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Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses

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Abstract

The immune response to pathogens varies substantially among people. Whereas both genetic and nongenetic factors contribute to interperson variation, their relative contributions and potential predictive power have remained largely unknown. By systematically correlating host factors in 534 healthy volunteers, including baseline immunological parameters and molecular profiles (genome, metabolome and gut microbiome), with cytokine production after stimulation with 20 pathogens, we identified distinct patterns of co-regulation. Among the 91 different cytokine–stimulus pairs, 11 categories of host factors together explained up to 67% of interindividual variation in cytokine production induced by stimulation. A computational model based on genetic data predicted the genetic component of stimulus-induced cytokine production (correlation 0.28–0.89), and nongenetic factors influenced cytokine production as well.

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Fig. 1: Analysis of baseline immunological parameters and molecular profiling shows that baseline parameters are intercorrelated.
Fig. 2: Contribution of baseline immunological parameters and multi-omics to cytokine variation.
Fig. 3: Examples of baseline molecules differentially associated with cytokine responses.
Fig. 4: Cumulative contribution of multiple baseline traits to the variation in stimulated cytokine production.
Fig. 5: Integrating gene expression profiles and cytokine production in response to C. albicans.
Fig. 6: Stimulated cytokine production correlates with genetic risk score for autoimmune diseases.
Fig. 7: Cytokine production in response to pathogens can be predicted on the basis of genetics and baseline immune profiles.
Fig. 8: Prediction using the genetic model in an independent dataset shows that some cytokine–stimulus pairs can be predicted successfully.

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  • 18 June 2018

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Acknowledgements

We thank all of the volunteers in the 500FG and GoNL cohorts for their participation. We thank T. A. Wassenaar and L. Steenhuis of the Hanze University of Applied Science for providing input on the project and helping with the analyses. We thank K. Mc Intyre for editing the text. We thank the EAGLE eczema consortium for making their summary statistics publicly available. The HFGP is supported by a European Research Council (ERC) Consolidator grant (ERC 310372). This study was further supported by an IN-CONTROL CVON grant (CVON2012-03) and a Netherlands Organization for Scientific Research (NWO) Spinoza prize (NWO SPI 94-212) to M.G.N.; an ERC advanced grant (FP/2007-2013/ERC grant 2012-322698) and an NWO Spinoza prize (NWO SPI 92-266) to C.W.; a European Union Seventh Framework Programme grant (EU FP7) TANDEM project (HEALTH-F3-2012-305279) to C.W. and V.K.; and an NWO VENI grant (NWO 863.13.011) and an ZonMw-OffRoad-91215206 grant to Y.L. M.O. was supported by an NWO VENI grant (016.176.006). R.J.X. was supported by National Institutes of Health (NIH) grants DK43351, AT009708 and AI137325.

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Y.L., C.W. and M.G.N. designed the study. M.O., S.P.S., M.J., R.T.N.-M., H.J.P.M.K., I.J., R.J.X. and L.A.B.J. performed the experiments and processed the data. U.V. collected and preprocessed public summary statistics. O.B.B. performed statistical analysis with assistance from R.A.-G., S.S., U.V. and L.F.; O.B.B., M.Z., Y.L., S.W., V.K., M.G.N., and C.W. interpreted the data. Y.L., C.W., M.G.N. and O.B.B. wrote the manuscript with input from all authors.

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Correspondence to Cisca Wijmenga or Mihai G. Netea or Yang Li.

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Integrated Supplementary Information

Supplementary Figure 1 Overview of variation in data in the 500FG cohort.

(a) Distributions of age in 500FG shows that there is a large proportion of young people. (b) Distribution in BMI in 500FG shows most people are within a healthy BMI range. (c) Sample variance after age, gender and season correction of gene expression quantified by RNAseq (n=89). The top 75 most varying genes are shown (d) Sample variance of cell counts after correction for age, gender and season effects shows effector T-cell populations are among the most variable between individuals (n=472). (e) Sample variance of baseline molecular components and immune parameters (n=489).

Supplementary Figure 2 Validation and replication experiments.

(a) HDL cholesterol decreases cytokine production in response to A. fumigatus. Log2 cytokine expression relative to RPMI in PBMCs cultured using LPDP and LPDP+HDL shows a decrease in expression upon stimulation with A. fumigatus. Experiments were performed in 6 healthy individuals. (b) Spearman correlations after age and gender correction between cytokine production and circulating IL-18BP concentration in 500FG (left, n=489) and a subset of individuals from 300-OB filtered on BMI < 28 (right, n=51). (c) Cytokine production of TNF-α and IL-6 by PBMC derived macrophages in response to E. coli and S. aureus is lowered in cells cultured on a medium containing acetate but not for C. albicans. Y-axis shows log2 fold changes in cytokine level compared to the non-acetate condition in macrophages cultured on a medium with acetate and stimulated with C. albicans, S. Aureus and E. coli in 6 healthy Dutch donors. Blue indicates TNFA. Red indicates IL-6.

Supplementary Figure 3 Baseline molecular components are differentially associated with cytokine responses to pathogens.

Spearman correlations between stimulated cytokine production (after correction for age- and gender-effects) and the top 5 (when taking effect size into account) most-selected metabolites (total cholesterol level in HDL3, glutamine, free cholesterol and α-1 acid glycoprotein) and mediators (α-1 antitrepsin, IL-18BP, adiponecting, resistin and leptin) show differential effects between monocyte- and lymphocyte-derived cytokine measurements as well as across multiple stimulations. Sample size was n = 377 for the metabolites and n = 489 for the modulators, with each sample representing an individual.

Supplementary Figure 4 Estimation of the unique contributions of multiple baseline immunological parameters to the variation in stimulated cytokine production.

Adjusted R2 between a full and multiple-reduced multivariate linear models (MVLM) giving an estimation unique of the effect. Each colored bar represents how much additional variation (on top of the preceding colors) the MVLM for that category explains when conditioned on the other 9 MVLMs. The combined dataset consisted of 266 samples. Each sample represents an individual. Gene expression was not included in this analysis because of the relatively small sample size of the RNA-seq experiment after overlapping with the other datasets (n = 69). X-axis denotes adjusted R2 values. Y-axis denotes different cytokine-stimulation pairs.

Supplementary Figure 5 Correlations between stimulated cytokine production and polygenic risk scores for immune disease.

(a) Spearman correlations between PRS and stimulated cytokine production remain largely stable across multiple GWAS thresholds. Spearman correlations between PRS and cytokine measurements (n = 430) show similar direction and effect size across the multiple GWAS thresholds (indicated by the color of the boxplot) used for calculating PRS for most immune diseases tested. (b) Distribution of permuted mean correlations between disease risk in monocyte derived cytokines (left panel) and lymphocyte derived cytokines (right panel) shows the measured mean estimate (red arrow) differs from the permuted distribution for all diseases showing significant changes.

Supplementary Figure 6 Correlations between predicted and measured stimulated cytokine level for multiple tissue types.

(a) Correlations between predicted and measured cytokine level for each of the 10 cross validation folds. Predicted using multivariate linear models (MVLM) trained by Elastic Net (n = 392). (b) Correlations between predicted and measured cytokine level for each of the 10 cross validation folds. Predicted using multivariate linear models MVLMs trained by RR-blup (n = 392). (C) Correlations between predicted and measured cytokine level for each of the 10 cross validation folds. Predicted using MVLMs trained by PLS (n = 392). (e) Correlations of predicted and measured cytokine level of MVLMs trained with all categories and gene expression shows model large variation (n=69).

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Bakker, O.B., Aguirre-Gamboa, R., Sanna, S. et al. Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses. Nat Immunol 19, 776–786 (2018). https://doi.org/10.1038/s41590-018-0121-3

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