Genetically personalised organ-specific metabolic models in health and disease

Carles Foguet; Yu Xu; Scott C Ritchie; Samuel A Lambert; Elodie Persyn; Artika P Nath; Emma E Davenport; David J Roberts; Dirk S Paul; Emanuele Di Angelantonio; John Danesh; Adam S Butterworth; Christopher Yau; Michael Inouye

doi:10.1038/s41467-022-35017-7

Genetically personalised organ-specific metabolic models in health and disease

Nat Commun. 2022 Nov 29;13(1):7356. doi: 10.1038/s41467-022-35017-7.

Authors

Carles Foguet^{1

2

3

4}, Yu Xu^{5

6

7}, Scott C Ritchie^{5

6

7

8

9}, Samuel A Lambert^{5

10

6

7}, Elodie Persyn^{5

6

7}, Artika P Nath^{5

9}, Emma E Davenport¹¹, David J Roberts^{12

13

14}, Dirk S Paul^{6

7

8}, Emanuele Di Angelantonio^{10

6

7

8

13

15}, John Danesh^{10

6

7

8

11

13}, Adam S Butterworth^{10

6

7

8

13}, Christopher Yau^{16

17}, Michael Inouye^{18

19

20

21

22

23

24}

Affiliations

¹ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. [email protected].
² Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. [email protected].
³ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. [email protected].
⁴ Heart and Lung Research Institute, University of Cambridge, Cambridge, UK. [email protected].
⁵ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁶ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁷ Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
⁸ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
⁹ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
¹⁰ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
¹¹ Wellcome Sanger Institute, Hinxton, UK.
¹² BRC Haematology Theme, Radcliffe Department of Medicine, and NHSBT-Oxford, John Radcliffe Hospital, Oxford, UK.
¹³ National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK.
¹⁴ NHS Blood and Transplant, John Radcliffe Hospital, Oxford, UK.
¹⁵ Health Data Science Centre, Human Technopole, Milan, Italy.
¹⁶ Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, OX3 9DU, UK.
¹⁷ Health Data Research UK, Gibbs Building, 215 Euston Road, London, NW1 2BE, UK.
¹⁸ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. [email protected].
¹⁹ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. [email protected].
²⁰ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. [email protected].
²¹ Heart and Lung Research Institute, University of Cambridge, Cambridge, UK. [email protected].
²² British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK. [email protected].
²³ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia. [email protected].
²⁴ The Alan Turing Institute, London, UK. [email protected].

Abstract

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adipose Tissue*
Brain
Coronary Artery Disease*
Genome, Human
Heart
Humans

Abstract

Publication types

MeSH terms

Grants and funding