SD) | 16.91 (6.80) | 105.14 (41.04) | 56.93 (52.24) |
| Median | 18.34 | 94.04 | 25.40 |
| Q1, Q3a | 11.15, 22.78 | 80.67, 117.56 | 17.23, 91.81 |
| Range | 3.72 – 27.25 | 56.75 – 267.29 | 3.72 – 267.29 |
| BMI | |||
| Mean (SD) | 28.89 (7.25) | 28.98 (6.20) | 28.93 (6.78) |
| Median | 27.26 | 27.31 | 27.28 |
| Q1, Q3a | 22.29, 34.47 | 24.41, 32.03 | 23.70, 32.75 |
| Range | 19.84 – 47.64 | 19.84 – 42.93 | 19.84 – 47.64 |
Q1 and Q3 represent the 25th and 75th percentiles, respectively.
3.2. Characterization of RV-specific Cytokine/Chemokine Responses
Cytokine and chemokine secretion in response to in vitro RV stimulation are summarized in Figure 1, Table 2, and Supplementary Figure S1. We observed statistically significant production of all detectable cytokines and chemokines at both timepoints in response to in vitro RV stimulation, although the amounts of several cytokines were notably low (e.g., < 10 pg/mL). Levels of eotaxin-3, IFN-α2a, MDC, and MIP-1α were undetectable in the majority of subjects and were excluded from subsequent analyses. The secreted levels of cytokines and chemokines were largely equivalent between high and low responder groups at both timepoints. The low responder group secreted higher levels of IL-8 compared to the high responder group at both Baseline (187.5 vs. 102.5 pg/mL) and Day 28 (224.5 vs. 181.5 pg/mL), although only the difference at Baseline was statistically significant (p = 0.047). Conversely, secreted levels of TARC (29.1 vs. 15.2 pg/mL; p = 0.013) were significantly higher in the high responder group at Day 28.
Figure 1.
Radar plots of the RV-specific cytokine and chemokine secretion profiles in PBMCs from high and low vaccine responders. Cytokine secretion profile at Baseline and Day 28 for (A) low responders and (B) high responders. Chemokine secretion profile at Baseline and Day 28 for (C) low responders and (D) high responders. Concentric levels increase outwards from 0.01–1,000 (A,B) and 0.01–10,000 (C,D) pg/mL on a log10 scale.
Table 2.
Rubella-specific Cytokine and Chemokine Levels.
| Time Point | Low Responders | High Responders | Total | |
|---|---|---|---|---|
| Cytokinesa | ||||
| IL-10 | Baseline | 1.12 (0.52, 1.95) | 1.08 (0.41, 1.98) | 1.12 (0.47, 1.95) |
| Day 28 | 1.25 (0.42, 2.17) | 1.58 (0.69, 2.96) | 1.42 (0.52, 2.47) | |
| Day 28 - Baseline | 0.11 (0.0, 1.06) | 0.17 (0.0, 1.47) | 0.15 (0.0, 1.22) | |
| IL-12p70 | Baseline | 2.28 (1.63, 3.28) | 1.84 (1.31, 3.09) | 2.13 (1.33, 3.23) |
| Day 28 | 2.03 (1.29, 3.13) | 1.88 (1.16, 3.42) | 2.03 (1.23, 3.18) | |
| Day 28 - Baseline | 0.0 (0.0, 0.44) | 0.09 (0.0, 0.55) | 0.0 (0.0, 0.5) | |
| IL-13 | Baseline | 5.94 (1.19, 9.34) | 2.74 (0.37, 9.80) | 4.20 (0.75, 9.49) |
| Day 28 | 4.94 (0.8, 10.14) | 3.89 (0.49, 10.37) | 4.24 (0.5, 10.14) | |
| Day 28 - Baseline | 0.0 (0.0, 1.94) | 0.0 (0.0, 1.76) | 0.0 (0.0, 1.8) | |
| IL-1β | Baseline | 0.50 (0.0, 1.51) | 1.15 (0.18, 2.17) | 0.78 (0.03, 1.81) |
| Day 28 | 0.53 (0.0. 1.41) | 0.53 (0.0, 2.20) | 0.53 (0.0, 1.91) | |
| Day 28 - Baseline | 0.0 (0.0, 0.36) | 0.0 (0.0, 0.18) | 0.0 (0.0, 0.36) | |
| IL-2 | Baseline | 6.20 (3.0, 13.7) | 7.83 (3.49, 17.3) | 7.65 (3.20, 15.7) |
| Day 28 | 13.30 (4.16, 20.5) | 16.95 (7.16, 28.4) | 13.90 (5.60, 24.9) | |
| Day 28 - Baseline | 4.70 (0.0, 13.34) | 5.83 (0.0, 15.8) | 5.00 (0.0, 14.8) | |
| IL-4 | Baseline | 2.05 (1.65, 2.68) | 1.60 (1.14, 2.41) | 1.85 (1.40, 2.65) |
| Day 28 | 1.95 (1.40, 2.65) | 1.88 (1.30, 2.57) | 1.95 (1.30, 2.65) | |
| Day 28 - Baseline | 0.0 (0.0, 0.35) | 0.08 (0.0, 0.40) | 0.05 (0.0, 0.35) | |
| IL-6 | Baseline | 486.4 (484.7, 486.4) | 484.7 (477.0, 486.4) | 486.3 (481.6, 486.4) |
| Day 28 | 486.4 (486.2, 486.4) | 486.4 (485.9, 486.4) | 486.4 (485.9, 486.4) | |
| Day 28 - Baseline | 0.0 (0.0, 1.76) | 0.05 (0.0, 4.91) | 0.0 (0.0, 2.67) | |
| IFN-γ | Baseline | 5.51 (0.0, 20.25) | 5.49 (0.12, 32.25) | 5.51 (0.0, 21.78) |
| Day 28 | 24.68 (4.08, 78.63) | 44.36 (3.51, 102.8) | 28.13 (4.01, 80.73) | |
| Day 28 - Baseline | 13.66 (0.0, 68.60) | 19.55 (0.95, 88.02) | 14.80 (0.0, 74.80) | |
| TNF-α | Baseline | 3.15 (2.10, 4.95) | 3.00 (1.47, 4.41) | 3.00 (1.90, 4.80) |
| Day 28 | 4.76 (1.75, 6.70) | 4.35 (2.03, 7.44) | 4.60 (1.95, 7.00) | |
| Day 28 - Baseline | 0.25 (0.0, 2.80) | 1.58 (0.0, 3.97) | 0.89 (0.0, 3.36) | |
| IFN-α2a | Baseline | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) |
| Day 28 | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | |
| Day 28 - Baseline | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | |
| Chemokinesa | ||||
| Eotaxin-1 | Baseline | 0.0 (0.0, 23.99) | 4.5 (0, 22.72) | 1.23 (0.0, 23.99) |
| Day 28 | 5.35 (0.0, 49.9) | 17.89 (0.0, 51.85) | 8.06 (0.0, 51.09) | |
| Day 28 - Baseline | 0.0 (0.0, 26.33) | 0.16 (0.0, 20.19) | 0.0 (0.0, 24.56) | |
| Eotaxin-3 | Baseline | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) |
| Day 28 | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | |
| Day 28 - Baseline | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | |
| IL-8 | Baseline | 187.5 (29.5, 244.5) | 102.5 (0.0, 194.5) | 141.5 (0.0, 235.9) |
| Day 28 | 224.5 (109.0, 286.85) | 181.5 (35.38, 261.38) | 197.5 (77.0, 267.0) | |
| Day 28 - Baseline | 30.5 (0.0, 143.5) | 34.68 (0.0, 135.62) | 31.4 (0.0, 143.5) | |
| IP-10 | Baseline | 522.5 (109.44, 1016.4) | 446.86 (198.24, 1183.6) | 513.0 (188.26, 1138.1) |
| Day 28 | 752.8 (363.33, 1336.2) | 1024.3 (486.93, 1464.5) | 890.3 (380.81, 1436.6) | |
| Day 28 - Baseline | 258.3 (0.0, 576.62) | 91.29 (0.0, 746.7) | 135.1 (0.0, 653.98) | |
| MCP-1 | Baseline | 1461.2 (1334.8, 1479.2) | 1439.9 (1371.9, 1472.5) | 1449.8 (1364.3, 1476.7) |
| Day 28 | 1449.4 (1386.9, 1477.8) | 1446.4 (1413.4, 1474.3) | 1449.4 (1410.2, 1477.5) | |
| Day 28 - Baseline | 0.0 (0.0, 53.32) | 6.12 (0.0, 51.13) | 0.1 (0.0, 52.38) | |
| MCP-4 | Baseline | 31.72 (17.76, 55.93) | 31.19 (19.18, 55.39) | 31.72 (17.76, 55.93) |
| Day 28 | 26.33 (7.77, 43.62) | 30.9 (15.25, 62.08) | 28.47 (10.25, 47.44) | |
| Day 28 - Baseline | 0.0 (0.0, 4.96) | 0.0 (0.0, 12.19) | 0.0 (0.0, 7.31) | |
| MDC | Baseline | 0.0 (0.0, 0.0) | 0.0 (0.0, 22.93) | 0.0 (0.0, 13.72) |
| Day 28 | 0.0 (0.0, 7.54) | 0.53 (0.0, 50.94) | 0.0 (0.0, 31.32) | |
| Day 28 - Baseline | 0.0 (0.0, 20.93) | 0.0 (0.0, 41.77) | 0.0 (0.0, 24.77) | |
| MIP-1α | Baseline | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.49) | 0.0 (0.0, 0.43) |
| Day 28 | 0.0 (0.0, 0.0) | 0.0 (0.0, 1.14) | 0.0 (0.0, 0.0) | |
| Day 28 - Baseline | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | |
| MIP-1β | Baseline | 8.3 (0.0, 38.04) | 13.36 (0.0, 43.42) | 12.12 (0.0, 40.77) |
| Day 28 | 22.09 (4.46, 76.87) | 55.19 (9.97, 112.92) | 30.39 (7.76, 96.69) | |
| Day 28 - Baseline | 21.8 (0.0, 56.39) | 23.6 (0.0, 76.15) | 22.72 (0.0, 65.1) | |
| TARC | Baseline | 11.11 (2.89, 22.46) | 14.66 (3.14, 33.05) | 11.52 (3.12, 26.16) |
| Day 28 | 15.22 (1.78, 27.9) | 29.1 (7.58, 46.9) | 19.18 (3.87, 36.5) | |
| Day 28 - Baseline | 1.21 (0.0, 13.71) | 8.01 (0.26, 17.77) | 4.03 (0.0, 16.55) |
Values reported are the Median (Q1, Q3) in pg/mL and have been corrected for background by subtracting measured values of unstimulated PBMCs.
3.3. Associations between RV-specific Cytokine/Chemokine Responses and Immune Response Outcomes
We previously reported the RV-specific humoral immune response outcomes (B-cell ELISpot, neutralizing antibody titer, total IgG antibody titer, antibody avidity) in this cohort following the third MMR vaccine dose [12]. These datasets are summarized in Supplementary Table S1. Heat maps summarizing the correlations between cytokine and chemokine responses and humoral immune response outcomes are presented in Figure 2, and Spearman’s rank correlations for cytokine/chemokines responses with immune response outcomes are summarized in Supplementary Table S2. Interestingly, we detected associations that were unique to each responder group. Only cytokines and chemokines displaying statistically significant (p < 0.05) correlations with an immune response outcome will be subsequently discussed. The FDR for all correlation analyses are provided in Supplementary Tables S2–S4 to allow for direct interpretation of the associations presented here.
Figure 2.
Spearman’s correlation heat maps of the relationships between immune response outcomes and secreted cytokine/chemokine levels. (A) Associations of cytokine/chemokine levels at Baseline with immune response outcomes at Day 28. (B) Associations of cytokine/chemokine levels at Baseline with the change in immune response outcomes (Day 28 – Baseline). (C) Associations of cytokine/chemokine levels at Day 28 with immune response outcomes at Day 28. (D) Associations of the change in cytokine/chemokine levels (Day 28 – Baseline) with the change in immune response outcomes (Day 28 – Baseline). Red indicates a positive correlation; blue indicates a negative correlation. *p<0.05.
3.3.1. Neutralizing Antibody Titer
We identified several statistically significant associations between neutralizing antibody titers and levels of secreted cytokines and chemokines within the high responder group. The Baseline levels of several cytokines and chemokines were associated with neutralizing antibody titers at Day 28 (Figure 2A). Secreted levels of IL-6 (r= −0.304, p=0.05), IL-8 (r= −0.326, p=0.03), and MCP-4 (r= −0.312, p=0.04) were negatively correlated with Day 28 neutralizing antibody titers, whereas IL-1β (r=0.388, p=0.01), IL-10 (r=0.363, p=0.02), IL-12p70 (r=0.371, p=0.01), and MCP-1 (r=0.337, p=0.03) levels were all positively correlated. Secreted levels of IL-1β (r=0.374, p=0.01), IL-12p70 (r=0.484, p=0.001), IL-13 (r=0.325, p=0.03), IL-10 (r=0.296, p=0.05) and IL-4 (r=0.432, p=0.004) at Baseline were all positively correlated with the change in neutralizing antibody titer from Baseline to Day 28 (Figure 2B). Negative correlations were observed between the secreted levels of IL-6 (r= −0.431, p=0.004) and IL-8 (r= −0.341, p=0.03) at Day 28 and neutralizing antibody titers at the same timepoint (Figure 2C).
Interestingly, several negative associations were identified between secreted cytokines and neutralizing antibody titers among low responders in our cohort. Similar to the high responder group, secreted levels of IL-6 (r= −0.277, p=0.05) and IL-8 (r= −0.33, p=0.02) at Day 28 were negatively associated with neutralizing antibody titers (Figure 2C), while Baseline levels of IL-1β (r=0.275, p=0.05) were positively correlated (Figure 2A). In contrast with our observations in the high responder group, Baseline levels of IL-13 were negatively correlated with neutralizing antibody titer at Day 28 (r= −0.297, p=0.03) (Figure 2A) as well as the change in neutralizing antibody titer from Baseline to Day 28 (r= −0.312, p=0.02) (Figure 2B). A significant negative correlation was observed between IL-10 levels and neutralizing antibody titer at Day 28 (r= −0.315, p=0.02) (Figure 2C), and a similar association was observed between the change in secreted IL-10 and the change in neutralizing antibody titer from Baseline to Day 28 (r= −0.326, p=0.02) (Figure 2D). Linear univariate modeling identified changes in both IL-10 (p=0.004) and IL-4 (p=0.006) levels from Baseline to Day 28 as significantly predictive of the change in neutralizing antibody titer among low responders (Supplementary Table S3). This same analysis did not identify any predictors of neutralizing antibody titer among high responders.
3.3.2. Total IgG Antibody Titer
Total RV-specific IgG antibody titers were significantly associated with cytokine and chemokine secretion among both responder groups. Among high responders, Baseline levels of IL-12p70 (r=0.314, p=0.04) were positively correlated with the increase in IgG antibody titer from Baseline to Day 28 (Figure 2B). Similar to neutralizing antibody titers, total IgG titers at Day 28 were negatively associated with levels of IL-6 (r= −0.296, p=0.05) at Day 28 among high responders (Figure 2C). The change in secreted levels of IL-2 (r=0.402, p=0.008), IFN-γ (r=0.439, p=0.003), eotaxin-1 (r=0.434, p=0.004), and IP-10 (r=0.438, p=0.003) from Baseline to Day 28 were all significantly correlated with the increase in IgG antibody titers among high responders (Figure 2D).
Baseline levels of IL-1β exhibited a positive association with the change in antibody titer from Baseline to Day 28 (r=0.303, p=0.03) among low responders (Figure 2B). Notably, changes in the secreted levels of IL-10 (r=0.298, p=0.05) from Baseline to Day 28 were positively correlated with the change in RV-specific IgG titers among high responders but negatively correlated among low responders (r= −0.308, p=0.03) (Figure 2D). Similarly, levels of IL-10 at Day 28 were negatively correlated (r= −0.282, p=0.04) with IgG titers at Day 28 among low responders (Figure 2C).
3.3.3. B Cell ELISpot
The change in TNF-α (r=0.429, p=0.01), IP-10 (r=0.414, p=0.01), and MCP-1 (r=0.356, p=0.04) levels from Baseline to Day 28 displayed positive associations with the corresponding change in B cell ELISpot response among high responders (Figure 2D). Baseline levels of IL-6 exhibited a negative correlation with the B cell ELISpot response at Day 28 (r= −0.364, p=0.03) (Figure 2C), while levels of IL-1β at Baseline (r=0.345, p=0.04) were positively associated with the change in B cell ELISpot response among high responders (Figure 2B).
Levels of IL-4 (r= −0.318, p=0.04) at Day 28 were negatively associated with memory B cell ELISpot responses at Day 28 (Figure 2C), and the change in IL-4 levels from Baseline to Day 28 (r= −0.333, p=0.03) was similarly associated with the change in B cell ELISpot response among low responders (Figure 2D). The change in IP-10 (r=0.308, p=0.05) levels was suggestive of a positive association with the change in memory B cell ELISpot response among low responders (Figure 2D).
3.3.4. Antibody Avidity
Baseline levels of IFN-γ (r=−0.326, p=0.03) exhibited a negative correlation with the change in antibody avidity among high responders (Figure 2B). Negative associations with several cytokines were also identified among low responders. Secreted levels of IL-2 (r= −0.287, p=0.04), and IL-10 (r= −0.356, p=0.009) at Day 28 were negatively correlated with antibody avidity among low responders (Figure 2C). The change in IL-1β (r= −0.36, p=0.01) levels from Baseline to Day 28 was also negatively associated with the change in antibody avidity for low responders, whereas the change in IL-8 levels (r=0.277, p=0.05) was suggestive of a positive correlation (Figure 2D).
3.4. Associations between RV-specific Cytokine/Chemokine Responses and Demographic Variables
Correlations between secreted cytokines/chemokines and BMI are summarized in Supplementary Table S4. Secreted levels of IL-1β (r=0.357, p=0.02) and IL-13 (r=0.305, p=0.05) at Baseline were positively correlated with BMI among high responders. Interestingly, levels of IL-13 at Baseline were significantly higher among all subjects with high BMI (> 25) compared to those with low BMI (5.94 vs. 2.74 pg/mL; p=0.04). In addition, Baseline levels of IL-6 (r= −0.317, p=0.04) and IL-8 (r= −0.399, p=0.008) exhibited negative correlations with BMI among high responders. Baseline levels of TNF-α (r=0.319, p=0.02) were the only significant association identified among low responders, and no significant associations were identified between cytokine/chemokine responses and age among either responder group.
4. Discussion
In the current study, we evaluated the cytokine and chemokine response of 97 females (30–40 years of age) following a third dose of MMR vaccine and analyzed associations with RV-specific humoral immune response outcomes previously reported for this cohort [12]. The majority of subjects exhibited a strong pro-inflammatory response to in vitro RV stimulation both before and after vaccination. While secreted cytokine and chemokine levels were largely similar between the two responder groups, markedly different associations with measures of humoral immunity were observed. IL-10 and IL-4 levels were identified as negative predictors of post-vaccination neutralizing antibody titers among low responders, and IL-4 exhibited a negative correlation with memory B cell ELISpot responses. IL-10 levels were also negatively correlated with IgG antibody titer and antibody avidity among low responders. In contrast, IgG antibody titers exhibited positive correlations with IL-10, IL-2, IFN-γ, eotaxin-1, and IP-10 among high responders. These findings provide evidence that markers of vaccine-induced cellular immunity exhibit different interindividual associations with humoral immune outcomes, suggesting that inherent differences in host biology govern an individuals’ response to vaccination.
Rubella-specific cellular immune responses were characterized by robust secretion of IL-6 and modest production of TNF-α at both timepoints (Figure 1 and Table 2), consistent with previous studies of rubella vaccine response [31, 32]. Lambert et al. observed inflammatory response profiles in healthy children and adolescents who had previously received two doses of MMR vaccine [32], and this study extends that work to show similar responses to rubella vaccine in healthy young adult women following a third vaccine dose. We also observed an underlying Th1-shifted cytokine/chemokine profile characterized by secretion of IL-2, IFN-γ, MIP-1β, and IP-10 with lower levels of Th2-associated cytokines (IL-4, IL-10, IL-13) at both timepoints (Figure 1 and Table 2), consistent with our previous report on rubella-specific cytokine responses in healthy adolescents after two doses of MMR vaccine [31]. While cellular immune responses to RV vaccination have been well-studied, detailed reports on the correlations between markers of cellular and humoral immunity to RV are lacking.
We identified numerous correlations between markers of cellular immunity and humoral immune response outcomes that may explain the biological predisposition of individuals to be high or low vaccine responders. Memory B cell responses negatively correlated with IL-4 among low responders (Figure 2A, B), which was unexpected given the established role of IL-4 in enhancing B cell stimulation and differentiation into antibody-secreting plasma cells (PCs) [33–35]. This may indicate one potential mechanism by which IL-4 negatively impacts neutralizing antibody titers among low responders. For example, SNPs in IL4R (or other downstream signaling molecules) may result in altered protein conformations that directly impact signal transduction. This could result in the diminished production of other cytokines and growth factors under regulation of IL-4, thus negatively regulating B cell development and the robust production of RV-specific antibodies. Conversely, high responders exhibited positive correlations between memory B cell responses and a variety of pro-inflammatory factors (IP-10, IL-2, MCP-1, TNF-α) (Figure 2A, B). These molecules have been previously shown to govern the differentiation and proliferation of activated B cells [36–39], and it is plausible that high responders harbor a variant within these pathways that enhances signal transduction and leads to robust B cell responses.
Secreted levels of IL-10 also exhibited strikingly different associations with RV-specific humoral response measures between high and low responders. The magnitude of the IgG antibody response was positively associated with secreted levels of IL-10 among high responders, while opposing correlations with IgG antibody titer, neutralizing antibody titer, and antibody avidity were observed among low responders (Figure 2A, D). It is unclear why high responders exhibit positive associations between IL-10 and IgG antibody titer, as previous studies have shown that production of IL-10 following natural rubella infection or vaccination leads to diminished antibody responses [40, 41]. Furthermore, both IL-4 and IL-10 were previously found to be associated with experimental Leishmaniasis vaccine failure in mice [42], suggesting the natural immunosuppressive effects of these pleiotropic cytokines may dictate rubella vaccine response outcomes in certain individuals. Polymorphisms in downstream signaling molecules may also modulate the effects of IL-10 on humoral immunity, and further studies are needed to understand the mechanistic relationship between IL-10 and rubella vaccine response outcomes.
Cytokine and chemokine production exhibited a unique association with antibody response kinetics, as the Baseline levels of several cytokines (IL-10, IL-12p70, IL-13, IL-1β, IL-4) were correlated with the increase in neutralizing antibody titer after vaccination among high responders (Figure 2B, C). Despite robust production of IL-6 and IL-8 following RV stimulation, neutralizing antibody titers were negatively correlated with secreted levels of these factors. This may indicate the presence of a negative feedback loop in immune signaling, with excess production of these inflammatory mediators resulting in diminished immune cell activation.
As our study cohort was predominantly comprised of Caucasian females (99%), we lacked sufficient statistical power to evaluate correlations with race or biological sex (Table 1). Age was not associated with markers of cellular immunity or RV-specific antibody titer in our cohort, although we identified several correlations with BMI. Previous studies have identified associations between higher BMI and elevated levels of pro-inflammatory cytokines and chemokines [43–47], although it should be noted that these studies have largely focused on measurements in serum or adipose tissue. We observed both positive and negative correlations between BMI and the secreted levels of pro-inflammatory cytokines at Baseline (Supplementary Table S4), suggesting that obesity may impact cellular immune responses in the periphery in a complex manner.
To our knowledge, this is the first study to evaluate rubella-specific cytokine and chemokine production on such a comprehensive panel of inflammatory markers; previous reports have focused on subsets of canonical Th1/Th2 cytokines [32, 48, 49]. This is also the first study to investigate the relationship between rubella-specific cytokine/chemokine production and humoral immune response outcomes following a third dose of MMR vaccine – a strategy being used in the control of mumps outbreaks. Our study was limited by several factors inherent in the study design. Study enrollment was restricted to a defined geographic region and focused on young healthy women at the extremes of antibody response, which limits the translatability of our results to the general population, but is relevant to the issue of rubella protection in women of child-bearing age. This could concurrently be viewed as a strength, as we focused on a clinically relevant population exhibiting defined differences in prior humoral immune responses to rubella in order to more readily detect significant associations with markers of cellular immunity following vaccination. For this cohort, we do not currently have any information on genotype to relate inherent differences at the genetic level with biological differences in cytokine or chemokine signaling, although studies of vaccine-induced gene expression are currently ongoing.
In summary, we have extended our understanding of the cellular immune response to RV in a clinically relevant cohort and have identified numerous significant correlations between the secreted levels of cytokines and chemokines and measures of RV-specific humoral immunity. Our findings suggest that inherent differences in host biology or genetics—particularly in relation to IL-4 and IL-10 signaling—influence the humoral immune response to rubella vaccination, but the mechanisms governing these associations remain unclear. Replication studies in larger cohorts will serve to validate our findings from this relatively small sample size and verify the associations that we detected. Furthermore, detailed mechanistic studies of cytokine/chemokine signaling pathways will be required to fully understand the inherent biology that governs RV vaccine responses.
Supplementary Material
Supplementary Figure S1. Associations between Rubella-specific Cytokines and Chemokines after a Third MMR Vaccine Dose. (A) Spearman’s correlation between cytokine/chemokine levels at Baseline. (B) Spearman’s correlation between cytokine/chemokine levels at Day 28. Correlations shown are only for cytokines/chemokines identified as having significant associations with immune response outcomes.
Acknowledgments
The authors would like to thank Caroline L. Vitse for editorial assistance and Diane Grill for contributions to statistical analysis. The research presented here was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award R37AI048793. The conclusions in this report are solely those of the authors and do not represent the official views of the National Institutes of Health.
Footnotes
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Conflicts of Interest
Dr. Poland is the chair of a Safety Evaluation Committee for novel investigational vaccine trials being conducted by Merck Research Laboratories. Dr. Poland offers consultative advice on vaccine development to Merck & Co. Inc., Medicago, Sanofi Pasteur, GlaxoSmithKline, Emergent Biosolutions, Dynavax, Genentech, Eli Lilly and Company, Janssen Global Services LLC, Kentucky Bioprocessing, and Genevant Sciences, Inc. Drs. Poland and Ovsyannikova hold three patents related to measles and vaccinia peptide research. Dr. Kennedy holds a patent on vaccinia peptide research. Dr. Kennedy has received funding from Merck Research Laboratories to study waning immunity to measles and mumps after immunization with the MMR-II® vaccine. Drs. Poland, Kennedy, and Ovsyannikova have received grant funding from ICW Ventures for preclinical studies on a peptide-based COVID-19 vaccine. All other authors declare no competing financial interests. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and was conducted in compliance with Mayo Clinic Conflict of Interest policies.
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Supplementary Materials
Supplementary Figure S1. Associations between Rubella-specific Cytokines and Chemokines after a Third MMR Vaccine Dose. (A) Spearman’s correlation between cytokine/chemokine levels at Baseline. (B) Spearman’s correlation between cytokine/chemokine levels at Day 28. Correlations shown are only for cytokines/chemokines identified as having significant associations with immune response outcomes.