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A lipidome landscape of aging in mice

Nature Aging (2024)Cite this article

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Abstract

Understanding the molecular mechanisms of aging is crucial for enhancing healthy longevity. We conducted untargeted lipidomics across 13 biological samples from mice at various life stages (2, 12, 19 and 24 months) to explore the potential link between aging and lipid metabolism, considering sex (male or female) and microbiome (specific pathogen-free or germ-free) dependencies. By analyzing 2,704 molecules from 109 lipid subclasses, we characterized common and tissue-specific lipidome alterations associated with aging. For example, the levels of bis(monoacylglycero)phosphate containing polyunsaturated fatty acids increased in various organs during aging, whereas the levels of other phospholipids containing saturated and monounsaturated fatty acids decreased. In addition, we discovered age-dependent sulfonolipid accumulation, absent in germ-free mice, correlating with Alistipes abundance determined by 16S ribosomal RNA gene amplicon sequencing. In the male kidney, glycolipids such as galactosylceramides, galabiosylceramides (Gal2Cer), trihexosylceramides (Hex3Cer), and mono- and digalactosyldiacylglycerols were detected, with two lipid classes—Gal2Cer and Hex3Cer—being significantly enriched in aged mice. Integrated analysis of the kidney transcriptome revealed uridine diphosphate galactosyltransferase 8A (UGT8a), alkylglycerone phosphate synthase and fatty acyl-coenzyme A reductase 1 as potential enzymes responsible for the male-specific glycolipid biosynthesis in vivo, which would be relevant to sex dependency in kidney diseases. Inhibiting UGT8 reduced the levels of these glycolipids and the expression of inflammatory cytokines in the kidney. Our study provides a valuable resource for clarifying potential links between lipid metabolism and aging.

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Fig. 1: Results of PCA for the 13 biological samples.
Fig. 2: Investigation of age-related lipid changes using OPLS-R.
Fig. 3: Overview of age-, sex-, and bacteria-dependent lipidome alterations.
Fig. 4: Fecal microbiome analysis to elucidate the relationship between bacteria and lipids.
Fig. 5: Interpretation of kidney transcriptome data.
Fig. 6: Integrated analysis of transcriptome and lipidome data.
Fig. 7: Metabolic pathways of glycolipids.
Fig. 8: Changes in lipid and mRNA expression after UGT8i administration.

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Data availability

All raw MS data are available on the RIKEN DROP Met website (http://prime.psc.riken.jp/menta.cgi/prime/drop_index) under index number DM0044. The lipidomics results are recorded in Supplementary Data 1 and can be browsed from our RIKEN lipidomics database (http://prime.psc.riken.jp/menta.cgi/lipidomics/index). The raw RNA-sequencing data are available on the DNA Data Bank of Japan (DDBJ) web page under the identifier PRJDB14285. The raw 16S rDNA amplicon sequence data are available on the DDBJ webpage under the identifier PRJDB16347. The 16S rDNA and transcriptomics results are recorded in Supplementary Data 5 and 6, respectively. Source data are provided with this paper.

References

  1. Harayama, T. & Riezman, H. Understanding the diversity of membrane lipid composition. Nat. Rev. Mol. Cell Biol. 19, 281–296 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Soehnlein, O. & Libby, P. Targeting inflammation in atherosclerosis—from experimental insights to the clinic. Nat. Rev. Drug Discov. 20, 589–610 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Snaebjornsson, M. T., Janaki-Raman, S. & Schulze, A. Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer. Cell Metab. 31, 62–76 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Huby, T. & Gautier, E. L. Immune cell-mediated features of non-alcoholic steatohepatitis. Nat. Rev. Immunol. 22, 429–443 (2022).

    Article  CAS  PubMed  Google Scholar 

  5. Baek, J., He, C., Afshinnia, F., Michailidis, G. & Pennathur, S. Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease. Nat. Rev. Nephrol. 18, 38–55 (2022).

    Article  CAS  PubMed  Google Scholar 

  6. Johnson, A. A. & Stolzing, A. The role of lipid metabolism in aging, lifespan regulation, and age-related disease. Aging Cell 18, e13048 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Khosla, S., Farr, J. N., Tchkonia, T. & Kirkland, J. L. The role of cellular senescence in ageing and endocrine disease. Nat. Rev. Endocrinol. 16, 263–275 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Ferrucci, L. & Fabbri, E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat. Rev. Cardiol. 15, 505–522 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mutlu, A. S., Duffy, J. & Wang, M. C. Lipid metabolism and lipid signals in aging and longevity. Dev. Cell 56, 1394–1407 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sacket, S. J., Chung, H. Y., Okajima, F. & Im, D. S. Increase in sphingolipid catabolic enzyme activity during aging. Acta Pharmacol. Sin. 30, 1454–1461 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mielke, M. M. et al. Serum ceramides increase the risk of Alzheimer disease: the Women’s Health and Aging Study II. Neurology 79, 633–641 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Streeper, R. S. et al. Deficiency of the lipid synthesis enzyme, DGAT1, extends longevity in mice. Aging (Albany NY) 4, 13–27 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Su, L.-J. et al. Reactive oxygen species-induced lipid peroxidation in apoptosis, autophagy, and ferroptosis. Oxid. Med. Cell. Longev. 2019, 5080843 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ponnappan, U., Holley, D. H. & Lipschitz, D. A. Effect of age on the fatty acid composition of phospholipids in human lymphocytes. Exp. Gerontol. 31, 125–133 (1996).

    Article  CAS  PubMed  Google Scholar 

  15. Rabini, R. A. et al. Reduced susceptibility to peroxidation of erythrocyte plasma membranes from centenarians. Exp. Gerontol. 37, 657–663 (2002).

    Article  CAS  PubMed  Google Scholar 

  16. Mitchell, T. W., Buffenstein, R. & Hulbert, A. J. Membrane phospholipid composition may contribute to exceptional longevity of the naked mole-rat (Heterocephalus glaber): a comparative study using shotgun lipidomics. Exp. Gerontol. 42, 1053–1062 (2007).

    Article  CAS  PubMed  Google Scholar 

  17. Nagpal, R. et al. Gut microbiome and aging: physiological and mechanistic insights. Nutr. Healthy Aging 4, 267–285 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Albouery, M. et al. Age-related changes in the gut microbiota modify brain lipid composition. Front. Cell. Infect. Microbiol. 9, 444 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Naoe, S., Tsugawa, H., Takahashi, M., Ikeda, K. & Arita, M. Characterization of lipid profiles after dietary intake of polyunsaturated fatty acids using integrated untargeted and targeted lipidomics. Metabolites 9, 241 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Weger, B. D. et al. The mouse microbiome is required for sex-specific diurnal rhythms of gene expression and metabolism. Cell Metab. 29, 362–382 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yasuda, S. et al. Elucidation of gut microbiota-associated lipids using LC–MS/MS and 16S rRNA sequence analyses. iScience 23, 101841 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ghorasaini, M. et al. Cross-laboratory standardization of preclinical lipidomics using differential mobility spectrometry and multiple reaction monitoring. Anal. Chem. 93, 16369–16378 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  24. Gonzalez-Covarrubias, V. Lipidomics in longevity and healthy aging. Biogerontology 14, 663–672 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Slade, E. et al. Age and sex are associated with the plasma lipidome: findings from the GOLDN study. Lipids Health Dis. 20, 30 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Beyene, H. B. et al. High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: evidence from two large population cohort studies. PLoS Biol. 18, e3000870 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Eum, J. Y. et al. Aging-related lipidomic changes in mouse serum, kidney, and heart by nanoflow ultrahigh-performance liquid chromatography–tandem mass spectrometry. J. Chromatogr. A 1618, 460849 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Papsdorf, K. & Brunet, A. Linking lipid metabolism to chromatin regulation in aging. Trends Cell Biol. 29, 97–116 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Pollard, A. K., Ortori, C. A., Stöger, R., Barrett, D. A. & Chakrabarti, L. Mouse mitochondrial lipid composition is defined by age in brain and muscle. Aging (Albany NY) 9, 986–998 (2017).

    Article  CAS  PubMed  Google Scholar 

  30. Ding, J. et al. A metabolome atlas of the aging mouse brain. Nat. Commun. 12, 6021 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Tan, D. et al. A class of anti-inflammatory lipids decrease with aging in the central nervous system. Nat. Chem. Biol. 19, 187–197 (2023).

    Article  CAS  PubMed  Google Scholar 

  32. Ni, Z. X., Angelidou, G., Lange, M., Hoffmann, R. & Fedorova, M. LipidHunter identifies phospholipids by high-throughput processing of LC–MS and shotgun lipidomics datasets. Anal. Chem. 89, 8800–8807 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Koelmel, J. P. et al. LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinformatics 18, 331 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Velagapudi, V. R. et al. The gut microbiota modulates host energy and lipid metabolism in mice. J. Lipid Res. 51, 1101–1112 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Grabner, G. F. et al. Metabolic regulation of the lysosomal cofactor bis(monoacylglycero)phosphate in mice. J. Lipid Res. 61, 995–1003 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Showalter, M. R. et al. The emerging and diverse roles of bis(monoacylglycero) phosphate lipids in cellular physiology and disease. Int. J. Mol. Sci. 21, 8067 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jojima, K., Edagawa, M., Sawai, M., Ohno, Y. & Kihara, A. Biosynthesis of the anti-lipid-microdomain sphingoid base 4,14-sphingadiene by the ceramide desaturase FADS3. FASEB J. 34, 3318–3335 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Pergande, M. R. et al. Lipidomic analysis identifies age-disease-related changes and potential new biomarkers in brain-derived extracellular vesicles from metachromatic leukodystrophy mice. Lipids Health Dis. 21, 32 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Slomiany, B. L., Murty, V. L., Liau, Y. H. & Slomiany, A. Animal glycoglycerolipids. Prog. Lipid Res. 26, 29–51 (1987).

    Article  CAS  PubMed  Google Scholar 

  40. Walker, A. et al. Sulfonolipids as novel metabolite markers of Alistipes and Odoribacter affected by high-fat diets. Sci. Rep. 7, 11047 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Vital, M., Rud, T., Rath, S., Pieper, D. H. & Schluter, D. Diversity of bacteria exhibiting bile acid-inducible 7α-dehydroxylation genes in the human gut. Comput. Struct. Biotechnol. J. 17, 1016–1019 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhang, Q. et al. Genetic mapping of microbial and host traits reveals production of immunomodulatory lipids by Akkermansia muciniphila in the murine gut. Nat. Microbiol. 8, 424–440 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Brejchova, K. et al. Understanding FAHFAs: from structure to metabolic regulation. Prog. Lipid Res. 79, 101053 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Patel, R. et al. ATGL is a biosynthetic enzyme for fatty acid esters of hydroxy fatty acids. Nature 606, 968–975 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wang, Y. et al. Sex differences in transcriptomic profiles in aged kidney cells of renin lineage. Aging (Albany NY) 10, 606–621 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Sembach, F. E. et al. Impact of sex on diabetic nephropathy and the renal transcriptome in UNx db/db C57BLKS mice. Physiol. Rep. 7, e14333 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Braun, F. et al. Altered lipid metabolism in the aging kidney identified by three layered omic analysis. Aging (Albany NY) 8, 441–457 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Franceschi, C., Garagnani, P., Parini, P., Giuliani, C. & Santoro, A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat. Rev. Endocrinol. 14, 576–590 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Carrero, J. J., Hecking, M., Chesnaye, N. C. & Jager, K. J. Sex and gender disparities in the epidemiology and outcomes of chronic kidney disease. Nat. Rev. Nephrol. 14, 151–164 (2018).

    Article  PubMed  Google Scholar 

  51. Zou, Z. N., Ohta, T., Miura, F. & Oki, S. ChIP-Atlas 2021 update: a data-mining suite for exploring epigenomic landscapes by fully integrating ChIP-seq, ATAC-seq and Bisulfite-seq data. Nucleic Acids Res. 50, W175–W182 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Martovetsky, G., Tee, J. B. & Nigam, S. K. Hepatocyte nuclear factors 4α and 1α regulate kidney developmental expression of drug-metabolizing enzymes and drug transporters. Mol. Pharmacol. 84, 808–823 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Chamouton, J. & Latruffe, N. PPARα/HNF4α interplay on diversified responsive elements. Relevance in the regulation of liver peroxisomal fatty acid catabolism. Curr. Drug Metab. 13, 1436–1453 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Harris, A. N., Castro, R. A., Lee, H.-W., Verlander, J. W. & Weiner, I. D. Role of the renal androgen receptor in sex differences in ammonia metabolism. Am. J. Physiol. Renal Physiol. 321, F629–F644 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. O’Brown, Z. K., Van Nostrand, E. L., Higgins, J. P. & Kim, S. K. The inflammatory transcription factors NFκB, STAT1 and STAT3 drive age-associated transcriptional changes in the human kidney. PLoS Genet. 11, e1005734 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Liu, M. et al. Androgen–STAT3 activation may contribute to gender disparity in human simply renal cysts. Int. J. Clin. Exp. Pathol. 6, 686–694 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Iida, K. et al. A possible role of vitamin D receptors in regulating vitamin D activation in the kidney. Proc. Natl Acad. Sci. USA 92, 6112–6116 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Cozzolino, M. & Malindretos, P. The role of vitamin D receptor activation in chronic kidney disease. Hippokratia 14, 7–9 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Molenaar, M. R. et al. LION/web: a web-based ontology enrichment tool for lipidomic data analysis. Gigascience 8, giz061 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Muralidharan, S. et al. A reference map of sphingolipids in murine tissues. Cell Rep. 35, 109250 (2021).

    Article  CAS  PubMed  Google Scholar 

  62. van der Bijl, P., Strous, G. J., Lopes-Cardozo, M., Thomas-Oates, J. & van Meer, G. Synthesis of non-hydroxy-galactosylceramides and galactosyldiglycerides by hydroxy-ceramide galactosyltransferase. Biochem. J. 317, 589–597 (1996).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Hayashi, T., Hayashi, E., Fujimoto, M., Sprong, H. & Su, T.-P. The lifetime of UDP-galactose:ceramide galactosyltransferase is controlled by a distinct endoplasmic reticulum-associated degradation (ERAD) regulated by sigma-1 receptor chaperones. J. Biol. Chem. 287, 43156–43169 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Eckhardt, M. Fatty acid 2-hydroxylase and 2-hydroxylated sphingolipids: metabolism and function in health and diseases. Int. J. Mol. Sci. 24, 4908 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lu, C.-L. et al. Indoxyl-sulfate-induced redox imbalance in chronic kidney disease. Antioxidants (Basel) 10, 936 (2021).

    Article  CAS  PubMed  Google Scholar 

  66. Strott, C. A. & Higashi, Y. Cholesterol sulfate in human physiology: what’s it all about? J. Lipid Res. 44, 1268–1278 (2003).

    Article  CAS  PubMed  Google Scholar 

  67. Stofan, M. & Guo, G. L. Bile acids and FXR: novel targets for liver diseases. Front. Med. (Lausanne) 7, 544 (2020).

    Article  PubMed  Google Scholar 

  68. Rouillard, A. D. et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford) 2016, baw100 (2016).

    Article  PubMed  Google Scholar 

  69. Dayama, G., Priya, S., Niccum, D. E., Khoruts, A. & Blekhman, R. Interactions between the gut microbiome and host gene regulation in cystic fibrosis. Genome Med. 12, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Ohsaka, F. et al. Gut commensals suppress interleukin-2 production through microRNA-200/BCL11B and microRNA-200/ETS-1 axes in lamina propria leukocytes of murine large intestine. Biochem. Biophys. Res. Commun. 534, 808–814 (2021).

    Article  CAS  PubMed  Google Scholar 

  71. Kolter, T. & Sandhoff, K. Lysosomal degradation of membrane lipids. FEBS Lett. 584, 1700–1712 (2010).

    Article  CAS  PubMed  Google Scholar 

  72. Johmura, Y. et al. Senolysis by glutaminolysis inhibition ameliorates various age-associated disorders. Science 371, 265–270 (2021).

    Article  CAS  PubMed  Google Scholar 

  73. Babenko, N. A., Garkavenko, V. V., Storozhenko, G. V. & Timofiychuk, O. A. Role of acid sphingomyelinase in the age-dependent dysregulation of sphingolipids turnover in the tissues of rats. Gen. Physiol. Biophys. 35, 195–205 (2016).

    Article  CAS  PubMed  Google Scholar 

  74. Medoh, U. N. et al. The Batten disease gene product CLN5 is the lysosomal bis(monoacylglycero)phosphate synthase. Science 381, 1182–1189 (2023).

    Article  CAS  PubMed  Google Scholar 

  75. Parker, B. J., Wearsch, P. A., Veloo, A. C. M. & Rodriguez-Palacios, A. The genus Alistipes: gut bacteria with emerging implications to inflammation, cancer, and mental health. Front. Immunol. 11, 906 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Almsherqi, Z. A. Potential role of plasmalogens in the modulation of biomembrane morphology. Front. Cell Dev. Biol. 9, 673917 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Tadano-Aritomi, K. et al. Kidney lipids in galactosylceramide synthase-deficient mice: absence of galactosylsulfatide and compensatory increase in more polar sulfoglycolipids. J. Lipid Res. 41, 1237–1243 (2000).

    Article  CAS  PubMed  Google Scholar 

  78. Honke, K. et al. Paranodal junction formation and spermatogenesis require sulfoglycolipids. Proc. Natl Acad. Sci. USA 99, 4227–4232 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Stormo, G. D. Modeling the specificity of protein–DNA interactions. Quant. Biol. 1, 115–130 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Thelen, A. M. & Zoncu, R. Emerging roles for the lysosome in lipid metabolism. Trends Cell Biol. 27, 833–850 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Yu, Z. et al. Human serum metabolic profiles are age dependent. Aging Cell 11, 960–967 (2012).

    Article  CAS  PubMed  Google Scholar 

  82. Montoliu, I. et al. Serum profiling of healthy aging identifies phospho- and sphingolipid species as markers of human longevity. Aging (Albany NY) 6, 9–25 (2014).

    Article  CAS  PubMed  Google Scholar 

  83. Jarrell, Z. R. et al. Plasma acylcarnitine levels increase with healthy aging. Aging (Albany NY) 12, 13555–13570 (2020).

    Article  CAS  PubMed  Google Scholar 

  84. Folz, J. et al. Human metabolome variation along the upper intestinal tract. Nat. Metab. 5, 777–788 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Akiyama, H. et al. Galabiosylceramide is present in human cerebrospinal fluid. Biochem. Biophys. Res. Commun. 536, 73–79 (2021).

    Article  CAS  PubMed  Google Scholar 

  86. Nowak, A., Beuschlein, F., Sivasubramaniam, V., Kasper, D. & Warnock, D. G. Lyso-Gb3 associates with adverse long-term outcome in patients with Fabry disease. J. Med. Genet. 59, 287–293 (2022).

    Article  CAS  PubMed  Google Scholar 

  87. Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).

    Article  CAS  PubMed  Google Scholar 

  88. Tsugawa, H., Rai, A., Saito, K. & Nakabayashi, R. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat. Prod. Rep. 38, 1729–1759 (2021).

    Article  CAS  PubMed  Google Scholar 

  89. McDonald, J. G. et al. Introducing the Lipidomics Minimal Reporting Checklist. Nat. Metab. 4, 1086–1088 (2022).

    Article  PubMed  Google Scholar 

  90. Okahashi, N., Ueda, M., Yasuda, S., Tsugawa, H. & Arita, M. Global profiling of gut microbiota-associated lipid metabolites in antibiotic-treated mice by LC–MS/MS-based analyses. STAR Protoc. 2, 100492 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. da Costa, E., Amaro, H. M., Melo, T., Guedes, A. C. & Domingues, M. R. Screening for polar lipids, antioxidant, and anti-inflammatory activities of Gloeothece sp. lipid extracts pursuing new phytochemicals from cyanobacteria. J. Appl. Phycol. 32, 3015–3030 (2020).

    Article  Google Scholar 

  92. Moore, E. K. et al. Novel mono-, di-, and trimethylornithine membrane lipids in northern wetland planctomycetes. Appl. Environ. Microbiol. 79, 6874–6884 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Guo, L., Amarnath, V. & Davies, S. S. A liquid chromatography–tandem mass spectrometry method for measurement of N-modified phosphatidylethanolamines. Anal. Biochem. 405, 236–245 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Munger, L. H., Boulos, S. & Nystrom, L. UPLC–MS/MS based identification of dietary steryl glucosides by investigation of corresponding free sterols. Front. Chem. 6, 342 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Kessner, D., Chambers, M., Burke, R., Agus, D. & Mallick, P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534–2536 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Kato, T. et al. Multiple omics uncovers host–gut microbial mutualism during prebiotic fructooligosaccharide supplementation. DNA Res. 21, 469–480 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Maki, K. A., Wolff, B., Varuzza, L., Green, S. J. & Barb, J. J. Multi-amplicon microbiome data analysis pipelines for mixed orientation sequences using QIIME2: assessing reference database, variable region and pre-processing bias in classification of mock bacterial community samples. PLoS ONE 18, e0280293 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by the Japan Science and Technology Agency (JST) ERATO ‘Arita Lipidome Atlas Project’ (JPMJER2101 to H.T. and M.A.), RIKEN Aging Project (M.A. and A.M.), Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research on Innovative Areas ‘Biology of LipoQuality’ (15H05897 and 15H05898 to M.A.), JSPS KAKENHI (21K18216 to H.T., 22K11718 to T.I., 20H00495 to M.A.), National Cancer Center Research and Development Fund (2020-A-9 to H.T.), AMED Moonshot Research and Development Program (JP22zf0127007 to M.A.), AMED NEDDTrim (22ae0121036h0002 to M.A.), AMED Japan Program for Infectious Diseases Research and Infrastructure (21wm0325036h0001 to H.T. and M.A.), AMED Brain/MINDS (JP15dm0207001 to H.T.), JST National Bioscience Database Center (JPMJND2305 to H.T.) and Takeda Science Foundation (M.A.). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Hiroshi Tsugawa, Tomoaki Ishihara, Aya Hori & Makoto Arita

  2. Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Tokyo, Japan

    Hiroshi Tsugawa

  3. Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan

    Hiroshi Tsugawa, Mikiko Takahashi & Yutaka Yamada

  4. Molecular and Cellular Epigenetics Laboratory, Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan

    Hiroshi Tsugawa & Makoto Arita

  5. Department of Pharmacy, Nagasaki International University, Sasebo, Japan

    Tomoaki Ishihara

  6. Division of Physiological Chemistry and Metabolism, Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan

    Kota Ogasa, Seigo Iwanami & Makoto Arita

  7. Laboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Naoko Satoh-Takayama & Hiroshi Ohno

  8. Laboratory for Cellular Epigenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Aki Minoda

  9. Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands

    Aki Minoda

  10. Human Biology-Microbiome-Quantum Research Center (WPI-Bio2Q), Keio University, Tokyo, Japan

    Makoto Arita

Authors
  1. Hiroshi Tsugawa
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  2. Tomoaki Ishihara
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  3. Kota Ogasa
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  4. Seigo Iwanami
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  5. Aya Hori
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  6. Mikiko Takahashi
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  7. Yutaka Yamada
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  8. Naoko Satoh-Takayama
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  9. Hiroshi Ohno
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  10. Aki Minoda
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  11. Makoto Arita
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Contributions

H.T., T.I., A.M. and M.A. designed the study. H.T. and M.T. developed an MS-DIAL annotation system. T.I. prepared the biological samples, and T.I. and A.H. performed the LC–MS experiments. K.O. and S.I. performed the kidney transcriptome analysis. N.S.-T. and H.O. performed the microbiome analysis. Y.Y. developed the RIKEN lipidomics database. H.T. and M.A. wrote the paper. All authors have thoroughly discussed this project and helped improve the paper.

Corresponding authors

Correspondence to Hiroshi Tsugawa or Makoto Arita.

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The authors declare no competing interests.

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Nature Aging thanks Naama Geva-Zatorsky, Marcelo Mori, Martin Denzel 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 Summary of experimental design and lipid profiling in this study.

(a) Four types of mice were prepared: male and SPF (specific pathogen-free), male and GF (germ free), female and SPF, and female and GF. Total 13 biospecimens were harvested at 2 months, 12 months, 19 months, and 24 months. (b) The lipid extraction was performed for the optimal volume of biological samples. The untargeted lipidomics data was obtained using our experimental condition. The mass spectrometry data was analyzed by the MS-DIAL 4.20 algorithm with the updated lipid libraries. Panel a icons from iStock.

Source data

Extended Data Fig. 2 Lipid profiling result of AIN-93M chow.

The x- and y-axis show the lipid subclass name and the log10 transformed value of normalized peak height. Each dot denotes each lipid molecule. The elements of the box plot are defined as follows: center line, median; box limits, upper and lower quartiles; and whiskers, 1.5x interquartile range. The number of molecules included in each lipid subclass is described in Supplementary Data 1.

Extended Data Fig. 3 Relationship between fecal microbiome and lipidome.

(a) The scatter plot of principal coordinates analysis (PCoA) using the unweighted UniFrac distance. The sky- and dark-blue colors denote 2- and 24-month-old mice, respectively. The circle and diamond shapes denote male and female, respectively. (b) The correlations between Alistipes and sulfonolipid (SL). The 95% confidence interval is also described by the gray color. The correlation coefficient test was performed to calculate the p-value (two-sided). (c) Bacteria relative abundances between aged- and young mice. The left- and right panels show the results from 16S rRNA and qPCR data, respectively. Student t-test was used to calculate the p-value (two-sided). N = 6 biologically independent samples where the results of male and female mice are recognized in the same group to calculate the p-value.

Source data

Extended Data Fig. 4 The significantly changed lipids and their related metabolic pathways in kidney tissue.

(a) The metabolic pathway of ether-linked (alkylacyl) glycerolipids and glycerophospholipids. The level of ether PE is calculated from the molecules annotated as plasmalogen type in the positive ion mode. (b) The significant genes related to the alkylacyl glycerolipids, and the other UGT genes, which were expected to be related to glycosyl lipid metabolism. The definition of symbol and color in the dot plot is the same as in Fig. 2. The p-value was calculated by Dunnett’s test (two-sided). *P < 0.05, **P < 0.01, and ***P < 0.001 against 2 months. N = 6 biologically independent samples where the results of SPF and GF mice are recognized in the same group.

Extended Data Fig. 5 The correlation of genes with the lipid metabolites associated with gut microbiota.

The definitions of statistical significance with months (aging), SPF/GF, and gene-lipid correlation are the same as those in Fig. 6. For phosphatidylcholine (PC), the molecules containing 17:0 and 17:1 were investigated even if several molecules were not included in the lipid clusters. The yellow color of SPF/GF means the significance. Several bile acids were identified by authentic standards. Dot plots indicate the significantly changed genes only in SPF mice with aging. The p-value was calculated by Dunnett’s test (two-sided). *P < 0.05, **P < 0.01 against 2 months. N = 6 biologically independent samples where the results of male and female mice are recognized in the same group.

Source data

Supplementary information

Supplementary Information

Legends of Supplementary Tables, legends of Supplementary Data, and Supplementary Note showing the Lipidomics Minimal Reporting Checklist.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8. The legends are also listed in the Supplementary Information.

Supplementary Data 1

Lipidome results of biological samples.

Supplementary Data 2

Annotation results from LipidHunter and LipidMatch.

Supplementary Data 3

MS/MS spectral annotation for odd-chain fatty acid-containing lipids and oxidized phospholipids.

Supplementary Data 4

VIP values generated in OPLS-R.

Supplementary Data 5

Fecal microbiome results.

Supplementary Data 6

Kidney transcriptome results.

Source data

Source Data Fig. 3

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Source Data Fig. 6

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Source Data Extended Data Fig. 1

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Source Data Extended Data Fig. 3

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Source Data Extended Data Fig. 5

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Tsugawa, H., Ishihara, T., Ogasa, K. et al. A lipidome landscape of aging in mice. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00610-6

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