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A large-scale genome–lipid association map guides lipid identification

Nature Metabolism volume 2, pages 1149–1162 (2020)Cite this article

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Abstract

Despite the crucial roles of lipids in metabolism, we are still at the early stages of comprehensively annotating lipid species and their genetic basis. Mass spectrometry–based discovery lipidomics offers the potential to globally survey lipids and their relative abundances in various biological samples. To discover the genetics of lipid features obtained through high-resolution liquid chromatography–tandem mass spectrometry, we analysed liver and plasma from 384 diversity outbred mice, and quantified 3,283 molecular features. These features were mapped to 5,622 lipid quantitative trait loci and compiled into a public web resource termed LipidGenie. The data are cross-referenced to the human genome and offer a bridge between genetic associations in humans and mice. Harnessing this resource, we used genome–lipid association data as an additional aid to identify a number of lipids, for example gangliosides through their association with B4galnt1, and found evidence for a group of sex-specific phosphatidylcholines through their shared locus. Finally, LipidGenie’s ability to query either mass or gene-centric terms suggests acyl-chain-specific functions for proteins of the ABHD family.

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Fig. 1: LC–MS/MS lipidomics and QTL mapping as ways to lipid identification.
Fig. 2: Large-scale lipid quantitative profiling and subsequent QTL mapping reveals hotspots of associated lipids.
Fig. 3: Comapping of lipids at the Apoa2 locus facilitated identification of additional cholesteryl esters.
Fig. 4: Lipid features mapping to B4galnt1 lead to identification of GM2 and GM3 gangliosides.
Fig. 5: Web resource LipidGenie guides exploration of genome–lipid connections.

Data availability

Genotypes and additional phenotype data associated with the DO mouse population have been deposited with Dryad (https://doi.org/10.5061/dryad.pj105; data files: Attie Islet eQTL data) (see Keller et al., ref. 32, for details). In addition, the data reported here are available for download and interactive web-based analysis at https://churchilllab.jax.org/qtlviewer/attie/islets. Genotyping used the Mouse Universal Genotyping Array (GigaMUGA; 143,259 markers).

MS data have been deposited in Chorus (http://chorusproject.org/) under ID 1610 (direct links to cell experiments https://chorusproject.org/anonymous/download/experiment/4984245205453479277, DO liver https://chorusproject.org/anonymous/download/experiment/a639bcc5602c441c9a1df94f4340d626, DO plasma https://chorusproject.org/anonymous/download/experiment/f8b273d222364f2a9d92cfdd0eb601b6, FS liver https://chorusproject.org/anonymous/download/experiment/c930cd419eb34dfebda7f53508c6969e, and FS plasma https://chorusproject.org/anonymous/download/experiment/9d4d025df0114687924d4075f3c927ca). Human Mouse homologues were obtained from the MGI homology database (available here: http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt). SNP associations were performed accessing variants from the database cc_variants.sqlite (available here: https://ndownloader.figshare.com/files/18533342) and genes from mouse_genes_mgi.sqlite (available here: https://ndownloader.figshare.com/files/17609252). Figures 1–5 and Extended Data Figs. 1–6 have associated raw data. Source data are provided with this paper.

Code availability

The data preparation and QTL mapping analysis are reproducibly documented in UNIX shell and R scripts posted on github (https://github.com/dmgatti/AttieMetabolomics). Code for data analysis and plotting is available at https://github.com/vanilink/DOLipids/ with input from Supplementary Tables 8 and 9. The genome–lipid associations are also accessible through an interactive web-based analysis tool that will allow users to replicate the analyses reported here (http://lipidgenie.com/). The source code for this resource can be found at https://github.com/coongroup/LipidGenie.

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Acknowledgements

This work was supported by National Institutes of Health grant nos. P41 GM108538 and 2R01DK101573. This research was performed using the compute resources and assistance of the UW–Madison Center For High Throughput Computing (CHTC) in the Department of Computer Sciences. The CHTC is supported by UW–Madison, the Advanced Computing Initiative, the Wisconsin Alumni Research Foundation, the Wisconsin Institutes for Discovery, and the National Science Foundation, and is an active member of the Open Science Grid, which is supported by the National Science Foundation and the US Department of Energy’s Office of Science. We thank B. Paulson for help with sample preparation and D. Hwang for help with figures. We thank J. Simcox (Department of Biochemistry, University of Wisconsin–Madison, Madison) for providing the His-tagged CMV6-GFP plasmid and Hepa1-6 cells.

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Authors and Affiliations

  1. Department of Chemistry, University of Wisconsin–Madison, Madison, WI, USA

    Vanessa Linke, Dain R. Brademan, Paul D. Hutchins, Edna A. Trujillo & Joshua J. Coon

  2. Morgridge Institute for Research, Madison, WI, USA

    Katherine A. Overmyer, Thiru R. Reddy & Jason D. Russell

  3. Department of Biomolecular Chemistry, University of Wisconsin–Madison, Madison, WI, USA

    Katherine A. Overmyer, Ian J. Miller, Thiru R. Reddy & Joshua J. Coon

  4. Department of Biochemistry, University of Wisconsin–Madison, Madison, WI, USA

    Emily M. Cushing, Kathryn L. Schueler, Donald S. Stapleton, Mary E. Rabaglia, Mark P. Keller & Alan D. Attie

  5. The Jackson Laboratory, Bar Harbor, ME, USA

    Daniel M. Gatti, Gregory R. Keele, Duy Pham & Gary A. Churchill

  6. Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI, USA

    Karl W. Broman

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  1. Vanessa Linke
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Contributions

J.D.R., M.P.K., G.A.C., A.D.A. and J.J.C. designed the experiment. K.L.S., D.S.S., M.E.R. and M.P.K. assisted with sample collection. V.L., P.D.H., E.A.T. and T.R.R. performed the MS analysis. E.M.C. performed cell experiments. V.L., I.J.M., D.R.B., P.D.H., M.P.K., D.M.G., G.R.K., D.P. and G.A.C. analysed data. V.L., K.A.O., I.J.M., M.P.K., K.W.B., G.A.C., A.D.A. and J.J.C. wrote the manuscript.

Corresponding author

Correspondence to Joshua J. Coon.

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The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Identified lipids and unidentified features occupy characteristic regions in the m/z vs. RT space.

a, In plasma, we quantified 1,721 lipidomic features, 621 of which were identified, and b, In liver, we quantified 1,562 lipidomic features, 615 of which were identified. Abbreviations: m/z (mass-to-charge), RT (retention time).

Extended Data Fig. 2 Lipid profiling and subsequent QTL mapping reveals clusters of associated lipids.

a, Lipid class distribution of all 1,721 plasma and b, 1,562 liver lipidomic features. c, 1,405 plasma and d, 1,190 lipid features showed at least one QTL with an LOD > 6 as displayed in a Manhattan plot (n = 3,353 and 2,269 total QTL, respectively). Hierarchical clustering of these features against the 69,005 markers on the mouse genome, resulted in clustering of lipid class based on hotspots at the genetic level. Abbreviations: Chr (chromosome), DO (diversity outbred), QTL (quantitative trait loci), LOD (logarithm of odds).

Extended Data Fig. 3 Apoa2 as the candidate gene at the largest lipid hotspot.

a, 255 plasma (black) features mapping to the apoa2 locus on chromosome 1 share an allele effect pattern with upregulation in the 129 allele, while 2 mapping liver features (white) do not share the pattern (based on hierarchical clustering on allele effects, with a Euclidean distance cutoff of h = 1.5). b, The allele effect is exemplary replicated in an independent experiment of founder strain plasma CE(18:2) levels (n = 4 for each sex and strain, boxplots are defined with the first and third quartiles (25th and 75th percentile) for lower and upper hinges, 1.5x interquartile range for the length of the whiskers, centre line at median (50% quantile)). c, The same pattern was not visible in previously reported34 Apoa2 liver protein and RNA allele effects. Abbreviations: CE (cholesteryl ester), FS (founder strain).

Extended Data Fig. 4 B4galnt1 as the candidate gene at the hotspot with the largest LOD.

a, The selection of B4galnt1 as the candidate gene for the chromosome 10:127 Mbp locus was corroborated by NOD-specific allele effects in previously reported liver eQTL and b, pQTL34. c, The allele effect patterns of the later as gangliosides identified features mapping to the B4galnt1 locus could further be validated in an independent experiment of founder strain mice (exemplar GM3 pattern, n = 4 for each sex and strain, boxplots are defined with the first and third quartiles (25th and 75th percentile) for lower and upper hinges, 1.5x interquartile range for the length of the whiskers, center line at median (50% quantile)). Abbreviations: FS (founder strain), Mbp (megabase pair).

Extended Data Fig. 5 Allele effects characterize genome-lipid hotspots.

a, Hierarchical clustering of allele effects at Chr 6:91 Mbp resulted in 21 features with matching A/J down effect (main cluster featuring the six B6 male specific features (red) after row-scaling and Ward clustering, cutoff at h=5). b, Consistently, the pattern of male >> female was observed for each of the FS except for A/J as visible in the example for m/z 1130 (n = 4 for each sex and strain, boxplots are defined with the first and third quartiles (25th and 75th percentile) for lower and upper hinges, 1.5x interquartile range for the length of the whiskers, center line at median (50% quantile).) c, Hierarchical clustering of allele effects at Chr 5:31 Mbp locus resulted in 10 features with matching B6 and NZO up effect (main cluster featuring LysoPC 14:0 (turquoise) after row-scaling and Ward clustering, cutoff at h=8). d, This pattern could be replicated in the FS (n = 4 for each sex and strain, boxplots are defined with the first and third quartiles (25th and 75th percentile) for lower and upper hinges, 1.5x interquartile range for the length of the whiskers, center line at median (50% quantile)), as shown for LysoPC 14:0, as well as e, in opposite directionality in a liver eQTL34. f, Hierarchical clustering of allele effects at Chr 7:79 Mbp locus resulted in 8 features with matching WSB down effect (main cluster featuring PUFA-containing phospholipids (turquoise) after row-scaling and Ward clustering, cutoff at h=2.5). g, The mapping phospholipids contained polyunsaturated fatty acids such as 20:4 and 22:6. h-i, Abhd2 liver RNA and protein allele effects34 matched with an opposite WSB high effect. Abbreviations: DO (diversity outbred), FS (founder strain), Chr (chromosome), Mbp (megabase pair), PC (phosphatidylcholine), PUFA (polyunsaturated fatty acid).

Extended Data Fig. 6 Overexpressing ABHD1 and ABHD3 results in distinct phospholipid signature.

a, Experimental design of the validation experiment featuring three technical and four biological replicates of Hepa1-6 cells either untransfected (CTL), transfected with a His-tag GFP control (GFP), or transfected with MYC-tagged ABHD1 or ABHD3. b, Western blot of Hepa1-6 overexpression of ABHD1 and ABHD3. Shown is an overlay of membrane and ECL blot for MYC-tag. c, Heatmap of top 49 features from discovery lipidomics experiment with p < 0.05 (ANOVA, Fisher’s LSD post-hoc). Features were sum-normalized and log2-transformed. Hierarchical clustering (Ward clustering, Euclidean distance) shows two clusters with opposite fold changes distinguishing between ABHD1 and ABHD3 and the GFP control.

Source data

Extended Data Fig. 7 Lipid class abbreviations and identifications with respective adduct types.

As searched for in LipiDex databases (see Methods).

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–12

Source data

Source Data Fig. 5

Statistical source data for Fig. 5k–m.

Source Data Extended Data Fig. 6

Unprocessed western blot from Extended Data Fig. 6b.

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Linke, V., Overmyer, K.A., Miller, I.J. et al. A large-scale genome–lipid association map guides lipid identification. Nat Metab 2, 1149–1162 (2020). https://doi.org/10.1038/s42255-020-00278-3

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