There is considerable heterogeneity in immunological parameters between individuals, but its sources are largely unknown. To assess the relative contribution of heritable versus non-heritable factors, we have performed a systems-level analysis of 210 healthy twins between 8 and 82 years of age. We measured 204 different parameters, including cell population frequencies, cytokine responses, and serum proteins, and found that 77% of these are dominated (>50% of variance) and 58% almost completely determined (>80% of variance) by non-heritable influences. In addition, some of these parameters become more variable with age, suggesting the cumulative influence of environmental exposure. Similarly, the serological responses to seasonal influenza vaccination are also determined largely by non-heritable factors, likely due to repeated exposure to different strains. Lastly, in MZ twins discordant for cytomegalovirus infection, more than half of all parameters are affected. These results highlight the largely reactive and adaptive nature of the immune system in healthy individuals.

The study of monozygotic (MZ) and dizygotic (DZ) twin pairs has provided a powerful means for separating heritable from non-heritable influences on measured traits for almost 100 years (). Such studies have been used to study autoimmune diseases, vaccine responses (), serum cytokines (), or the frequencies of major immune cell populations (). Most of these studies have found that both heritable and non-heritable factors contribute to the resulting phenotype. Recent advances in technology now allow much more comprehensive surveys to be conducted across the many different components of the immune system, and thus we performed a very broad “systems-level” study in which we measured 51 serum cytokines, chemokines, and growth factors; the frequencies of 95 different immune cell subsets; and cellular responses to cytokine stimulation ( Figure 1 A). Our results show that these functional units of immunity vary across individuals primarily as a consequence of non-heritable factors, with a generally limited influence of heritable ones. This indicates that the immune system of healthy individuals is very much shaped by the environment and most likely by the many different microbes that an individual encounters in their lifetime.

(C) Heritability estimates of 43 serum proteins as determined by a fluorescent bead assay. See also Table S4 . Error bars represent 95% confidence intervals for the heritability estimate. Gray area is heritability <0.2, our detection limit.

(B) Summary of all heritability estimates for 72 immune cell population frequencies as determined by flow (2009) and mass cytometry (2010–2011) ( Experimental Procedures , section 3). See also Table S3

In summary, our findings strongly suggest that a healthy human immune system adapts to non-heritable influences such as pathogens, nutritional factors, and more and that this overshadows the influences of most, although not all, heritable factors.

Finally, we immunized all of the subjects with seasonal flu vaccines in the year of participation (2009, 2010, or 2011) and assessed antibody responses using a standard hemagglutination inhibition (HAI) assay ( Experimental Procedures , “Hemagglutination Inhibition Assays”). We were surprised to find no detectable contribution from heritable factors on any of these vaccine responses ( Table 1 ). As pre-existing antibodies are known to influence flu vaccine responses (), we excluded subjects with a pre-vaccination titer above 40 but were still unable to find any heritable influences ( Table 1 ). Though preliminary due to a small sample size, this result suggests that responses to seasonal flu vaccines in healthy adults (median age, 44 years) are dominantly influenced by non-heritable factors, likely due to multiple previous vaccinations and/or infections involving this pathogen ( Table 1 ).

Table of published heritability estimates for vaccine responses to various vaccines as well as the seasonal influenza vaccine responses analyzed in this study with or without the removal of subjects with a pre-vaccine antibody titer ≥40.

As we postulate that microbial exposure is a likely driver of immune variation with age, a particularly interesting example is cytomegalovirus (CMV), a lifelong viral infection that has striking effects on the immune phenotypes of both humans and rhesus macaques (). In our twin cohort, 16 MZ pairs were discordant for CMV seropositivity, and we compared their twin-twin correlations for all measurements to those of 26 CMV concordant (negative) MZ pairs. Here, we found that the CMV discordant MZ twins showed greatly reduced correlations for many immune cell frequencies such as effector CD8and gamma-delta T cells ( Figure 5 A), as compared to CMV-negative MZ twins. The same was true for cell signaling responses, especially in response to IL-10 and IL-6 stimulation ( Figure 5 B), as well as the concentrations of these same cytokines in serum ( Figure 5 C). In general, the influence of CMV was very broad, affecting 119 of all 204 measurements (58%) dispersed throughout the immune network ( Figure 5 D) and illustrating how at least one type of microbial exposure can dramatically modulate the overall immune profile of healthy individuals.

(D) 58% of all 126 nodes in the immune network model with reduced correlations in CMV pos/neg as compared to CMV neg/neg MZ twin pairs.

(B and C) (B) Twin-twin correlations (Spearman’s rank) for cell signaling responses to cytokine stimulation and (C) serum protein measurements between CMV neg/neg and CMV pos/neg MZ twin pairs.

As a major source of non-heritable influence is likely to be environmental, particularly microbial exposure, we reasoned that such influences would increase with time. To this end, we compared twin-twin correlations for all immune measurements between the oldest (>60 years; median, 72 years) and the youngest (<20 years; median, 13 years) MZ pairs in our cohort. Here, we also note that twins in the younger (<20 years) cohort are in most cases living together, whereas the older (>60 years) twins have lived apart for decades, so concordance can also be a result of either shared environment and/or shared exposure, in addition to genetic similarity. For several cell population frequencies, we found much reduced correlations with age ( Figure 4 A). In the most striking example, the frequency of Tregs between the youngest MZ twins correlated very strongly at 0.78 but was highly uncorrelated at 0.24 between the oldest MZ twin pairs ( Figure 4 A). Similarly, several serum proteins showed remarkably reduced correlations between older as compared to younger MZ twins ( Figure 4 B). In particular, the chemokine CXCL10/IP10 showed a strong correlation (0.79) between the youngest MZ twins but was greatly reduced (0.18) in the older MZ twins ( Figure 4 B). Similar patterns were found for many cell signaling responses (data not shown), suggesting that this immune divergence between genetically identical twins with age is a common phenomenon, consistent with a major role for environmental exposure in driving variation, although some epigenetic changes could also contribute ().

Our analysis also allowed us to analyze the interrelationships between the different components of the immune system. To construct such a network model, we calculated a precision matrix derived from a Spearman correlation matrix (). A matrix of this type captures partial correlations between variables and avoids spurious, indirect interactions that might occur in standard correlation analyses. By penalizing non-zero entries in this matrix (), we could pursue what is referred to as a sparse (rather than dense) network model, making it more interpretable. After removing unconnected nodes and validating the edges by a permutation test ( Experimental Procedures , “Identification of Pairwise Dependencies between Measurements and the Creation of an Immune Network Model”), this model consists of 126 nodes and 142 edges ( Figure 3 A and Table S6 ). An interactive version is available online ( http://www.brodinlab.com/twins.html ). We found that heritable nodes (yellow) were generally connected to non-heritable nodes (purple) throughout the network ( Figure 3 A). One example shows how the weakly heritable cytokine IL-10 and CD161CD45RAregulatory T cells are connected to the strongly heritable frequency of naive CD4T cells ( Figure 3 B). We found that all hubs in the network were dominated by non-heritable influences, like the network as a whole, showing that heritable factors are not isolated by themselves but are buffered by connected non-heritable ones ( Figure 3 A and 3B). This may explain why the many gene polymorphisms found (for example, CTLA4 []) outside of the HLA locus that are associated with immune-mediated disease only explain a small fraction of the total disease risk ().

(A) Undirected network model of the healthy human immune system showing 126 nodes (measurements), connected by 142 undirected edges illustrating conditional measurement dependencies. Nodes are colored by their estimated heritability and sized by their number of edges.

Taken together, these results show that variation in blood cell frequencies and functions and soluble factors is largely driven by non-heritable factors, with 58% of all measurements having <20% of their total variance explained by heritable influences ( Figure 2 B). There was no relationship between the absolute degree of measurement variability in the cohort and estimated heritability ( Figure S3 ), and we could also rule out any underestimation of heritability due to the skewed ratio of MZ/DZ twin pairs in our cohort by a resampling test. In brief, by creating 1,000 synthetic data sets with uniform heritability and the same MZ/DZ ratio as in our cohort, we estimated heritability and found that none of the 1,000 data sets ever had >40% of measurements with an estimated heritability < 0.2 (p < 0.001) ( Figure 2 C), thus suggesting that the low heritability estimates obtained are not a result of study design or overall measurement variation in the cohort.

For each measurement coefficients of variance (CV) in the complete twin cohort is shown against the corresponding corrected estimates of heritability for each measurement.

Because these serum proteins often regulate immune cells, we assessed the responses of eight different cell populations stimulated in vitro with seven different cytokines for the phosphorylation of three important transcription factors, STAT1, 3, and 5, using phospho-specific antibodies in flow cytometry (). We performed a total of 192 different measurements but focused on the 24 baseline measurements and the 65 strongest induced responses ( Experimental Procedures , “Immune Cell Signaling Experiments”). Baseline measurements were generally driven by non-heritable factors, with possible minor contributions from heritable factors ( Figure 2 A). The important homeostatic cytokines IL-2 and IL-7, known to stimulate the proliferation and differentiation of T cells, were found to induce STAT5 phosphorylation in both CD4and CD8T cell populations, and these responses were highly heritable ( Figure 2 A and Table S5 ). In contrast, most signaling responses such as interferon-induced STAT1 phosphorylation and, in particular, the IL-6-, IL-21-, and regulatory IL-10-induced phosphorylation of STAT3, were dominated by non-heritable influences ( Figure 2 A and Table S5 ). In total, 69% of all signaling responses had no detectable heritable influence (e.g., <20%) ( Figure 2 A and Table S5 ). This lack of heritability was not related to the strength of responses or explained by a bias toward weak and variable responses ( Figure S2 ).

For each measurement above the (1.5 fold) background threshold, absolute fold-change (stimulated/unstimulated) of signaling responses upon cytokine stimulation is shown against the level of estimated heritability.

(C) The maximum number of measurements with heritability <0.2 across 1,000 synthetic data sets with the same MZ/DZ ratio as in our twin cohort is <40%, significantly less than our results of 58% of measurements with heritability <20% (gray bar). p < 0.001.

(A) Heritability estimates for immune cell signaling states upon stimulation with the indicated cytokines. Only unstimulated controls and induced responses >1.5-fold are shown. See also Table S5

Serum cytokines and chemokines also have important functions as immune mediators and biomarkers of disease (), and thus we measured 51 serum proteins but eliminated eight that were often at or below the limits of detection ( Experimental Procedures , “Serum Protein Quantification”). This left 24 cytokines, 10 chemokines, 6 growth factors, and 3 other serum proteins for which we estimated the influences of heritable and non-heritable factors ( Figure 1 C and Table S4 ). Some cytokines were particularly heritable, such as IL-12p40 ( Figure 1 C and Table S4 ). Interestingly, variants in the IL12B gene that contribute to the IL-12p40 protein have been associated with immune-mediated diseases such as psoriasis () and asthma (). In the latter condition, the susceptibility locus was also associated with a reduced serum concentration of IL-12p40 (). For many other measurements, such as IL-10 and a group of chemokines, the heritable influence was low ( Figure 1 C and Table S4 ).

Although it is well known that the frequencies of different types of immune cells in blood often vary widely between individuals, in most cases it is not known how much of this can be attributed to heritable or non-heritable factors, respectively. To address this question, we used antibodies against cell surface markers to quantify 95 different cell subset frequencies but used the 72 most non-redundant ones and estimated the influence of heritable and non-heritable factors on their variation ( Experimental Procedures , “Immune Cell Phenotyping by Mass Cytometry and Flow Cytometry”). Among these, a few had very strong influences from heritable factors, especially naive, CD27, and central memory CD4T cells ( Figure 1 B and Table S3 ), but for most, non-heritable influences were clearly dominant. In fact, for 61% of all cell populations, the influence of heritable factors was undetectable (<20% of the total variation) ( Figure 1 B and Table S3 ). This was true of both adaptive (T and B cells) and innate cell types (granulocytes, monocytes, and NK-cells).

We first ran a simulation experiment to verify that our cohort size of 210 twins (78 MZ and 27 DZ pairs) would be enough to test our hypothesis that most immunological traits are explained more by non-heritable than by heritable influences. We found this to be the case, and we estimate 20% heritability to be our detection limit, under which we cannot distinguish small heritable influences from zero ( Figure S1 ).

One thousand randomly generated data sets with 78 MZ twin pairs and 27 DZ twin pairs in each and for every level of known heritability between 0 and 1 at increments of 0.01. False-negative rate is the number of simulations yielding h = 0, for each true value of h. Grey area marks h ≤ 0.2, the estimated detection limit.

Heritability for each parameter was estimated by comparing observed MZ and DZ covariance matrices to the expected values based on a structural equation model that partitioned the observed variance into three components: heritable (A), shared (C), and unique (E) non-heritable factors. This model is based on the assumptions that: (1) heritable factors correlate perfectly between MZ twins (rMZ = 1) but only to 50% between DZ twins (rDZ = 0.5) and (2) that shared non-heritable influences are equally similar (rMZ = rDZ) between MZ and DZ twin pairs ( Experimental Procedures , “Structural Equation Modeling to Estimate Heritable and Non-Heritable Influences”). For each measurement, we subtracted the technical variance estimate from the e-component prior to normalization to correct for noise ( Experimental Procedures , “Correction of Model Estimates for Technical Variability”). We also corrected all measurements for the effects of age () and gender () by regressing out such effects and using only residual variance for estimating heritability. Finally, we performed jackknife bootstrapping tests to obtain 95% confidence intervals ( Experimental Procedures , “Structural Equation Modeling to Estimate Heritable and Non-Heritable Influences”). Importantly, as our model estimates heritability by comparing MZ and DZ twins, heritable influences include genomic and shared epigenetic traits (), and non-heritable influences include environmental factors and stochastic epigenetic changes ().

The ageing immune system: is it ever too old to become young again?.

Our study cohort was recruited from the Twin Research Registry at SRI International () in the years 2009–2011, with demographic data detailed in Table S1 (available online). The subjects were all apparently healthy, without any symptoms of disease ( Experimental Procedures , “Twin Cohort”). To minimize biological variability, the time between blood sampling of each twin in a pair was kept to a minimum ( Experimental Procedures , “Blood Sampling, PBMC Preparation, and Zygosity Testing”). Immunological assays were performed by the Human Immune Monitoring Center, where assays are continually benchmarked to minimize technical variability ( http://iti.stanford.edu/himc/ ) (). However, some technical variability is inevitable, and thus we corrected for this in all of our models. We did this by analyzing aliquots of the same control sample many (>17) times to estimate the technical variance and subtracted this from our estimates of heritability ( Experimental Procedures , “Correction of Model Estimates for Technical Variability”). We also analyzed longitudinal samples in an unrelated cohort over 2–5 consecutive, yearly samplings and found that the variation was largely due to technical variability ( Table S2 ). A total of 204 different immune measurements were included in our analyses.

Discussion

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et al. Naive antibody gene-segment frequencies are heritable and unaltered by chronic lymphocyte ablation. + and CD8+ subsets of T cells, which share the same antigen receptor apparatus. The vertebrate immune system consist of thousands of different components, and the application of systems biology () holds great promise as a way to understand the interactions between these during immune health and disease. Here, we combine a classical twin study approach with the most recent advances in immune monitoring technologies to assess the balance between heritable and non-heritable influences on the functional units of the immune system, namely serum proteins and cell populations. In every category, we find that non-heritable influences dominantly influence 77% of all measurements (>50% of variance) and almost exclusively drive 58% of the measurements (>80% of the variance). Because most measurements made in this study focus on the adaptive immune system, partly due to the availability of reagents, one possibility is that these low levels of heritability are related to the stochastic nature of antigen receptor recombination. Indeed, previous work has shown that there are significant differences in the immunoglobulin-sequence repertoires of MZ twins (). But this is unlikely, as we find low heritability estimates also for many innate immune cell frequencies (NK cells, monocytes, and granulocytes [ Figure 1 B]) and differences within and between CD4and CD8subsets of T cells, which share the same antigen receptor apparatus.

Although this study is not powered to completely rule out all heritable influences on any of the measurements made, the overall dominance of non-heritable factors is independent of this. The low estimates of heritable influences are also not explained by technical noise, as this is rigorously corrected for in our models ( Figure S6 A). We are also able to rule out that measurement variability over time is an important source of bias ( Figure S6 B). Therefore, the low heritability estimates for the majority of measurements cannot be explained by either technical noise or biological variability over time.

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Spector T.D. A twin approach to unraveling epigenetics. Given the concordance rates for common autoimmune diseases between 25% and 50% () and the many studies finding associations between specific genetic variants and immunological traits and disorders, we were surprised to find such a dominance by non-heritable factors on these functional units of the immune system. Several large population studies have associated specific loci with white blood cell counts, showing some heritable influence though the amount of variation explained is typically low (). A recent study by Orrù et al. estimated the heritability of 272 immune cell traits from a non-twin cohort of healthy individuals on the island of Sardinia. They found that 220 of these (∼81%) had an estimated heritability lower than 50% (), which is comparable to our results. Although all the most heritable subpopulations identified by Orrù et al. expressed the marker CD39, which was not analyzed in our study, a number of the cell populations gave quite similar values, although others were different, such as NKT cells and central memory CD8T cells ( Table S7 ), possibly reflecting different environments () or a more diverse Palo Alto cohort versus a less diverse Sardinian one.

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et al. Selective predisposition to bacterial infections in IRAK-4-deficient children: IRAK-4-dependent TLRs are otherwise redundant in protective immunity. It is important to note that the twins in our cohort are healthy and without any known immunological deficiencies. Interestingly, two serious immunodeficiency syndromes are caused by defects in the genes IRAK-4 and MyD88 and are associated with invasive bacterial infections due to defects in TLR or IL-1R signaling (). Despite being associated with severe and often lethal infections in young children, both of these conditions improve significantly with age, starting in late childhood (). Although this could be explained by developmental immaturity, an alternative explanation for the improvement with age could be that these children’s immune systems become more capable with greater environmental exposure.

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Davis M.M. Virus-specific CD4(+) memory-phenotype T cells are abundant in unexposed adults. Before the advent of childhood vaccines, antibiotics, and improvements in human hygiene, almost half of all children younger than 5 years of age died of infectious diseases. Casanova and colleagues have proposed a genetic theory of infectious disease to account for the interindividual differences in susceptibility (). Our data and the vaccine studies cited here suggest that such genetic predisposition would be most pronounced in young children but that, later on, the adaptive nature of the immune system is able to overcome many defects. This is similar to the hypothesis proposed by Alcais and colleagues to explain the discrepancies in genetic lesions underlying the susceptibility to primary and secondary infections, respectively (). Adaptations of the immune system with time could be the result of well-known immune mechanisms, such as specific antibodies and T cells or cross-reactive immunity (), or some as yet to be defined maturational process. We would also speculate that the immune system may have feedback mechanisms that allow it to skew its mix of cell types and functional properties in order to compensate for a given individual’s particular mix of gene polymorphisms and microbial exposures.

In summary, we find that, in an examination of many of the component parts of the immune system, as well as some response metrics, much of the considerable variation in human beings is driven by non-heritable influences. This variation increases with age and is likely due in large part to exposure to pathogens and other microbes, as we see for CMV discordant MZ twins and in the responses to influenza vaccination. Lastly, we expect that other complex systems in higher organisms, such as the nervous system, will also show this pronounced influence of non-heritable factors, as there is also a need (and ability) of such systems to adapt to environmental stimuli.