What this is
- This research investigates the expression patterns of () against () across different age groups.
- The study compares pediatric (n=83) and adult (n=198) populations, focusing on how levels change with age.
- Findings reveal significant differences in levels and profiles between children and adults, particularly in relation to biological aging.
Essence
- against are present from childhood through adulthood, but their expression patterns change significantly with age. Notably, certain levels are higher in children, while others increase in adults, reflecting the impact of biological aging.
Key takeaways
- Anti-GPCR levels vary with age, showing higher concentrations of ATR1, ADRA1A, BDKRB1, and ETAR in children compared to adults. In contrast, ACE2, CXCR3, and BDKRB1 levels significantly increase in adults.
- Distinct expression signatures correlate with accelerated aging, particularly in adults with positive Δ age scores, which resemble pediatric profiles. This suggests that biological aging alters expression in a way that reflects chronic inflammation.
- The study identifies a concordant increase in CXCR3- and CXCR3+ T cells with chronological age, indicating a potential link between these and inflammatory processes as individuals age.
Caveats
- The study does not perform functional analyses of or their corresponding , limiting insights into their mechanistic roles. Future research is needed to explore these dynamics further.
- Variability in immune responses may be influenced by factors such as vaccination history and chronic infections, which were not accounted for in this study.
Definitions
- Autoantibodies (Aab): Antibodies produced by the immune system that mistakenly target and react with a person's own tissues.
- G protein-coupled receptors (GPCRs): A large family of membrane proteins that play essential roles in transmitting signals within cells.
- PhenoAge clock: A biomarker used to estimate biological age based on clinical parameters associated with aging and mortality risk.
Simplified
Background
Natural autoantibodies (Aab) function as part of the innate immune system, have homeostatic functions such as clearing apoptotic cells and oxidized proteins [1–3]. Sometimes termed physiological or naturally occurring Aab, they are the most abundant of all Aab and are typically polyreactive and bind with low affinity [4–6]. This contrasts with pathogenic Aab, which are derived through affinity maturation and have high affinity and specificity [7]. Natural Aab contribute to the establishment and maintenance of immune memory in a manner, that is distinct from classical immune reactions and may be used as predictors of future disease in presently healthy individuals [8, 9]. Natural Aab are acquired in early childhood [10] and are also found in healthy pediatric individuals [8, 11–14].
G protein-coupled receptors (GPCRs) comprise the largest and most diverse family of integral membrane proteins that participate in different physiological processes such as the regulation of the nervous and immune system [15, 16]. Besides the endogenous ligands of GPCRs, functional Aab are also able to bind GPCRs, triggering or blocking intracellular signalling pathways, resulting in agonistic or antagonistic effects respectively [17, 18]. However, serum levels of GPCR Aab of healthy individuals is not per definition lower than in that of patients with autoimmune disorders [4, 19]. Besides the physiological roles, the levels of Aab against GPCRs are altered in pathological conditions, and both increased and decreased concentrations correlate with the development or progress of immune-mediated disorders as well as disease specificity [20].
High levels of Aab against the muscarinic acetylcholine receptor M3 as well as those targeting endothelin receptor type A (ETAR) and type 1 angiotensin II receptor (AT1R) have implications in the pathogenesis of rheumatic diseases such as Sjörgen syndrome [21] and systemic sclerosis [20, 22]; AT1R and ETAR determine the extent of vasculopathy in systemic sclerosis [22] and are also associated cardiovascular disease [23–25]. Dysregulation of Renin–Angiotensin–Aldosterone System Aab was suggested as a new mechanism, that can contribute to Parkinson disease progression, as higher serum AT1 and ACE2 Aab were found among patients with Parkinson disease [26].
As mentioned above, elevated titers of Aab against GPCRs have been demonstrated and correlated with the pathogenesis of age-related diseases. It is therefore reasonable to hypothesize, that the concentration of GPCR-targeting Aab increases with age, potentially reflecting the phenomenon of "inflammaging," an acknowledged hallmark of the aging process [27]. Previous studies have investigated age-related changes in natural Aab levels [4, 8, 11, 12, 19, 28, 29]; however, those studies did not specifically focus on Aab directed against GPCRs, nor did they include pediatric populations. Therefore, in this descriptive study, we examine the levels of Aab targeting 14 distinct GPCRs, two intracellular proteins, and two transmembrane receptors across two distinct age groups: pediatric and adult.
Materials and methods
Aab against CGP-receptors & absolute IgG concentration
Human IgG Aab against 14 different GPCRs (ATR1, ATR2, MAS1, BDKRB1, ADRA1A, ADRB2, CXCR3, ETAR, ETBR, M5R, PAR1, PAR2), two proteins (ACE2, ANXA2) and the transmembrane receptors ANXA2R and STAB1 (Autoantibodies dataset and full name in Table S1) were detected from frozen serum using commercial ELISA kits (CellTrend, Luckenwalde, Germany) according to the manufacturer’s instructions as previously described [30].
Absolute IgG concentration was measured via immunoturbidimetry from Central Laboratory of University Duisburg-Essen, Essen, Germany. A sample containing human IgG was suitably diluted and reacted with a specific antibody to form a precipitate, which was measured at 340 nm. The IgG concentration was determined using a calibration curve.
SARS-CoV-2 nucleocapsid IgG
Human IgG antibodies against the SARS-CoV-2 nucleocapsid protein were detected using commercial ELISA kits (EUROIMMUN) according to the manufacturer’s instructions.
PBMCs isolation
As previously described [31], peripheral blood was collected in S-Monovette K3 EDTA tubes (Sarstedt). The samples were diluted 1:1 with PBS/BSA (Gibco) and carefully layered over 15 mL of Ficoll-Paque Plus (GE Healthcare). Following centrifugation at 800 × g for 20 min at room temperature, PBMCs were isolated, washed twice with PBS/BSA, and stored at − 80 °C until further use. For downstream applications, cryopreserved PBMCs were thawed by incubating cryovials in a 37 °C bead bath for 2–3 min. Cells were then washed twice with prewarmed RPMI 1640 medium (Life Technologies) containing 1% penicillin–streptomycin–glutamine (Sigma-Aldrich) and 10% fetal calf serum (PAN-Biotech), and subsequently incubated overnight at 37 °C.
Flow cytometry- characterization T cells
Thawed and rested overnight PBMCs were plated in 96-U-Well plates in RPMI 1640 media (Life Technologies). The PBMCs were stained with optimal concentrations of antibodies for 10 min at room temperature in the dark. Stained cells were washed with PBS/BSA and were immediately acquired on a CytoFLEX flow cytometer (Beckman Coulter) (gating strategy Fig. 2A). Fluorescence minus one controls were used for optimal gating of chemokine receptor CXCR3 (CXCR3) populations. No modification to the compensation matrices was required throughout the study. Detailed listing of the antibody panel (extracellular staining) for the characterization of T cells is presented in supplementary table S2.
Statistics
Statistical analysis was performed using GraphPad Prism (v7) and R (v 4.2.2). Categorical variables are summarized as numbers and frequencies; quantitative variables are reported as median and interquartile range. Normality tests were performed with the Shapiro–Wilk Test. All applied statistical tests are two-sided. Kruskal–Wallis Test and Mann–Whitney-Test were applied to perform comparisons. Correlational analysis was performed with Pearson test. Biological sex was compared using two-tailed Fisher’s exact test. p values below 0.05 were considered significant; only significant p values are reported in the figures. p values were not corrected for multiple testing, as this study was of an exploratory nature.
Calculation of PhenoAge score
Aging clocks are machine learning models designed to identify patterns in molecular features across extensive sample cohorts, which can subsequently be used to estimate the biological age of the sample origin [32]. It has been widely hypothesized that this estimated age can serve as a measure of an individual’s biological age, and that the difference between estimated and actual chronological age, called ‘Δ age’, reflects variation in their past rate of aging [32–34]. These hypotheses have been supported by observations, that positive Δ age, termed age acceleration, is associated with systemic inflammation termed as ``inflammaging``, increased cardiovascular disease risk as well as other age-related diseases [32, 35].
Levine et al. [36] built the PhenoAge clock, and while this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell. This biomarker of aging, among others [37], is able to capture risks for diverse outcomes across multiple tissues and cells and provide insight into important pathways in aging. Furthermore, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. It is based on a linear combination of chronological age and nine clinical parameters associated with mortality risk [32, 36].
To assess biological aging, we calculated PhenoAge using the mathematical equation previously described [38] (fig. S1). The calculation incorporates several clinical biomarkers, including Albumin (g/L), Creatinine (µmol/L), Glucose (mmol/L), C-reactive protein (mg/dL), Lymphocyte percentage, Mean cell volume (fL), Red cell distribution width percentage, Alkaline phosphatase (U/L), and White blood cell count (10^3 cells/µL), along with chronological age. Each parameter was measured using standard clinical laboratory techniques. The resulting PhenoAge score is expressed in years. Participants were then stratified based on Δage into two groups: positive Δage (+ Δage), including those with Δage > 0, and negative Δage (− Δage), including those with Δage < 0. Positive Δage is termed as age acceleration, while negative Δage is termed as age deceleration.
Results
Characterization of the study group
| Children (= 83)N | Adults (= 198)N | valuep | |
|---|---|---|---|
| Age years -median (range) | 11 (1–17) | 59 (18–92) | < 0.0001 |
| Female gender N (%) | 37 (45) | 97 (49) | 0.05155 |
| SARS-CoV-2 unexposed*+ | 43 | 73 | N/A |
| COVID-19 Severity N (%)+# | |||
| Asymptomatic N | 40 | 57 | N/A |
| Mild N | 0 | 68 | N/A |
GPCR Aab prevalence changes with age
Taken together, these results suggest that natural Aab are present from childhood to adult life, however the expression patterns are significantly modified over the course of life with ATR1, ADRA1A, BDKRB1, ETAR Aab being predominant in early life and decreasing over the course of life. Furthermore, we observe a significant increase of ACE2, CXCR3 and BDKRB1 Aab in adulthood and later life. Sex does not seem to affect the concentration of GPCR Aab in our cohort.
GPCR Aab prevalence changes with age.-Age-adjusted analysis of ATR1-, ADRA1A-, ADRB2-, and ETAR-Aab titers.–Age-adjusted analysis of ACE2, CXCR3 and BDKRB1-Aab titers. the data were log transformed, assigning a value of zero for those with a value below the detection limit. The data were analyzed using linear regression.< 0.05 was considered significant A D E G p
Concordant prevalence of CXCR3-Aab and expansion of CXCR3 + T cells with increased chronological age
As CXCR3 and its ligands are considered part of an inflammatory chemokine system [44], the increase in CXCR3-Aab, along with higher frequencies of CXCR3⁺ T cells observed in our cohorts with chronological aging, may reflect a state of chronic inflammation.
Expansion of CXCR3 + T cells with increased chronological age.Flow cytometry gating strategy for identification of activated/migratory T cells. Thawed and rested overnight PBMCs were stained with optimal concentrations of antibodies. Living single lymphocytes were analyzed for expression of CD3, CD4, and CD8. The expression of the activation and migration marker CXCR3 was assessed on CD4 + (orange boxes) and CD8 + (blue boxes) single positive T cells. Fluorescence minus one control (FMO) were used for optimal gating of CXCR3 + population.Analysis of absolute CD4 + and CD8 + T cells regarding age.Analysis of CD4 + CXCR3 + and CD8 + CXCR3 + T cells regarding age. For the analysis of CD4 + CXCR3 + and CD8 + CXCR3 + T cells, the data were log transformed, assigning a value of zero for those with a value below the detection limit. The data were analyzed using linear regression.< 0.05 was considered significant A B C p
GPCR Aab expression-patterns in adults with decelerated biological aging mirror pediatric expression patterns
We further explored patterns of GPCR Aab correlations among the adult cohorts dividing the cohort once again based on positive or negative Δage. It is of interest, that almost the same pattern of strong positive correlations driven from ETBR, MAS1R, M5R, CXCR3, stabilin1 and ANXA2R Aab reappears among the + Δage but not among the -Δag cohort (Fig. 4C-D). Distinct GPCR Aab expression-patterns are present among adults with accelerated biological age. The expression profile of GPCR Aab in -Δage adults exhibits similarity to that observed in the pediatric cohort (Fig. 4 & fig. S6-7).
Signature patterns of GPCRs-Aab driven from accelerated aging.The PhenoAge score was calculated for 123 adult participants. The difference between estimated and actual chronological age, called ‘Δ age’, reflects variation in their past rate of aging.Analysis of anti-GPCR Aab concentrations of both study groups. Scatterplots show line at median. Unpaired data were compared with Mann–Whitney-test.< 0.05 was considered significant, only significant p values are documented in the figures A B p
GPCR-Aab expression in -Δage adults mirrors pediatric patterns. Heatmap analysis of correlations of diverse anti-GPCR Aab among () children, () adults, () adults with negative Δ age, () adults with positive Δ age. Pearson’s r values are represented by varying shades of blue and red A B C D
Discussion
In this descriptive study, we explored the influence of biological and chronological age on concentration of GPCR Aab in a large cohort of pediatric and adult individuals. We found, that natural Aab are present in childhood and adult life, however the expression patterns are significantly altered over the course of life. We observed a significant increase of ACE2, CXCR3 and BDKRB1 Aab levels in adulthood and later life. However, ATR1-, ADRA1A-, BDKRB1-, ETAR-Aab levels are predominant in early life and decrease over the course of life. This observation stands in contrast to the prevailing notion, that concentration of natural Aab increases progressively with age [6, 45].
Age has a significant influence on absolute IgG concentrations. In pediatric populations, IgG levels increase with age, concretely, IgG concentrations in children are lower in infancy and gradually rise, often not reaching adult levels until adolescence or later [46, 47]. In adults, age-related changes persist, with some studies showing variations in IgG and its subclasses across age groups [48]. Furthermore, IgG accumulates in various tissues of mice and humans during aging [49]. It is therefore noteworthy, that in our study children exhibit higher concentrations of specific anti-GPCR IgG Aab compared to adults, although we found no differences in absolute IgG concentration between adult and pediatric cohorts. Higher anti-GPCR IgG Aab concentrations in children may reflect the dynamic maturation of the immune system during development. This period is characterized by increased B-cell and plasma cell activity, as well as heightened immune responsiveness to environmental antigens, infections, and vaccinations [50, 51].
Taken the above into consideration, we postulate, that alterations in GPCR expression, whether in surface density, activation threshold, or inhibition dynamics [4, 6], may vary across the human lifespan, contributing to the observed differences in anti-GPCR Aab profiles and immune function across lifespan. Similarly, the properties of their corresponding Aab, including binding affinity or structural conformation, may also undergo age-related changes [52].
No significant association was observed between Aab levels and biological sex. Consistent with our findings, Shome et al. reported no significant sex-related bias in serum Aab expression patterns [45]. However, other studies have described a slight influence of biological sex on the expression of natural Aab [4, 6, 53].
In addition to chronological age, we investigated the influence of biological age and age acceleration on Aab levels. To estimate biological age within the adult cohort, we employed the PhenoAge metric. Our findings reveal distinct GPCR Aab signatures associated with accelerated aging, which are clearly distinguishable from the developmental expression patterns described above. Overall, we identify two distinct Aab expression trajectories: one associated with physiological development from childhood to adulthood, and another linked to accelerated aging in adults with a positive Δ age score.
When accounting for chronological age, moderate to strong positive correlations among various GPCR Aab were observed across both cohorts, with this trend being more pronounced in the adult cohort. Of note is, that we observed near perfectly linear correlations between several Aab, most notably between ETAR and AT1R Aab. This strong association may be attributable to potential cross-reactivity. However, current molecular and immunological evidence does not support broad cross-reactivity based solely on short linear motifs. Literature indicates, that Aab against AT1R and ETAR are typically directed against conformational epitopes, often within the second extracellular loop of these GPCRs, rather than short linear motifs alone [54, 55]. Therefore, cross-reactivity between AT1R and ETAR Aab based solely on short linear motifs is unlikely, as antibody specificity is determined by conformational epitopes within the extracellular domains of these receptors [56, 57].
A similar pattern of strong positive correlations re-emerged specifically among adults with accelerated biological aging (+ Δage), but was absent in the -Δage group. Importantly, the GPCR Aab expression patterns in -Δage adults more closely resembled those observed in the pediatric cohort. Cabral and colleagues identified correlational relationships among anti-GPCR-Aab targeting structurally and functionally related molecules, such as vascular, neuronal or chemokine receptors, describing these networks, as a natural component of the immune system homeostasis [4]. This balance may become dysregulated, due to inflammation, cell damage or other stimuli [19, 58]. Age acceleration is associated with systemic inflammation termed as ``inflammaging``, which is a recognized hallmark of the aging process [27], increased cardiovascular disease risk as well as other age-related diseases [32, 35–37]. According to our findings, we hypothesize, that this pronounced Aab correlational expression pattern observed in adults with accelerated biological aging may reflect this state of chronic inflammation.
Among other Aab, we found significantly increasing titers of CXCR3-Aab with age. As CXCR3 and its ligands are considered part of an inflammatory chemokine system [44], the increase in CXCR3-Aab, along with higher frequencies of CXCR3⁺ T cells observed in our adult cohort, may reflect an inflammatory state. Upregulation of the CXCR3 receptor has been associated with neuronal axonal damage secondary to inflammatory response of T cells a) during or after viral infection [43, 59] as well as b) in the frame of autoimmunity [60] mainly in multiple sclerosis patients [39, 61]. Accumulating data indicate the crucial role of CXCR3 receptor in directing the migration of inflammatory CD4 + but mainly CD8 + T-cells in an CXCR3-dependent manner [41–43].
Our study has limitations. Thus, we were unable to perform a functional analysis of GPCRs and their corresponding Aab. Future longitudinal investigations employing concurrent functional analyses of GPCRs and their corresponding Aab across pediatric and geriatric cohorts are warranted to elucidate the mechanistic basis of this functional switch. Taking into consideration the crucial role natural Aab play in maintaining the body homeostasis, the variation in their concentration at different age intervals may partially account for the immunological and pathological changes that occur in the elderly [1, 18]. Immune architecture is shaped by a range of factors, including vaccination history, chronic infections, and systemic inflammatory status. Accordingly, elucidating the influence of these variables, and their potential reciprocal interactions on GPCR AAb concentrations, remains of interest. Future studies will be critical for assessing the prognostic and therapeutic potential of these receptors as pharmacological targets in the contexts of senescence, inflammaging, and pathophysiology of autoimmunity.
Conclusions
Our findings indicate that anti-GPCR Aab are present from childhood through adulthood; however, their expression patterns undergo significant changes over the course of life. We also assessed the impact of age acceleration, defined by a positive Δ age score based on the PhenoAge clock, on Aab levels. Distinct GPCR Aab expression signatures correlated with accelerated aging, differing from patterns seen in the pediatric and adult cohorts. Furthermore, we found a concordant prevalence of CXCR3-Aab and expansion of CXCR3 + T cells with increased chronological age. Because systemic chronic inflammation is a known driver of accelerated aging, we hypothesize that the altered Aab expression profiles observed in biologically older adults may reflect this underlying inflammatory state.
Supplementary Information
Supplementary Material 1.