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Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import

Nature Genetics volume 56, pages 1614–1623 (2024)Cite this article

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Abstract

Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene–Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.

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Fig. 1: Summary and characterization of gene–metabolite associations.
Fig. 2: GDMNs are replicable and interpretable.
Fig. 3: GeneMAP identifies SLC25A48 as a genetic determinant of blood choline levels.
Fig. 4: SLC25A48 loss impacts mitochondrial choline homeostasis.
Fig. 5: SL25A48 mediates mitochondrial choline import.
Fig. 6: Choline-related phenomic consequences of SLC25A48 dysfunction.

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Data availability

We provide open access to the generated results for academic use through an interactive web server (https://birsoylab.rockefeller.edu/page/genemap/). In the study, we used publicly available summary statistics for the CLSA from the GWAS Catalog (https://www.ebi.ac.uk/gwas/) with accession numbers GCST90199621–GCST90201020; for METSIM with accession numbers GCST90139389–GCST90139409, GCST90139411–GCST90139491, GCST90139493–GCST90139502, GCST90139504–GCST90139575, GCST90139577–GCST90139640, GCST90139642–GCST90139714, GCST90139716–GCST90139847, GCST90139849–GCST90139891, GCST90139893–GCST90140217, GCST90140220–GCST90140276, GCST90140280–GCST90140282, GCST90140285–GCST90140312, GCST90140314, GCST90140316–GCST90140323, GCST90140325, GCST90140327, GCST90140330–GCST90140339, GCST90140341, GCST90140345–GCST90140406, GCST90140408-GCST90140420, GCST90140422–GCST90140476, GCST90140480–GCST90140482, GCST90140484–GCST90140487, GCST90140489–GCST90140496, GCST90140498–GCST90140515, GCST90140517–GCST90140530, GCST90140532–GCST90140609, GCST90140611–GCST90140648, GCST90140650–GCST90140662, GCST90140664, GCST90140665, GCST90140667, GCST90140677, GCST90140679, GCST90140681, GCST90140683–GCST90140696, GCST90140698–GCST90140701, GCST90140704, GCST90140708–GCST90140711, GCST90140713, GCST90140715–GCST90140718, GCST90140725, GCST90140728–GCST90140730, GCST90140733, GCST90140735, GCST90140741, GCST90140743, GCST90140744, GCST90140751, GCST90140752, GCST90140757–GCST90140762, GCST90140769, GCST90140771–GCST90140777, GCST90140779, GCST90140781–GCST90140786, GCST90140790–GCST90140794, GCST90140796–GCST90140799, GCST90140801, GCST90140802, GCST90140806–GCST90140813, GCST90140819, GCST90140825, GCST90140830, GCST90140832, GCST90140834–GCST90140837, GCST90140844, GCST90140849–GCST90140853, GCST90140855, GCST90140856, GCST90140858–GCST90140874, GCST90140884–GCST90140890, GCST90140899–GCST90140901, GCST90140903–GCST90140906, GCST90140910, GCST90140912, GCST90140913, GCST90140915–GCST90140917, GCST90140924 and GCST90140927–GCST90140932; for INTERVAL (https://app.box.com/s/rf6p81j3o507e8c5saywtlc1p91f8po9/folder/193817919002); and for GCKD from the GWAS Catalog with accession numbers GCST90264176–GCST90266872. The BioVU results are made available in this study. All requests for raw (genotype and phenotype) data and materials in BioVU are reviewed by Vanderbilt University Medical Center to determine whether the request is subject to any intellectual property or confidentiality obligations. For example, patient-related data not included in the paper may be subject to patient confidentiality. Any such data and materials that can be shared will be released via a material transfer agreement. Additional information on data access can be found on the Vanderbilt Institute for Clinical and Translational Research website (https://victr.vumc.org/how-to-use-biovu/). UK Biobank data were accessed under application number 94960. Additional details (code and source files) can be found on Zenodo (https://doi.org/10.5281/zenodo.11156916)60. Source data are provided with this paper.

Code availability

Code used for analysis in this study is available on GitHub (https://github.com/gamazonlab/GeneMAP/) as well as on Zenodo (https://doi.org/10.5281/zenodo.11156916)60.

References

  1. Prosser, G. A., Larrouy-Maumus, G. & de Carvalho, L. P. S. Metabolomic strategies for the identification of new enzyme functions and metabolic pathways. EMBO Rep. 15, 657–669 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Pizzagalli, M. D., Bensimon, A. & Superti-Furga, G. A guide to plasma membrane solute carrier proteins. FEBS J. 288, 2784–2835 (2021).

    Article  PubMed  Google Scholar 

  3. Wiedmer, T., Ingles-Prieto, A., Goldmann, U., Steppan, C. M. & Superti-Furga, G. Accelerating SLC transporter research: streamlining knowledge and validated tools. Clin. Pharmacol. Ther. 112, 439–442 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  4. César-Razquin, A. et al. A call for systematic research on solute carriers. Cell 162, 478–487 (2015).

    Article  PubMed  Google Scholar 

  5. Shi, X. et al. Combinatorial GxGxE CRISPR screen identifies SLC25A39 in mitochondrial glutathione transport linking iron homeostasis to OXPHOS. Nat. Commun. 13, 2483 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kenny, T. C. et al. Integrative genetic analysis identifies FLVCR1 as a plasma-membrane choline transporter in mammals. Cell Metab. 35, 1057–1071.e12 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wang, Y. et al. SLC25A39 is necessary for mitochondrial glutathione import in mammalian cells. Nature 599, 136–140 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Unlu, G. et al. Metabolic-scale gene activation screens identify SLCO2B1 as a heme transporter that enhances cellular iron availability. Mol. Cell 82, 2832–2843.e7 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dvorak, V. et al. An overview of cell-based assay platforms for the solute carrier family of transporters. Front. Pharmacol. 12, 722889 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Barroso, I. & McCarthy, M. I. The genetic basis of metabolic disease. Cell 177, 146–161 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rios, S. et al. Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes. Cardiovasc. Diabetol. 22, 252 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang, F. et al. Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals. Nat. Commun. 14, 5744 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Schlosser, P. et al. Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine. Nat. Genet. 55, 995–1008 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yin, X. et al. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat. Commun. 13, 1644 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen, Y. et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat. Genet. 55, 44–53 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Surendran, P. et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat. Med. 28, 2321–2332 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yin, X. et al. Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk. Am. J. Hum. Genet. 109, 1727–1741 (2022).

  18. Lotta, L. A. et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat. Genet. 53, 54–64 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Porcu, E. et al. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat. Commun. 10, 3300 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhou, D. et al. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat. Genet. 52, 1239–1246 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mogil, L. S. et al. Genetic architecture of gene expression traits across diverse populations. PLoS Genet. 14, e1007586 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023).

    Article  CAS  PubMed  Google Scholar 

  28. Rentzsch, P., Schubach, M., Shendure, J. & Kircher, M. CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 13, 31 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jeong, H., Tombor, B., Albert, R., Oltval, Z. N. & Barabásl, A. L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).

    Article  CAS  PubMed  Google Scholar 

  30. Stacey, D. et al. ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res. 47, e3 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Li, F., Chen, Y., Anton, M. & Nielsen, J. GotEnzymes: an extensive database of enzyme parameter predictions. Nucleic Acids Res. 51, D583–D586 (2023).

    Article  CAS  PubMed  Google Scholar 

  32. Aguet, F. et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  CAS  Google Scholar 

  33. Zeisel, S. H. & Da Costa, K. A. Choline: an essential nutrient for public health. Nutr. Rev. 67, 615–623 (2009).

    Article  PubMed  Google Scholar 

  34. Combs, G. F. Jr. & McClung, J. P. (eds) in The Vitamins 523–589 (Academic Press, 2022).

  35. Kennedy, E. P. & Weiss, S. B. The function of cytidine coenzymes in the biosynthesis of phospholipides. J. Biol. Chem. 222, 193–214 (1956).

    Article  CAS  PubMed  Google Scholar 

  36. Ducker, G. S. & Rabinowitz, J. D. One-carbon metabolism in health and disease. Cell Metab. 25, 27–42 (2017).

    Article  PubMed  Google Scholar 

  37. Dragolovich, J. Dealing with salt stress in animal cells: the role and regulation of glycine betaine concentrations. J. Exp. Zool. 268, 139–144 (1994).

    Article  CAS  Google Scholar 

  38. Ueland, P. M. Choline and betaine in health and disease. J. Inherit. Metab. Dis. 34, 3–15 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Chen, W. W., Freinkman, E., Wang, T., Birsoy, K. & Sabatini, D. M. Absolute quantification of matrix metabolites reveals the dynamics of mitochondrial metabolism. Cell 166, 1324–1337.e11 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Palmieri, F. The mitochondrial transporter family SLC25: identification, properties and physiopathology. Mol. Aspects Med. 34, 465–484 (2013).

    Article  CAS  PubMed  Google Scholar 

  41. Chen, S. et al. A genome-wide mutational constraint map quantified from variation in 76,156 human genomes. Preprint at bioRxiv https://doi.org/10.1101/2022.03.20.485034 (2022).

  42. Son, Y., Kenny, T. C., Khan, A., Birsoy, K. & Hite, R. K. Structural basis of lipid head group entry to the Kennedy pathway by FLVCR1. Nature 629, 710–716 (2024).

    Article  CAS  PubMed  Google Scholar 

  43. Ri, K. et al. Molecular mechanism of choline and ethanolamine transport in humans. Nature 630, 501–508 (2024).

  44. Verkerke, A. R. P., Shi, X., Abe, I., Gerszten, R. E. & Kajimura, S. Mitochondrial choline import regulates purine nucleotide pools via SLC25A48. Preprint at bioRxiv https://doi.org/10.1101/2023.12.31.573776 (2024).

  45. Patil, S. et al. SLC25A48 is a human mitochondrial choline transporter. Preprint at medRxiv https://doi.org/10.1101/2023.12.04.23299390 (2023).

  46. Ardlie, K. G. et al. The Genotype–Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  Google Scholar 

  47. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Lonsdale, J. et al. The Genotype–Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  CAS  Google Scholar 

  49. Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Burgess, S., Thompson, S. G. & CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40, 755–764 (2011).

  51. Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat. Commun. 12, 764 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wu, P. et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med. Inform. 7, e14325 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Choi, S. W. & O’Reilly, P. F. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience 8, giz082 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Xu, Y. et al. An atlas of genetic scores to predict multi-omic traits. Nature 616, 123–131 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Loos, M., Gerber, C., Corona, F., Hollender, J. & Singer, H. Accelerated isotope fine structure calculation using pruned transition trees. Anal. Chem. 87, 5738–5744 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. Pino, L. K. et al. The Skyline ecosystem: informatics for quantitative mass spectrometry proteomics. Mass Spectrom. Rev. 39, 229–244 (2020).

    Article  CAS  PubMed  Google Scholar 

  59. Boughton, A. P. et al. LocusZoom.js: interactive and embeddable visualization of genetic association study results. Bioinformatics 37, 3017–3018 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Khan, A. Lin, P. & Birsoy, K. & Gamazon, E. GeneMAP. Zenodo https://doi.org/10.5281/zenodo.11156917 (2024).

Download references

Acknowledgements

We thank all members of the Birsoy and Gamazon laboratories for suggestions and also members of the Rockefeller University Proteomics Resource Center and the Flow Cytometry Resource Center. Some figures use modified illustrations from Servier Medical Art licensed under a Creative Commons Attribution 3.0 Unported License. A.K. is supported by a Boehringer Ingelheim Fonds PhD Fellowship. G.U. is a Damon Runyon Fellow supported by the Damon Runyon Cancer Research Foundation (DRG-2431-21) and NIH/NCI 1K99CA286722-01. Y.L. is supported by NIH/NCI 1F99CA284249-01. T.C.K. is supported by the NIH/NIDDK (F32 DK127836), the Shapiro-Silverberg Fund for the Advancement of Translational Research and a Merck Postdoctoral Fellowship at the Rockefeller University. K.B. is supported by the NIH/NIDDK (R01 DK123323-01) and a Mark Foundation Emerging Leader Award and is a Searle and Pew-Stewart Scholar. E.R.G. is supported by the NIH/NHGRI (R01HG011138), the NIH/NIGMS (R01GM140287), the NIH/NIA (R56AG068026), the NIH Office of the Director (U24OD035523) and a Genomic Innovator Award (R35HG010718). UK Biobank data were accessed under application number 94960. The GTEx project was supported by the Common Fund of the Office of the Director of the National Institutes of Health and by the NCI, the NHGRI, the NHLBI, the NIDA, the NIMH and the NINDS. We thank E. Karoly (Metabolon) for help with testing the veracity of our predictions for uncharacterized metabolites.

Author information

Authors and Affiliations

  1. Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA

    Artem Khan, Gokhan Unlu, Yuyang Liu, Ece Kilic, Timothy C. Kenny & Kıvanç Birsoy

  2. Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Phillip Lin & Eric R. Gamazon

  3. Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA

    Eric R. Gamazon

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  1. Artem Khan
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  6. Timothy C. Kenny
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Contributions

E.R.G., K.B. and A.K. conceived the project and wrote the manuscript. K.B. and A.K. designed experiments. A.K. developed the GeneMAP platform with input from E.R.G. A.K. performed most of the experiments with the assistance of G.U., Y.L. and T.C.K. E.K. conducted LC–MS analysis. E.R.G., P.L. and A.K. analyzed phenomic datasets. E.R.G., P.L. and A.K. developed the interactive portal.

Corresponding authors

Correspondence to Kıvanç Birsoy or Eric R. Gamazon.

Ethics declarations

Competing interests

K.B. is scientific advisor to Nanocare Pharmaceuticals and Atavistik Bio. The other authors declare no competing interests.

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Nature Genetics thanks Johan Bjorkegren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Development of the GeneMAP pipeline.

a. Bar graph displaying the replicability measure π1 across JTI expression models when CLSA was used as discovery and METSIM as validation (yellow) and vice versa (black). b. Pie chart displaying the proportion of the GCTA-COJO-proposed gene-metabolite pairs (Chen et al.) by JTI-TWAS approach. Only metabolites measured in both CLSA and METSIM were considered. Orange represents identified by JTI-TWAS, black– not. c. Pie chart displaying the proportion of the JTI-TWAS-proposed analysis gene-metabolite pairs by GCTA-COJO approach (Chen et al.). Only metabolites measured in both CLSA and METSIM were considered. Yellow represents identified by JTI-TWAS, gray – not. d. Schematic for HyPrColoc analysis. e. Histogram displaying the distribution of p-values from the HyPrColoc analysis on significant gene-metabolite pairs. f. Replicability measure π1 as a function of the tissue sample size in GTEx used for the JTI expression model training. g. Number of genes in the model as a function of the tissue sample size in GTEx used for the JTI expression model training. h. Number of identified significant gene-metabolite associations as a function of the tissue sample size in GTEx used for the JTI expression model training. i. Histogram with the distribution of the number of JTI expression models in which the significant gene-metabolite associations are present. j. Pleiotropicity of the genes. Histograms with the distribution of the number of metabolites associated with the gene among the significant gene-metabolite associations. k. Violin plot showing the distribution of the top CADD scores for individual genes in the whole-genome (gray), the non-metabolic protein coding (yellow) and metabolic genes (orange) groups. Statistical significance was determined by two-tailed unpaired t test.

Source data

Extended Data Fig. 2 Genetically Determined Metabolic Network Analysis.

a. GDMN built from CLSA dataset with the Louvain community analysis. b. GDMN built from METSIM dataset with the Louvain community analysis. c. Distribution of the node degrees for the observed consensus network (yellow points) and simulated random networks (n = 20,000, black points). The yellow line is regression line for the consensus network degree distribution with R2, and the gray band is a confidence interval. d. Error tolerance of the consensus GDMN represented as an average distance between the metabolic nodes in network when consequently removing 10 random nodes (black points, simulated 500 times) and 10 hubs (yellow points, ordered from the most to least connected nodes). e. QQ-plot showing the distribution of Pearson correlation for metabolite-metabolite pairs computed from the genetically-determined component and metabolite level data. f. Histogram displaying the distribution of Pearson correlation of metabolite-metabolite pairs between the CLSA-built GDMN and permuted MDNs (n = 100). The red vertical line displays the actual value. g. Metabolite-derived network built from CLSA dataset with the Louvain community analysis. h. Histogram displaying the distribution of Pearson correlation for the node degrees between the CLSA-built GDMN and permuted MDNs (n = 100). The red vertical line displays the actual value. i. Histogram displaying the distribution of the number of overlapping Louvain communities between the CLSA-built GDMN and permuted MDNs (n = 100). The red vertical line displays the actual value.

Source data

Extended Data Fig. 3 SLC25A48 as a genetic determinant of blood choline level.

a. Distribution of the number of genes scoring for the same metabolite per extended locus (10 Mb) among the GeneMAP pairs. b. Number of discoveries as a function of distance from mQTLs. c. Schematic for integrating GDMN with prioritized GeneMAP pairs. d. Network integrating GDMN with prioritized GeneMAP pairs. e. Pie plots displaying the number of annotated metabolite-metabolite associations (if any gene-mediated connection is annotated) and gene-mediated metabolite-metabolite associations. f. LocusZoom plot for CLSA blood choline association signals in SLC25A48 region. P values are from the CLSA study GWAS summary statistics. g. LocusZoom plot for GCKD blood choline association signals in SLC25A48 region. P values are from the GCKD study GWAS summary statistics. h. Schematic for Mendelian Randomization analysis of SLC25A48 effect on plasma choline level. i. Scatterplot showing the variants’ genetic association with SLC25A48 viewed as exposure (x-axis) and choline viewed as outcome (y-axis). Spearman correlation ρ = 0.696, p-value < 2.2e-16. P value is computed by asymptotic t approximation.

Source data

Extended Data Fig. 4 Functionally uncharacterized GeneMAP gene-metabolite associations.

a. Bubble plot of unknown gene-metabolite associations identified by GeneMAP. Bubble color corresponds to the -log10(p-value) of the association between the indicated gene and metabolite in discovery (CLSA) dataset. Bubble size represents the -log10(p-value) of the association between the indicated gene and metabolite in validation (METSIM) dataset. P values (uncorrected) are from the TWAS METSIM and CLSA analysis. b. Bipartite plot displaying the unknown gene-metabolite associations.

Source data

Extended Data Fig. 5 Biochemical characterization of SLC25A48 as a mediator of mitochondrial choline import.

a. ICE sequencing results for the generated single clones of HEK293T SLC25A48 knockout cells. b. Schematic of 3xFLAG-SLC25A48 construct. c. Immunoblot of SLC25A48 (Flag) in HEK293T knockout cells expressing an empty control vector or 3xFLAG-SLC25A48 cDNA. Tubulin was used as loading control. d. Immunoblot of indicated proteins in input (whole cell) and immunopurified mitochondria from HEK293T SLC25A48 knockout cells expressing a vector control or 3xFLAG-SLC25A48 cDNA. e. Volcano plot with -log10(q-value) vs. log2 fold change in metabolite abundance normalized to ISTDs and protein concentration in input (whole cell) from HEK293T SLC25A48-knockout cells expressing a vector control or SLC25A48 cDNA. The dotted line is the significance threshold of q-value < 0.05. f. Betaine [M + 2]/Phosphocholine [M + 2] abundance ratio in HEK293T SLC25A48-knockout cells expressing an empty vector control or 3xFLAG-SLC25A48 cDNA after incubation with [1,2-13C2]Choline for the indicated time points. Data are individual points and normalized by ISTDs and cells seeded; n = 3 biological replicates. Line corresponds to the mean for each timepoint. g. Barplot of betaine [M + 2] abundance in HEK293T SLC25A48-knockout cells (Clone 2) expressing an empty vector control or 3xFLAG-SLC25A48 cDNA after incubation with [1,2-13C2]Choline for 2 hours. Data are mean ± standard deviation and normalized by ISTDs and cells seeded; n = 3 biological replicates. h. Barplot of phosphocholine [M + 2] abundance in HEK293T SLC25A48-knockout cells (Clone 2) expressing an empty vector control or 3xFLAG-SLC25A48 cDNA after incubation with [1,2-13C2]Choline for 2 hours. Data are mean ± standard deviation and normalized by ISTDs and cells seeded; n = 3 biological replicates. i. Barplot showing the ratio of metabolic abundance of betaine [M + 2] to phosphocholine [M + 2] in HEK293 parental and SLC25A48 knockout cells after incubation with [1,2-13C2]Choline for 24 hours. Data are mean ratios ± standard deviation. The metabolite abundances were normalized by ISTDs and cells seeded. All the experiments were repeated at least twice. Two-tailed unpaired t tests followed by Benjamini, Krieger, and Yekutieli multiple test correction (e), two-way RM ANOVA followed by post hoc Bonferroni multiple correction (f), two-tailed unpaired t test (g, h, i).

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Extended Data Fig. 6 Phenomic consequences of SLC25A48 dysfunction.

a. QQ plot showing the observed and expected distribution of -log10(p-value) for the rare variant testing results on SLC25A48 pLoF variants. The gray line is y = x, the orange dashed line corresponds to FDR = 0.05, the black dashed line is Bonferroni threshold based on the number of considered phenotypes. P values from the SKAT-O rare variant testing (Methods). b. Schematic for replication of the rare variant results from UK Biobank using analysis of genetically-determined expression in the independent BioVU, Vanderbilt University’s DNA biobank. The association of SLC25A48 with ‘Hereditary disturbances in tooth structure’ replicated (p-value = 0.008) under Bonferroni correction from the analysis of genetically-determined expression in BioVU.

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Supplementary information

Supplementary Information

Supplementary Note and Figs. 1 and 2.

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Supplementary Tables 1–3

Supplementary Table 1: Annotation of gene–metabolite pairs. Supplementary Table 2: Minor allele frequency of rs200164783. Supplementary Table 3: Nucleotide sequences.

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Khan, A., Unlu, G., Lin, P. et al. Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import. Nat Genet 56, 1614–1623 (2024). https://doi.org/10.1038/s41588-024-01827-2

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