Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

γ-Linolenic acid in maternal milk drives cardiac metabolic maturation

Nature volume 618, pages 365–373 (2023)Cite this article

Subjects

An Author Correction to this article was published on 15 June 2023

This article has been updated

Abstract

Birth presents a metabolic challenge to cardiomyocytes as they reshape fuel preference from glucose to fatty acids for postnatal energy production1,2. This adaptation is triggered in part by post-partum environmental changes3, but the molecules orchestrating cardiomyocyte maturation remain unknown. Here we show that this transition is coordinated by maternally supplied γ-linolenic acid (GLA), an 18:3 omega-6 fatty acid enriched in the maternal milk. GLA binds and activates retinoid X receptors4 (RXRs), ligand-regulated transcription factors that are expressed in cardiomyocytes from embryonic stages. Multifaceted genome-wide analysis revealed that the lack of RXR in embryonic cardiomyocytes caused an aberrant chromatin landscape that prevented the induction of an RXR-dependent gene expression signature controlling mitochondrial fatty acid homeostasis. The ensuing defective metabolic transition featured blunted mitochondrial lipid-derived energy production and enhanced glucose consumption, leading to perinatal cardiac dysfunction and death. Finally, GLA supplementation induced RXR-dependent expression of the mitochondrial fatty acid homeostasis signature in cardiomyocytes, both in vitro and in vivo. Thus, our study identifies the GLA–RXR axis as a key transcriptional regulatory mechanism underlying the maternal control of perinatal cardiac metabolism.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: EDKO mice undergo lethal cardiac dysfunction and transcriptional alteration of lipid metabolism.
Fig. 2: Lack of RXR in perinatal cardiomyocytes blocks fetal-to-neonatal metabolic switch.
Fig. 3: RXR transactivates the expression of mtFAH genes.
Fig. 4: Maternal milk-borne GLA activates the RXR-dependent gene signature.
Fig. 5: GLA is a potential ligand for RXR.

Similar content being viewed by others

Data availability

Data generated from RNA-seq, ATAC–seq and ChIP–seq have been deposited in the Gene Expression Omnibus with the accession number GSE188991. Source data for main and extended data figures are available online as separate Excel files for each figure. Lipidomics raw data from cardiac tissue (Fig. 2d) and milk (Fig. 4h) can be found in Supplementary Table 1 and Supplementary Table 2, respectively. Source data are provided with this paper.

Change history

References

  1. Itoi, T. & Lopaschuk, G. D. The contribution of glycolysis, glucose oxidation, lactate oxidation, and fatty acid oxidation to ATP production in isolated biventricular working hearts from 2-week-old rabbits. Pediatr. Res. 34, 735–741 (1993).

    Article  CAS  PubMed  Google Scholar 

  2. Lopaschuk, G. D. & Spafford, M. A. Energy substrate utilization by isolated working hearts from newborn rabbits. Am. J. Physiol. 258, H1274–H1280 (1990).

    CAS  PubMed  Google Scholar 

  3. Girard, J., Ferré, P., Pégorier, J. P. & Duée, P. H. Adaptations of glucose and fatty acid metabolism during perinatal period and suckling-weaning transition. Physiol. Rev. 72, 507–562 (1992).

    Article  CAS  PubMed  Google Scholar 

  4. Rőszer, T., Menéndez-Gutiérrez, M. P., Cedenilla, M. & Ricote, M. Retinoid X receptors in macrophage biology. Trends Endocrinol. Metab. 24, 460–468 (2013).

    Article  PubMed  Google Scholar 

  5. Lopaschuk, G. D., Ussher, J. R., Folmes, C. D., Jaswal, J. S. & Stanley, W. C. Myocardial fatty acid metabolism in health and disease. Physiol. Rev. 90, 207–258 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Lopaschuk, G. D., Spafford, M. A. & Marsh, D. R. Glycolysis is predominant source of myocardial ATP production immediately after birth. Am. J. Physiol. 261, H1698–H1705 (1991).

    CAS  PubMed  Google Scholar 

  7. Piquereau, J. & Ventura-Clapier, R. Maturation of cardiac energy metabolism during perinatal development. Front. Physiol. 9, 959 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Krężel, W., Rühl, R. & de Lera, A. R. Alternative retinoid X receptor (RXR) ligands. Mol. Cell. Endocrinol. 491, 110436 (2019).

    Article  PubMed  Google Scholar 

  9. Mascrez, B. et al. The RXRα ligand-dependent activation function 2 (AF-2) is important for mouse development. Development 125, 4691–4707 (1998).

    Article  CAS  PubMed  Google Scholar 

  10. Sucov, H. M. et al. RXR alpha mutant mice establish a genetic basis for vitamin A signaling in heart morphogenesis. Genes Dev. 8, 1007–1018 (1994).

    Article  CAS  PubMed  Google Scholar 

  11. Chen, J., Kubalak, S. W. & Chien, K. R. Ventricular muscle-restricted targeting of the RXRα gene reveals a non-cell-autonomous requirement in cardiac chamber morphogenesis. Development 125, 1943–1949 (1998).

    Article  CAS  PubMed  Google Scholar 

  12. Menendez-Gutierrez, M. P. et al. Retinoid X receptors orchestrate osteoclast differentiation and postnatal bone remodeling. J. Clin. Invest. 125, 809–823 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ricote, M. et al. Normal hematopoiesis after conditional targeting of RXRα in murine hematopoietic stem/progenitor cells. J. Leukocyte Biol. 80, 850–861 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Stanley, E. G. et al. Efficient Cre-mediated deletion in cardiac progenitor cells conferred by a 3′ UTR-ires-Cre allele of the homeobox gene Nkx2-5. Int. J. Dev. Biol. 46, 431–439 (2002).

    CAS  PubMed  Google Scholar 

  15. Moses, K. A., DeMayo, F., Braun, R. M., Reecy, J. L. & Schwartz, R. J. Embryonic expression of an Nkx2-5/Cre gene using ROSA26 reporter mice. Genesis 31, 176–180 (2001).

    Article  CAS  PubMed  Google Scholar 

  16. Schooneman, M. G., Vaz, F. M., Houten, S. M. & Soeters, M. R. Acylcarnitines: reflecting or inflicting insulin resistance? Diabetes 62, 1–8 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Koves, T. R. et al. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 7, 45–56 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Wyss, M. & Kaddurah-Daouk, R. Creatine and creatinine metabolism. Physiol. Rev. 80, 1107–1213 (2000).

    Article  CAS  PubMed  Google Scholar 

  19. Ritterhoff, J. et al. Metabolic remodeling promotes cardiac hypertrophy by directing glucose to aspartate biosynthesis. Circ. Res. 126, 182–196 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Zdzisińska, B., Żurek, A. & Kandefer-Szerszeń, M. α-Ketoglutarate as a molecule with pleiotropic activity: well-known and novel possibilities of therapeutic use. Arch. Immunol. Ther. Exp. 65, 21–36 (2017).

    Article  Google Scholar 

  21. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.21–21.29.29 (2015).

    Google Scholar 

  22. Szanto, A. et al. Retinoid X receptors: X-ploring their (patho)physiological functions. Cell Death Differ. 11, S126–S143 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. Silverman, J., Stone, D. W. & Powers, J. D. The lipid composition of milk from mice fed high or low fat diets. Lab. Anim. 26, 127–131 (1992).

    Article  CAS  PubMed  Google Scholar 

  24. Vries, J. Y. D., Pundir, S., McKenzie, E., Keijer, J. & Kussmann, M. Maternal circulating vitamin status and colostrum vitamin composition in healthy lactating women–a systematic approach. Nutrients 10, 687 (2018).

    Article  Google Scholar 

  25. Dawson, M. I. & Xia, Z. The retinoid X receptors and their ligands. Biochim. Biophys. Acta 1821, 21–56 (2012).

    Article  CAS  PubMed  Google Scholar 

  26. Oosting, A., Verkade, H. J., Kegler, D., van de Heijning, B. J. & van der Beek, E. M. Rapid and selective manipulation of milk fatty acid composition in mice through the maternal diet during lactation. J. Nutr. Sci. 4, e19 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sergeant, S., Rahbar, E. & Chilton, F. H. Gamma-linolenic acid, dihommo-gamma linolenic, eicosanoids and inflammatory processes. Eur. J. Pharmacol. 785, 77–86 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wright, S. & Bolton, C. Breast milk fatty acids in mothers of children with atopic eczema. Br. J. Nutr. 62, 693–697 (1989).

    Article  CAS  PubMed  Google Scholar 

  29. Vaidya, H. & Cheema, S. K. Breastmilk with a high omega-6 to omega-3 fatty acid ratio induced cellular events similar to insulin resistance and obesity in 3T3-L1 adipocytes. Pediatr. Obes. 13, 285–291 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Okolska, G., Ziemlański, S., Kowalska, M. & Ostojska, J. The levels of essential unsaturated fatty acids in human milk on the 3rd, 4th, 5th, and 6th days after labour. Acta Physiol. Pol. 34, 239–248 (1983).

    CAS  PubMed  Google Scholar 

  31. Lengqvist, J. et al. Polyunsaturated fatty acids including docosahexaenoic and arachidonic acid bind to the retinoid X receptor alpha ligand-binding domain. Mol. Cell Proteomics 3, 692–703 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Forman, B. M., Chen, J. & Evans, R. M. Hypolipidemic drugs, polyunsaturated fatty acids, and eicosanoids are ligands for peroxisome proliferator-activated receptors alpha and delta. Proc. Natl Acad. Sci. USA 94, 4312–4317 (1997).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Watanabe, M. & Kakuta, H. Retinoid X receptor antagonists. Int. J. Mol. Sci. 19, 2354 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Henttu, P. M., Kalkhoven, E. & Parker, M. G. AF-2 activity and recruitment of steroid receptor coactivator 1 to the estrogen receptor depend on a lysine residue conserved in nuclear receptors. Mol. Cell. Biol. 17, 1832–1839 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Niu, H. et al. Endogenous retinoid X receptor ligands in mouse hematopoietic cells. Sci. Signal. 10, eaan1011 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kastner, P. et al. Abnormal spermatogenesis in RXR beta mutant mice. Genes Dev. 10, 80–92 (1996).

    Article  CAS  PubMed  Google Scholar 

  37. Mascrez, B., Ghyselinck, N. B., Chambon, P. & Mark, M. A transcriptionally silent RXRα supports early embryonic morphogenesis and heart development. Proc. Natl Acad. Sci. USA 106, 4272–4277 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Subbarayan, V. et al. RXRα overexpression in cardiomyocytes causes dilated cardiomyopathy but fails to rescue myocardial hypoplasia in RXRα-null fetuses. J. Clin. Invest. 105, 387–394 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Merki, E. et al. Epicardial retinoid X receptor alpha is required for myocardial growth and coronary artery formation. Proc. Natl Acad. Sci. USA 102, 18455–18460 (2005).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Leone, T. C., Weinheimer, C. J. & Kelly, D. P. A critical role for the peroxisome proliferator-activated receptor alpha (PPARα) in the cellular fasting response: the PPARα-null mouse as a model of fatty acid oxidation disorders. Proc. Natl Acad. Sci. USA 96, 7473–7478 (1999).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cardoso, A. C. et al. Mitochondrial substrate utilization regulates cardiomyocyte cell-cycle progression. Nat. Metab. 2, 167–178 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Chinen, Y. et al. A novel homozygous missense SLC25A20 mutation in three CACT-deficient patients: clinical and autopsy data. Hum. Genome Var. 7, 11 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mychaleckyj, J. C. et al. Association of breast milk gamma-linolenic acid with infant anthropometric outcomes in urban, low-income Bangladeshi families: a prospective, birth cohort study. Eur. J. Clin. Nutr. 74, 698–707 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Li, M. et al. Retinoid X receptor ablation in adult mouse keratinocytes generates an atopic dermatitis triggered by thymic stromal lymphopoietin. Proc. Natl Acad. Sci. USA 102, 14795–14800 (2005).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. Cruz-Adalia, A. et al. CD69 limits the severity of cardiomyopathy after autoimmune myocarditis. Circulation 122, 1396–1404 (2010).

    Article  CAS  PubMed  Google Scholar 

  46. Ram, R., Mickelsen, D. M., Theodoropoulos, C. & Blaxall, B. C. New approaches in small animal echocardiography: imaging the sounds of silence. Am. J. Physiol. 301, H1765–H1780 (2011).

    CAS  Google Scholar 

  47. Koelmel, J. P. et al. Lipid annotator: towards accurate annotation in non-targeted liquid chromatography high-resolution tandem mass spectrometry (LC–HRMS/MS) lipidomics using a rapid and user-friendly software. Metabolites 10, 101 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tsugawa, H. et al. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. 38, 1159–1163 (2020).

    Article  CAS  PubMed  Google Scholar 

  49. Ortega-Senovilla, H., Schaefer-Graf, U. & Herrera, E. Pregnant women with gestational diabetes and with well controlled glucose levels have decreased concentrations of individual fatty acids in maternal and cord serum. Diabetologia 63, 864–874 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Alonso-Herranz, L. et al. Macrophages promote endothelial-to-mesenchymal transition via MT1-MMP/TGFβ1 after myocardial infarction. eLife 9, e57920 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Li, Z., Ross Stewart, K. M., Bruton, F. A., Denvir, M. A. & Brittan, M. in Angiogenesis: Methods and Protocols (ed. Benest, A. V.) 297–309 (Springer, 2022).

  52. Quiros, P. M., Goyal, A., Jha, P. & Auwerx, J. Analysis of mtDNA/nDNA ratio in mice. Curr. Protoc. Mouse Biol. 7, 47–54 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Fernandez-Vizarra, E., Lopez-Perez, M. J. & Enriquez, J. A. Isolation of biogenetically competent mitochondria from mammalian tissues and cultured cells. Methods 26, 292–297 (2002).

    Article  PubMed  Google Scholar 

  54. Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).

    Article  CAS  PubMed  Google Scholar 

  55. Vives-Bauza, C., Yang, L. & Manfredi, G. Assay of mitochondrial ATP synthesis in animal cells and tissues. Methods Cell. Biol. 80, 155–171 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Herrero-Mendez, A. et al. The bioenergetic and antioxidant status of neurons is controlled by continuous degradation of a key glycolytic enzyme by APC/C-Cdh1. Nat. Cell Biol. 11, 747–752 (2009).

    Article  CAS  PubMed  Google Scholar 

  57. Martinez-Bartolome, S. et al. Properties of average score distributions of SEQUEST: the probability ratio method. Mol. Cell. Proteomics 7, 1135–1145 (2008).

    Article  CAS  PubMed  Google Scholar 

  58. Navarro, P. & Vazquez, J. A refined method to calculate false discovery rates for peptide identification using decoy databases. J. Proteome Res. 8, 1792–1796 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Bonzon-Kulichenko, E., Garcia-Marques, F., Trevisan-Herraz, M. & Vazquez, J. Revisiting peptide identification by high-accuracy mass spectrometry: problems associated with the use of narrow mass precursor windows. J. Proteome Res. 14, 700–710 (2015).

    Article  CAS  PubMed  Google Scholar 

  60. Navarro, P. et al. General statistical framework for quantitative proteomics by stable isotope labeling. J. Proteome Res. 13, 1234–1247 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Garcia-Marques, F. et al. A novel systems-biology algorithm for the analysis of coordinated protein responses using quantitative proteomics. Mol. Cell. Proteomics 15, 1740–1760 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Trevisan-Herraz, M. et al. SanXoT: a modular and versatile package for the quantitative analysis of high-throughput proteomics experiments. Bioinformatics 35, 1594–1596 (2018).

    Article  PubMed Central  Google Scholar 

  63. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Article  Google Scholar 

  64. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  65. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  69. Stark, R. & Brown, G. DiffBind: differential binding analysis of ChIP–seq peak data. https://bioconductor.org/packages/devel/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf (2011).

  70. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  71. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Rey-Stolle, F. et al. Low and high resolution gas chromatography-mass spectrometry for untargeted metabolomics: a tutorial. Anal. Chim. Acta 1210, 339043 (2021).

    Article  PubMed  Google Scholar 

  75. Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20, 473 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Song, Y. et al. High-resolution comparative modeling with RosettaCM. Structure 21, 1735–1742 (2013).

    Article  CAS  PubMed  Google Scholar 

  77. Dong, R., Pan, S., Peng, Z., Zhang, Y. & Yang, J. mTM-align: a server for fast protein structure database search and multiple protein structure alignment. Nucleic Acids Res. 46, W380–w386 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Dong, R., Peng, Z., Zhang, Y. & Yang, J. mTM-align: an algorithm for fast and accurate multiple protein structure alignment. Bioinformatics 34, 1719–1725 (2018).

    Article  CAS  PubMed  Google Scholar 

  79. Conway, P., Tyka, M. D., DiMaio, F., Konerding, D. E. & Baker, D. Relaxation of backbone bond geometry improves protein energy landscape modeling. Protein Sci. 23, 47–55 (2014).

    Article  CAS  PubMed  Google Scholar 

  80. Nivón, L. G., Moretti, R. & Baker, D. A Pareto-optimal refinement method for protein design scaffolds. PLoS ONE 8, e59004 (2013).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  81. Mukherjee, S. & Zhang, Y. MM-align: a quick algorithm for aligning multiple-chain protein complex structures using iterative dynamic programming. Nucleic Acids Res. 37, e83 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Capra, J. A., Laskowski, R. A., Thornton, J. M., Singh, M. & Funkhouser, T. A. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput. Biol. 5, e1000585 (2009).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  83. Fleishman, S. J. et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS ONE 6, e20161 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  84. Egea, P. F. et al. Crystal structure of the human RXRα ligand-binding domain bound to its natural ligand: 9-cis retinoic acid. EMBO J. 19, 2592–2601 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the members of the M.R. and J.A.E laboratories, A. Hidalgo and M. Torres for extensive discussions of the paper; G. Sabio, N. Rochel and D. Metzger for mice and reagents; CNIC and CRG Genomics Units for sequencing; S. Bartlett for editorial assistance; and A.V. Alonso, L. Flores, R. Baeza, R. Santos-Clemente, C. Gifford and N. Spann for technical assistance. J.P.B. is funded by the NextGenerationEU/PRTR and Agencia Estatal de Investigación (10.13039/501100011033; PID2019-105699RB-I00; PDC2021-121013-I00, RED2018-102576-T), Instituto de Salud Carlos III (CB16/10/00282), and Junta de Castilla y León (Apoyo Regional a la Competitividad Empresarial, ICE 04/18/LE/0017 and Escalera de Excelencia CLU-2017-03). D.J.-B. And P.H.A. are recipients of a Juan de la Cierva-Incorporación contracts (IJC2020-044230-I and IJC2020-042679-I, respectively). F.J.R. is funded by the Ministerio de Ciencia e Innovación (MCIN) and European Regional Development Fund FEDER (PID2021-122490NB-I00). V.A.R.S. received funding from Airbus Defense and Space through the CLX-2 programme in partnership with Comando da Aeronautica (COMAER) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES). E.T. received funding from the Swedish Research Council (2020-01150), the Swedish Cancer Society (211582), and the Novo Nordisk Foundation (NNF20OC0063672). J.A.E. was supported by RTI2018-099357-B-I00, MCIU/AEI/ ERDF/UE; RTI2018-099357-B-I00 MCIU/AEI; PID2021-1279880B-100 MCIN/AEI/10.13039/501100011033/ERDF,UE; CB16/10/00282 CIBERFES and 17CVD04 Foundation Leducq. This work was supported by grants to M.R.: SAF2017-90604-REDT-NurCaMeIn MINECO/AEI; RTI2018-095928-BI00, MCIU/AEI/ ERDF/UE; PID2021-122552OB-I00 MCIN/AEI/10.13039/501100011033/ERDF,UE; 201605-32 Fundació La Marató de TV3; and S2017/BMD-3684 Comunidad de Madrid/ESF/EU, S2022/BMD-7227 Comunidad de Madrid; and an MINECO fellowship to A.P. (BES-2016-076632/AEI/ESF) and to R.J.-M. (PRE2019-087462). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación (MCIN), and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by MICIN/ AEI/ 10.13039/501100011033).

Author information

Authors and Affiliations

  1. Cardiovascular Regeneration Program, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

    Ana Paredes, Raquel Justo-Méndez, Vanessa Núñez, Irene Calero, María Villalba-Orero, Pablo Hernansanz-Agustín, Carmen Contreras, José Antonio Enríquez & Mercedes Ricote

  2. Institute of Functional Biology and Genomics (IBFG), University of Salamanca, CSIC, Salamanca, Spain

    Daniel Jiménez-Blasco & Juan Pedro Bolaños

  3. Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain

    Daniel Jiménez-Blasco & Juan Pedro Bolaños

  4. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain

    Daniel Jiménez-Blasco, Juan Pedro Bolaños & José Antonio Enríquez

  5. Departamento de Medicina y Cirugía Animal, Universidad Complutense de Madrid (UCM), Madrid, Spain

    María Villalba-Orero

  6. Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine (IBUB) of the University of Barcelona (UB), Barcelona, Spain

    Andrea Alegre-Martí & Eva Estébanez-Perpiñá

  7. Department of Immunology and Oncology, Centro Nacional de Biotecnología-Consejo Superior de Investigaciones Científicas (CNB/CSIC), Campus Universidad Autónoma de Madrid (UAM), Madrid, Spain

    Thierry Fischer

  8. Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain

    Ana Gradillas, Viviane Aparecida Rodrigues Sant’Anna, Francisco Javier Rupérez & Coral Barbas

  9. Bioinformatics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

    Felipe Were, Fernando Martínez & Fátima Sánchez-Cabo

  10. Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden

    Zhiqiang Huang & Eckardt Treuter

  11. CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain

    Fernando Martínez, Emilio Camafeita & Jesús Vázquez

  12. Proteomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

    Emilio Camafeita & Jesús Vázquez

  13. CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain

    Jesús Ruiz-Cabello

  14. Ikerbasque, Basque Foundation for Science, Bilbao, Spain

    Jesús Ruiz-Cabello

  15. CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain

    Jesús Ruiz-Cabello

  16. Departamento de Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense Madrid (UCM), Madrid, Spain

    Jesús Ruiz-Cabello

  17. Departament of Cellular and Molecular Biology, Centro de Investigaciones Biológicas Margarita Salas-CSIC, Madrid, Spain

    Estela Area-Gómez

  18. Department of Neurology, Columbia University Medical Campus, New York, NY, USA

    Estela Area-Gómez

Authors
  1. Ana Paredes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raquel Justo-Méndez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Jiménez-Blasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vanessa Núñez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Irene Calero
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. María Villalba-Orero
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andrea Alegre-Martí
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Thierry Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ana Gradillas
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Viviane Aparecida Rodrigues Sant’Anna
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Felipe Were
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Zhiqiang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Pablo Hernansanz-Agustín
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Carmen Contreras
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Fernando Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Emilio Camafeita
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jesús Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Jesús Ruiz-Cabello
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Estela Area-Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Fátima Sánchez-Cabo
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Eckardt Treuter
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Juan Pedro Bolaños
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Eva Estébanez-Perpiñá
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Francisco Javier Rupérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Coral Barbas
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. José Antonio Enríquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Mercedes Ricote
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: A.P. and M.R. Performed research: A.P., R.J.-M., D.J.-B., V.N., I.C., M.V.-O., A.A., T.F., A.G., V.A.R.S., Z.H., P.H.-A., C.C. and E.C. Software: F.W., F.M. and F.S.-C. Data interpretation and analysis: A.P., R.J.-M, M.V.-O., J.V., J.R.-C., E.A.-G., E.T., J.P.B., E.E.-P., F.J.R., C.B., J.A.E. and M.R. Writing, reviewing and manuscript editing: A.P., J.A.E. and M.R. Project supervision: A.P., J.A.E. and M.R. Funding: M.R.

Corresponding author

Correspondence to Mercedes Ricote.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Functional features of EDKO mice after birth.

(a) RXRs gene expression (E12.5 (n = 5), E14.5 (n = 4), E18.5 (n = 5), P1 (n = 4), P15 (n = 4) and P21 (n = 3)) in C57Bl6/J hearts. Data as means±s.e.m. (b) Rxra and Rxrb cardiac expression in EDKO (n = 3-6) and Control (n = 4-6) mice. Data as means±s.e.m. Two-way ANOVA. (c) Echocardiography parameters in EDKO (n = 9) and control (n = 11) mice (P0). Data as means±s.e.m. Two-tailed Student t test. (d) Left, Heart diameter (mm) and right, non-compacted (NC) and compacted (C) myocardial width in E18.5 EDKO (n = 9) and control (n = 8) mice. Data as means±s.e.m. Two-tailed Student t test. (e) Thickness (µm) of trabeculae (T) and compact myocardium (CM) in E18.5 EDKO (n = 9) and control (n = 8) mice. Data as means ± s.e.m. Paired Two-tailed Student t test. (f) Hematoxylin-eosin staining of EDKO and control hearts (E18.5). Scale bar = 500 µm. (g) Heart weight/body weight ratio (%HWBW) (control = 17, EDKO = 15), body weight (g) (control = 48, EDKO = 33), and heart weight (g) (control = 17, EDKO = 14). Data as means±s.e.m. Two-tailed Student t test. (h) Electrocardiography parameters in P0 EDKO (n = 10) and control (n = 5) mice. Data as means±s.e.m. Two-tailed Student t test. (i) Rxra and Rxrb expression in tissues from EDKO (n = 3-6) and control (n = 5-6) mice (P0). Data as means±s.e.m. Two-tailed Student t test. (j) Suckling score piecharts for 30 control and 15 EDKO mice. (k) Stress markers expression in control (n = 5) and EDKO (n = 6) lungs (P0). Data as means±s.e.m. Two-tailed Student t test. (l) Hematoxylin-eosin staining of EDKO and control lungs (P0). Scale bar = 500 µm. (m) Wet/Dry lung ratio from control (n = 16) and EDKO (n = 9) (P0) mice. Data as means±s.e.m. Two-tailed Student t test. (n) Mean, maximum, and minimum body temperature (ºC) in EDKO (n = 5) and control (n = 5) mice at P0. Data as means±s.e.m. Two-tailed Student t test. Exact P values in Source Data.

Source data

Extended Data Fig. 2 Cardiac mitochondrial morphology or quantity is not altered in EDKO hearts.

(a) Fatty acid-derived energy homeostasis pathway. (b) Heatmaps and hierarchical clustering of proteomics enrichment data (Zq value EDKO vs control hearts) for proteins involved in ROS metabolism, the degradome, mitochondrial dynamics, and the mitochondrial unfolded protein response (mtUPR). (c) TEM acquisition of cardiac mitochondria at P0 in control and EDKO hearts. (d) Left, Mitochondrial area normalized to the cell area in EDKO (n = 3) and control (n = 3) hearts (P0) (arbitrary units). Dots are technical replicates from 3 individual biological replicates/group. Right. Mitochondrial area in EDKO (n = 3) and control (n = 3) hearts (P0). Each dot is one mitochondrion (mean = 25-65 mitochondria/3 mice per condition). Data as means±s.e.m. Two-tailed Student t test. (e) Mitochondrial copy number (mtDNA/nDNA) in P0 EDKO (n = 8) and control (n = 12) hearts. Data as means±s.e.m. Two-tailed Student t test. (f) GC-MS volcano plot in EDKO and control hearts (P0). PAspartic acid = 0.00669, PUracil = 0.0249, PCreatinine = 0.02812, PGlycerol = 0.03959. Log2FC, log2 fold-change EDKO vs control. The dotted line indicates P value < 0.05. Dot size represents the variable importance parameter (VIP) value. Green and yellow dots represent downregulated and upregulated metabolites, respectively. (g) RT-qPCR quantification of Upp1 in cardiac tissue in EDKO (n = 3-6) and Control (n = 4-6) mice. Data as means±s.e.m. Two-way ANOVA. (h) Ex vivo rate of amino acid decarboxilation in P0 EDKO (n = 7) and control (n = 12) hearts, measured as [U-14C] amino acid conversion to 14CO2 (nmol x h−1 per mg tissue). Data as means ± s.e.m. Two-tailed Student t test. (i) a-ketoglutarate (α-KG) quantification (area corrected/signal quantification, arbitrary units) in EDKO (n = 4) and control (n = 4) P0 hearts. Data were presented as means±s.e.m. Two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Exact P values in Source Data.

Source data

Extended Data Fig. 3 RXR drives chromatin openness and histone activation of mtFAH signature genes.

(a) MA plots representing the H3K27ac ChIP-seq data (left) and ATAC-seq data (right) of changes in chromatin activation and openness in EDKO perinatal hearts, respectively. x-axis denotes the log2mean expression for each peak and y-axis indicates Log2FC EDKO vs control for each peak. Significant (adjusted P value < 0.05, Log2FC = 0.6) active/open and inactive/closed annotated genes are colored in red and blue, respectively. Two-tailed Student t test (Benjamini-Hochberg correction per gene). Top 20 genes are highlighted in each case. (b) Annotation distribution of differentially open peaks (ATAC-seq) and differentially active loci (H3K27ac ChIP-seq) in EDKO P0 hearts. Total peaks are indicated as well as the percentage of each annotation. (c) Relative RT-qPCR quantification of mtFAH genes in cardiac tissue from control (n = 5) and PPARα-null (n = 5) P0 newborns. Independent biological replicates. Data were presented as means ± s.e.m. Two-tailed Student t test. (d) Volcano plot depicting the intersection between RNA-seq and H3K27ac ChIP-seq experiments in EDKO P0 hearts. Points are plot according Log2FC and –log10(adjusted P value) in RNA-seq experiment. Two-tailed Student t test (Benjamini-Hochberg correction per gene). Color and size are plot according Log2FC and –log10(adjusted P value) in H3K27ac ChIP-seq experiment, respectively. (e) HOMER motif enrichment analysis. Top-scoring motifs in RXR ChIP-seq peaks are shown, together with P values, best-match transcription factors, type of direct repeat sequence (DR) and % of target and background sequences. (f) Annotation distribution of RXR cistrome in P0 hearts. Total peaks are indicated as well as the percentage of each annotation. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Exact P values are provided in Source Data.

Source data

Extended Data Fig. 4 Fasting and milk-borne vitamin A effects on the mtFAH gene signature.

(a) RXR and mtFAH gene expression in C57Bl6/J hearts treated with vehicle (n = 5) or bexarotene (n = 5). Data as means ± s.e.m. Two-tailed Student t test (Benjamini-Hochberg). (b) Experimental outline for studying the contribution of milk suckling. (c) RXR expression from fed (black) or fasted (green) control (n = 5-12) and EDKO (n = 4-6) hearts. Data as means±s.e.m. Two-way ANOVA (Tukey’s) (d) mtFAH gene expression from fed or fasted control (n = 5-12) and EDKO (n = 4-6) hearts (P0). Data as mean ± s.e.m. Two-way ANOVA (Tukey’s). (e) RXR and mtFAH gene expression from control (n = 10-22) and EDKO (n = 6-12) newborns (P0) suckled with NCD or vitamin-A-deficient (VAD) milk. Data as means±s.e.m. Two-way ANOVA (Tukey’s). (f) Metabolic gene expression from NCD (n = 6) or FFD (n = 11) hearts (P0). Data as mean ± s.e.m. Two-tailed Student t test. (g) RXRs gene cardiac expression from control (n = 10-17) and EDKO (n = 12-13) NCD or FFD pups. Data as means±s.e.m. Two-way ANOVA (Tukey). (h) Body weight (g) of NCD (n = 12) or FFD (n = 16) newborns (P1). Data as means±s.e.m. Two-tailed Student t test. (i) Echocardiography parameters in NCD (n = 4) and FFD (n = 6) newborns. Fractional area change (FAC, %). Data as means±s.e.m. Two-tailed Student t test. (j) Body temperature (ºC) in FFD (n = 9) and NCD (n = 5) mice (P0). Data as means±s.e.m. Two-tailed Student t test. (k) Wet/Dry lung ratio from NCD (n = 19) and FFD (n = 7) (P0) mice. Data as means±s.e.m. Two-tailed Student t test (n = 7-19 mice/condition). (l) Corrected abundance of total free FAs in NCD (n = 6) or FFD (n = 4) milk. SAFA, saturated FAs. MUFA, monounsaturated FAs. DUFA, di-unsaturated FAs. PUFA, polyunsaturated FAs. Data as means±s.e.m. Two-tailed Student t test. (m) Relative abundance of predominant SAFAs and MUFAs in NCD (n = 6) and FFD (n = 4) milk. Data as means±s.e.m. Two-tailed Student t test. Exact P values in Source Data.

Source data

Extended Data Fig. 5 GLA-RXR activates the expression of mtFAH gene signature.

(a) ω-6 fatty acids in vitro stimulation approach. (b-c) mtFAH gene expression (% of control) resulting from GLA-BSA (n = 4), LA-BSA (n = 4), and LG268 (n = 4-6) stimulation in primary neonatal cardiomyocytes (nCM) (b) or HL1 cell line (c). Data as means±s.e.m. Two-tailed Student t-test (ligand vs baseline, Benjamini-Hochberg). Representative experiment (n = 3). (d) Dose-response curves of GLA-BSA stimulation in nCM (n = 3 technical replicates). Representative experiment (n = 3). Data as means±s.e.m. Non-linear regression. (e) mtFAH gene signature expression in nCM with GLA-BSA (n = 10) and GLA-BSA +UVI3003 (n = 9). GLA as a %Control, and GLA+UVI as a %Control+UVI. Data as means±s.e.m. Two-way ANOVA (Tukey&Benjamini-Hochberg). (f) Blood glucose in NCD (n = 21), FFD (n = 56) or GLA+fat-free-diet (GLA, n = 55) newborns (P0). Data as means±s.e.m. Kruskal-Wallis test (Dunn’s). (g) Body weight of NCD (n = 34), FFD (n = 17) or GLA (n = 17) newborns. Data as means±s.e.m. One-way ANOVA (Tukey). (h) Suckling score piecharts (%) for 33 NCD, 29 FFD, 18 GLA neonates. (i) Cardiac mtFAH gene signature expression from NCD (n = 6), FFD (n = 8) or GLA (n = 21) newborns. Data as means±s.e.m. One-way ANOVA (Tukey&Benjamini-Hochberg). (j) Kaplan-Meier curve of FFD (n = 64), GLA+LA+fat-free diet milk (n = 33), GLA+fat-free-diet milk (n = 24) mice. Log-Rank test. (P < 0.00001). (k) (AOX)3-TK-driven luciferase reporter assay in HEK293.T cells. Cells transfected with: empty vector, wild-type RXRα(LBD) (n = 3) or mutated RXRα(LBD)-ΔAF2 (n = 3) and stimulated with GLA-BSA. Data as means±s.e.m. Two-way ANOVA (Tukey’s). Representative experiment (n = 3). (l) (AOX)3-TK-driven luciferase reporter assay to assess SRC1 coactivator recruitment (HEK293.T cells). Cells transfected with wild-type RXRα(LBD) (n = 3) and/or SRC1 coactivator (n = 3), and treated with GLA-BSA. Data as means±s.e.m. Two-way ANOVA (Tukey). Representative experiment (n = 3). (m) Dose-response curves of GLA-BSA in (AOX)3-TK-driven luciferase assay. HEK293.T cells were transfected with wild-type RXRα(LBD) (n = 3). Representative experiment (n = 3). Data as means±s.e.m. Non-linear regression. Exact P values in Source Data.

Source data

Supplementary information

Supplementary File 1

This file includes descriptive statistics and diagnostic plots for ‘omics data. Related to Figs. 1d–f and 3.

Reporting Summary

Supplementary Table 1

Heart lipidome EDKO versus control newborn hearts

Supplementary Table 2

Milk lipidome FFD versus NCD milk

Supplementary Table 3

In silico docking parameters

Supplementary Table 4

Primer sequences

Supplementary Video 1

Parasternal 2D long-axis echocardiography view of P0 Control newborn

Supplementary Video 2

Parasternal 2D long-axis echocardiography view of P0 EDKO pre-mortem newborn

Supplementary Video 3

Parasternal 2D long-axis echocardiography view of P1 newborns suckled with regular (NCD) milk

Supplementary Video 4

Parasternal 2D long-axis echocardiography view of P1 pre-mortem newborn suckled with milk from dams on a FFD.

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paredes, A., Justo-Méndez, R., Jiménez-Blasco, D. et al. γ-Linolenic acid in maternal milk drives cardiac metabolic maturation. Nature 618, 365–373 (2023). https://doi.org/10.1038/s41586-023-06068-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-06068-7

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing