Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome

Abstract

Despite a growing body of evidence, the role of the gut microbiome in cardiovascular diseases is still unclear. Here, we present a systems-genome-wide and metagenome-wide association study on plasma concentrations of 92 cardiovascular-disease-related proteins in the population cohort LifeLines-DEEP. We identified genetic components for 73 proteins and microbial associations for 41 proteins, of which 31 were associated to both. The genetic and microbial factors identified mostly exert additive effects and collectively explain up to 76.6% of inter-individual variation (17.5% on average). Genetics contribute most to concentrations of immune-related proteins, while the gut microbiome contributes most to proteins involved in metabolism and intestinal health. We found several host–microbe interactions that impact proteins involved in epithelial function, lipid metabolism, and central nervous system function. This study provides important evidence for a joint genetic and microbial effect in cardiovascular disease and provides directions for future applications in personalized medicine.

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Fig. 1: Study analysis workflow.
Fig. 2: Proportion of inter-individual variation explained by genetic and microbial factors.
Fig. 3: Association of FUT2, Ep-CAM, and Blautia.
Fig. 4: Network of protein co-abundance and PPIs.
Fig. 5: Genetic–microbiome interaction for CNTN1.

Data availability

The LifeLines-DEEP metagenomics sequencing data are available at the European Genome-phenome Archive under accession EGAS00001001704.

Change history

  • 19 October 2018

    In the version of this paper originally published, there was a typographical error. In the Discussion, the sentence “In line with this, Ep-CAM-deficient mice exhibited increased intestinal permeability and decreased ion transport60, which may contribute to CVD susceptibility risk59” originally read iron instead of ion transport. This error has been corrected in the HTML, PDF and print versions of the article.

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Acknowledgements

We dedicate this paper to the memory of Marten H. Hofker, who was a key person in developing the concept of this study. We thank participants and staff of the LifeLines-DEEP cohort for their collaboration. The study was approved by the UMCG review board, ref. M12.113965. We thank J. Dekens, M. Platteel, A. Maatman, and J. Arends for management and technical support and Jackie Senior and Kate Mc Intyre for English editing. We thank A. V. Vila and R. K. Weersma for helpful discussions. This project was funded by the Netherlands Heart Foundation (IN-CONTROL CVON grant 2012-03 to M.H.H., M.G.N., F.K., A.Z., and J.F. and CVON-DOSIS grant 2014-40 to R.A.D.B.); by Top Institute Food and Nutrition, Wageningen, The Netherlands (TiFN GH001 to C.W.); by the Netherlands Organization for Scientific Research (NWO) (NWO-VIDI 864.13.013 to J.F., NWO VIDI 917.13.350 to R.A.D.B., NWO-VIDI 016.178.056 to A.Z., NWO-VIDI 917.14.374 to L.F., NWO Spinoza Prize SPI 94-212 to M.G.N., NWO Spinoza Prize SPI 92-266 to C.W., and NWO Gravitation Netherlands Organ-on-Chip Initiative (024.003.001) to C.W.); by the European Research Council (ERC) (FP7/2007-2013/ERC Advanced Grant Agreement 2012-322698 to C.W., ERC Consolidator Grant 310372 to M.G.N., ERC Starting Grant 715772 to A.Z., and ERC Starting Grant 637640 to L.F.); by the Stiftelsen Kristian Gerhard Jebsen Foundation (Norway) to C.W.; and by the RuG Investment Agenda Grant Personalized Health to C.W. A.Z. also holds a Rosalind Franklin Fellowship from the University of Groningen. D.V.Z. was supported by St. Petersburg State University (Genome Russia Grant no. 1.52.1647.2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Contributions

M.H.H., C.W., A.Z., and J.F. conceptualized the study. D.V.Z., T.L., A.K., M.J.B., A.Z., and J.F. designed the methodology. D.V.Z., T.L., M.J.B., and J.F. were responsible for the software. D.V.Z., T.L., A.K., B.A., M.J.B., A.C., U.V., P.D., A.Z., and J.F. carried out the formal analysis. D.V.Z., T.L., A.K., A.Z., and J.F. wrote the original draft of the manuscript. B.A., M.J.B., S.S., A.C., U.V., P.D., L.F., R.A.B., F.K., M.G.N., C.W., A.Z., and J.F. reviewed and edited the manuscript. D.V.Z., T.L., A.K., and J.F. were responsible for visualization. A.Z. and J.F. supervised the project. M.H.H., A.Z., and J.F. were responsible for project administration. L.F., R.A.D.B., F.K., M.G.N., M.H.H., C.W., A.Z., and J.F. were responsible for funding acquisition.

Corresponding authors

Correspondence to Alexandra Zhernakova or Jingyuan Fu.

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Competing interests

Dr. de Boer has received research grants and/or fees from AstraZeneca, Abbott, Bristol-Myers Squibb, Novartis, Roche, Trevena, and ThermoFisher GmbH. Dr. de Boer is a minority shareholder of scPharmaceuticals, Inc. Dr. de Boer received personal fees from MandalMed Inc, Novartis, and Servier. All other authors declare no competing interests.

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Integrated supplementary information

Supplementary Figure 1 Protein tissue specificity and association with diseases: CVD III panel protein characteristics according to the OLINK panel description.

(a), Forty-four proteins were widely expressed in different tissue types (not shown), but some proteins were tissue specific. (b), In addition to association with CVD, proteins were found to be associated with many other diseases, including cancer, neurological, metabolic, inflammatory and infectious diseases. (c), Correlation of protein levels with Framingham risk score calculated for the LifeLines-DEEP cohort. Significant correlations at Bonferroni-corrected P < 0.05 are shown.

Supplementary Figure 2

Manhattan plot of 129 cis-pQTLs for 66 proteins.

Supplementary Figure 3 Circos plot of 85 trans-pQTLs for 36 proteins.

The chromosome locations of the associated trans-SNPs and proteins are highlighted. SNPs are marked as red bars, and the mapped proteins are labeled. Each curved arrow indicates the trans-pQTL effect from a SNP to a protein.

Supplementary Figure 4 Pleiotropic effect of the KLKB1 locus.

(a), Trans-pQTL effect detected at four independent SNPs at the KLKB1 locus with multiple trans-regulated proteins associated with a missense SNP (rs3733402) and neighboring SNPs. The assessed allele is given in brackets. The associations of the SNP and proteins are presented as a bar plot. The y axis refers to the association strength in terms of –log P value. Association direction is shown. A blue positive bar indicates that the allele assessed is associated with a higher level of the protein. A red negative bar indicates association to a lower level. (b), Inter-correlation structure of nine proteins trans-regulated by rs3733402. Colored circles represent Spearman correlation coefficients: positive correlation (blue) and negative correlation (red). Color intensity and circle size indicate correlation strength. (c), Association of the KLKB1 locus in cis and in trans: trans-pQTL SNPs located at the KLKB1 locus are depicted as red diamonds; the corresponding trans-regulated proteins are depicted as blue ellipses; cis-pQTL SNPs regulating these proteins are shown as green diamonds; complex diseases and traits associated with SNPs and genes are shown as gray rectangles. We also show expression QTLs (eQTLs) on the same plot: genes whose expression is regulated in cis by trans-pQTL SNPs are depicted as pink ellipses. Blue edges correspond to trans-pQTLs, green edges to cis-pQTLs, red edges to cis-eQTLs and gray edges to trait associations.

Supplementary Figure 5 Comparison of QTL effect between plasma cis-pQTL and blood cis-eQTL from the same individuals.

x axis, Z score of cis-eQTL; y axis: Z score of cis-pQTL effect. Each circle represents a SNP–gene/protein pair. Circle size and color represent the strength and direction of the pQTL effect, respectively. The gray area indicates non-significant eQTL effects.

Supplementary Figure 6

Protein associations with microbial diversity with Bonferroni-corrected P < 0.05.

Supplementary Figure 7

Overlap of proteins significantly affected by genetics and microbiome.

Supplementary Figure 8 Comparison of protein and microbiome associations before and after correcting for genetic effects.

(a), The association between proteins and taxonomies. (b), The association between proteins and pathways. For all reported protein–microbiome associations, we compared the association strength, in terms of Spearman correlation coefficients and the corresponding P values, before and after correcting for 224 pQTLs and 26 mbQTLs.

Supplementary Figure 9

Comparison of the explained variance of circulating protein levels between a combined model of cis-pQTL, trans-pQTL and microbiome and an additive model that sums the explained variation by genetic factors and microbial factors separately.

Supplementary Figure 10 FUT2 and TMIGD1 gene expression by tissue type.

RPKM (reads per kilobase per million mapped reads) values are given according to ‘HPA RNA-seq normal tissues’ data from Fagerberg et al. (Mol. Cell. Proteomics 20141) displayed at NCBI Gene.

Supplementary Figure 11 PON3-PAI cluster microbiome associations.

(a), Pairwise Spearman correlation between PON3-associated taxa and pathways. (b), The number of pathways (left) and species (right) associated with PON3, PAI and both proteins is given before and after (in brackets) correction for BMI.

Supplementary information

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Zhernakova, D.V., Le, T.H., Kurilshikov, A. et al. Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome. Nat Genet 50, 1524–1532 (2018). https://doi.org/10.1038/s41588-018-0224-7

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