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A class of anti-inflammatory lipids decrease with aging in the central nervous system

Nature Chemical Biology volume 19, pages 187–197 (2023)Cite this article

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Abstract

Lipids contribute to the structure, development, and function of healthy brains. Dysregulated lipid metabolism is linked to aging and diseased brains. However, our understanding of lipid metabolism in aging brains remains limited. Here we examined the brain lipidome of mice across their lifespan using untargeted lipidomics. Co-expression network analysis highlighted a progressive decrease in 3-sulfogalactosyl diacylglycerols (SGDGs) and SGDG pathway members, including the potential degradation products lyso-SGDGs. SGDGs show an age-related decline specifically in the central nervous system and are associated with myelination. We also found that an SGDG dramatically suppresses LPS-induced gene expression and release of pro-inflammatory cytokines from macrophages and microglia by acting on the NF-κB pathway. The detection of SGDGs in human and macaque brains establishes their evolutionary conservation. This work enhances interest in SGDGs regarding their roles in aging and inflammatory diseases and highlights the complexity of the brain lipidome and potential biological functions in aging.

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Fig. 1: Global lipidomics of mouse brain during aging.
Fig. 2: Discovery of a class of lipids that progressively decrease during aging.
Fig. 3: Quantification of SGDGs in mouse tissues.
Fig. 4: SGDGs are present in human.
Fig. 5: Lyso-SGDGs are the potential degradation products of SGDGs in the brain.
Fig. 6: SGDG exhibits anti-inflammatory effects via the NF-κB pathway.

Data availability

The lipidomics raw data and metadata have been deposited into MassIVE public repository with the accession number MSV000090179 (https://doi.org/10.25345/C59Z90G8H). LipidBlast, Lipid MAPS, and METLIN are available at https://fiehnlab.ucdavis.edu/projects/lipidblast, https://www.lipidmaps.org/, and https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage, respectively. Source data are provided with this paper.

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Acknowledgements

We thank members of the Saghatelian laboratory for helpful advice and discussions; R. Rissman for providing the human hippocampus sample; and J. Ning for providing the anti-PLP antibody. This research was supported by Ferring Pharmaceuticals and Frederik Paulsen (D.S. and A.S.), NIH (P30 CA014195, R01DK106210, A.S.), the Wu Tsai Human Performance Alliance (A.S), ONPRC (P51 OD 010092, S.G.K.), the Anderson Foundation (D.T.), the Bruce Ford and Anne Smith Bundy Foundation (D.T.), Pioneer Fellowship (D.T.), NIH (R01NS119823, J.B.Z.), the Howard Hughes Medical Institute (A.D.), CZI Neurodegeneration Network (N.J.A.), FAPESP (2017/01184-9, M.A.M.), and NIH (R01AG069206, RF1AG061296, P.M.)

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Author notes

  1. These authors contributed equally: Meric Erikci Ertunc, Pan Zhang.

Authors and Affiliations

  1. Clayton Foundation Laboratories for Peptide Biology, Salk Institute for Biological Studies, La Jolla, CA, USA

    Dan Tan, Meric Erikci Ertunc, Justin Wang, Tina Chang, Antonio F. M. Pinto, Andrea Rocha, Cynthia J. Donaldson, Joan M. Vaughan, Peter C. Gray & Alan Saghatelian

  2. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA

    Srihari Konduri & Dionicio Siegel

  3. Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Pan Zhang

  4. Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil

    Raissa G. Ludwig & Marcelo A. Mori

  5. Department of Molecular and Cellular Biology, Howard Hughes Medical Institute, The Glenn Center for Aging Research, University of California, Berkeley, Berkeley, CA, USA

    Elizabeth Willey & Andrew Dillin

  6. Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA

    Manasi Iyer & J. Bradley Zuchero

  7. Cellular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

    Pamela Maher

  8. Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

    Nicola J. Allen

  9. Oregon National Primate Research Center, Oregon Health and Science University, Portland, OR, USA

    Steven G. Kohama

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  1. Dan Tan
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Contributions

D.T. and A.S. conceived the project, designed the experiments, and analyzed and interpreted the data. D.T. performed the lipidomics, LC–MS/MS, biochemical, and cell-based experiments. S.K. synthesized lipid standards. M.E.E. collected mouse tissues. P.Z. performed the co-expression network analysis. J.W. extracted lipids and collected mouse tissues. T.C. performed cell-based experiments. A.F.M.P. contributed to LC–MS/MS analysis. A.R. prepared BMDMs. C.J.D. and J.M.V. performed the NF-κB luciferase assay. R.G.L. and M.A.M. provided the calorie restriction samples. E.W. and A.D. provided the 29-month-old and 5-month-old mouse tissues. M.I. and J.B.Z. provided OPCs and differentiated oligodendrocytes. P.M. contributed to the BV2 experiments. N.J.A. contributed to the shiverer mouse experiments. S.G.K. provided macaque tissues. D.T., S.K., D.S., and A.S. wrote the manuscript. P.C.G., P.Z., M.E.E., P.M., and S.G.K. edited the manuscript. A.S. and D.S. supervised the project.

Corresponding authors

Correspondence to Dionicio Siegel or Alan Saghatelian.

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Extended data

Extended Data Fig. 1 Average levels of the co-expressed lipids in the 23 modules across different ages.

n = 4 mice/group. Values are expressed with box plots. Boxes represent 25th to 75th percentile. Center lines represent the median values. Whiskers represent 1.5× interquartile range.

Extended Data Fig. 2 Association between module eigenlipid and age.

Blue line represents fitted linear regression line between module eigenlipid and age. Pink shaded area represents 95% confidence interval of the regression line. Modules that are significantly associated with age are highlighted with red boxes.

Extended Data Fig. 3 Quantification of SGDGs in the brain of mice with three months of calorie restriction.

Sex, male; age, 6 months. n = 8 mice/group, data are means ± SEM. *P < 0.05 versus ad libitum-fed mice. P values were 0.0217, 0.0155, 0.0276, 0.0453, 0.0410, 0.0243, 0.2387, 0.0694, 0.1417, 0.1103, 0.0423, 0.0451 (left to right). Statistical significance was calculated using unpaired, two-tailed Student’s t-test with equal variance.

Extended Data Fig. 4 Fractionation of the mouse brain lysate into membrane and soluble fractions revealed that SGDGs are largely membrane-associated.

Bar graph shows SGDG levels relative to the total lysate. n = 3, data are means ± SEM.

Extended Data Fig. 5 Quantification of SGAAGs in mouse tissues.

(a) Quantification of SGAAGs in the brain and spinal cord of WT female mice at different ages. n = 4 mice/group, data are means ± SEM. (b) Quantification of SGAAGs in the brain, spinal cord, and testis of WT male mice at different ages. Data were collected from six 3-month-old, three 7-month-old, and six 22-month-old mice. Data are means ± SEM.

Extended Data Fig. 6 SGDGs are enriched in myelin.

(a) Quantification of sulfatides in purified mouse brain myelin. n = 3 mice, data are means ± SEM. (b) Validation of myelin purification. Immunoblot shows that myelin proteins, PLP and MBP, are enriched in purified rat brain myelin compared with the brain lysate, whereas the abundant neuronal protein synaptophysin is depleted. (c) Quantification of SGDGs and sulfatides in purified rat brain myelin. n = 2 technical replicates, data are means ± SEM.

Source data

Extended Data Fig. 7

Quantification of SGDGs in oligodendrocyte precursor cells and myelinated oligodendrocytes at day 5 of differentiation.

Extended Data Fig. 8 Quantification of SGDGs in macaque and human brains.

(a) Extracted ion chromatograms of SGDG(14:0_16:0) from the parietal cortex of a 6-year-old male macaque and the internal standard SGDG(13C16-16:0/14:0). (b) Quantification of SGDGs in the parietal cortex of a 6-year-old male macaque. (c) Quantification of SGDGs in a frontal brain sample collected postmortem from a 5-month-old male subject.

Extended Data Fig. 9

Expression profile of MGDGs, SGAAGs, SGDGs, MGMGs, and lyso-SGDGs in the spinal cord module which shows progressive decrease during aging.

Extended Data Fig. 10 SGDG exhibits anti-inflammatory effects in RAW 264.7 cells and BV2 cells.

(a) RAW 264.7 cells were incubated with media alone, 100 ng/mL of LPS, or co-treated with 100 ng/mL of LPS and 5 μM of SGDG(14:0/16:0) for 4 hours. mRNA levels were determined by RT-PCR. n = 4 replicates. P values were 3.24e-13, 9.54e-11, 3.44e-9, 3.22e-7, 9.35e-11, 5.41e-7, 1.36e-10, 0.00004, 7.10e-10, 0.00001, 2.17e-6, 0.06078 (left to right, top to bottom). (b) BV2 cells were incubated with media alone, 100 ng/mL of LPS, or co-treated with 100 ng/mL of LPS and 25 μM of SGDG(14:0/16:0) or dexamethasone (Dex) for 24 hours. IL-6 and TNF-α levels in the media were measured by ELISA and normalized to cell viability. n = 3 replicates. P values were 0, 0, 0, 0, 0.00005, 0.00038 (left to right). In a, b, data are means ± SEM. *P < 0.05 versus LPS. Statistical significance was calculated using one-way ANOVA followed by post hoc Dunnett’s test to correct for multiple comparisons.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Supplementary Table 1, and Supplementary Note.

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Source Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 6

Unprocessed western blots.

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Tan, D., Konduri, S., Erikci Ertunc, M. et al. A class of anti-inflammatory lipids decrease with aging in the central nervous system. Nat Chem Biol 19, 187–197 (2023). https://doi.org/10.1038/s41589-022-01165-6

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