Variation in the human immune system is largely driven by non-heritable influences

Abstract

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.

Copyright © 2015 Elsevier Inc. All rights reserved.

Figures

Figure 1. Systems-level analysis of healthy human twins

(A) Overview of data collected covering the functional units of the immune system, the cells and proteins in circulation. (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. (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. Grey area is heritability

Figure 2. Heritable factors explain only a fraction of the variation for most immune measurements

(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. (B) The overall distribution of heritability for all 204 measurements. (C) The maximum number of measurements with heritability

Figure 3. Network of dependencies between immune measurements

(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.(B) Sub-network exemplifying direct relationships between heritable and non-heritable nodes. Solid edges represent positive relationships and dashed edges represent negative relationships. The edge weight represents the strength of relationships. See also Table S6.

Figure 4. Increased variability in the immune system with age

(A) Twin-twin correlations (Spearman’s rank) for all cell frequencies within the youngest MZ twin pairs (≤ 20yrs, median: 13.5, n=25 pairs), and the oldest MZ twin pairs (≥ 60yrs, median: 72yrs, n=16). (B) Twin-twin correlations (Spearman’s rank) for all serum protein concentrations within the youngest MZ twin pairs (≤ 20yrs, median: 13yrs, n=26), and the oldest MZ twin pairs (≥ 60yrs, median: 73yrs, n=13).

Figure 5. Broad non-heritable influences in the healthy immune system

(A) Twin-twin correlations (Spearman’s rank) for all cell frequency measurements made in CMV concordant negative (neg./neg.) MZ twin pairs (n=26 pairs), and CMV discordant (pos./neg.) MZ twin pairs (n=16 pairs). (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. (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.