Cardiovascular disease (CVD) is the leading cause of mortality in the world, with most CVD-related deaths resulting from myocardial infarction or stroke. The main underlying cause of thrombosis and cardiovascular events is atherosclerosis, an inflammatory disease that can remain asymptomatic for long periods. There is an urgent need for therapeutic and diagnostic options in this area. Atherosclerotic plaques contain autoantibodies1,2, and there is a connection between atherosclerosis and autoimmunity3. However, the immunogenic trigger and the effects of the autoantibody response during atherosclerosis are not well understood3,4,5. Here we performed high-throughput single-cell analysis of the atherosclerosis-associated antibody repertoire. Antibody gene sequencing of more than 1,700 B cells from atherogenic Ldlr−/− and control mice identified 56 antibodies expressed by in-vivo-expanded clones of B lymphocytes in the context of atherosclerosis. One-third of the expanded antibodies were reactive against atherosclerotic plaques, indicating that various antigens in the lesion can trigger antibody responses. Deep proteomics analysis identified ALDH4A1, a mitochondrial dehydrogenase involved in proline metabolism, as a target antigen of one of these autoantibodies, A12. ALDH4A1 distribution is altered during atherosclerosis, and circulating ALDH4A1 is increased in mice and humans with atherosclerosis, supporting the potential use of ALDH4A1 as a disease biomarker. Infusion of A12 antibodies into Ldlr−/− mice delayed plaque formation and reduced circulating free cholesterol and LDL, suggesting that anti-ALDH4A1 antibodies can protect against atherosclerosis progression and might have therapeutic potential in CVD.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Antibody sequences are available from ENA under accession PRJEB34262. Proteomics data are available on Peptide Atlas ftp://PASS01505:VE2555mc@ftp.peptideatlas.org/ and ftp://PASS01607:CP3575gq@ftp.peptideatlas.org/. Lipidomics data are available at the NMDR website (https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Project&ProjectID=PR000985), project ID PR000985. Source data are provided with this paper.
Hansson, G. K., Bondjers, G., Bylock, A. & Hjalmarsson, L. Ultrastructural studies on the localization of IgG in the aortic endothelium and subendothelial intima of atherosclerotic and nonatherosclerotic rabbits. Exp. Mol. Pathol. 33, 302–315 (1980).
Parums, D. & Mitchinson, M. J. Demonstration of immunoglobulin in the neighbourhood of advanced atherosclerotic plaques. Atherosclerosis 38, 211–216 (1981).
Sage, A. P., Tsiantoulas, D., Binder, C. J. & Mallat, Z. The role of B cells in atherosclerosis. Nat. Rev. Cardiol. 16, 180–196 (2019).
Tsiantoulas, D., Diehl, C. J., Witztum, J. L. & Binder, C. J. B cells and humoral immunity in atherosclerosis. Circ. Res. 114, 1743–1756 (2014).
Gisterå, A. & Hansson, G. K. The immunology of atherosclerosis. Nat. Rev. Nephrol. 13, 368–380 (2017).
Hansson, G. K. Inflammation, atherosclerosis, and coronary artery disease. N. Engl. J. Med. 352, 1685–1695 (2005).
Lusis, A. J. Atherosclerosis. Nature 407, 233–241 (2000).
Reardon, C. A. et al. Effect of immune deficiency on lipoproteins and atherosclerosis in male apolipoprotein E-deficient mice. Arterioscler. Thromb. Vasc. Biol. 21, 1011–1016 (2001).
Song, L., Leung, C. & Schindler, C. Lymphocytes are important in early atherosclerosis. J. Clin. Invest. 108, 251–259 (2001).
Hansson, G. K. & Hermansson, A. The immune system in atherosclerosis. Nat. Immunol. 12, 204–212 (2011).
Caligiuri, G., Nicoletti, A., Poirier, B. & Hansson, G. K. Protective immunity against atherosclerosis carried by B cells of hypercholesterolemic mice. J. Clin. Invest. 109, 745–753 (2002).
Ait-Oufella, H. et al. B cell depletion reduces the development of atherosclerosis in mice. J. Exp. Med. 207, 1579–1587 (2010).
Karvonen, J., Päivänsalo, M., Kesäniemi, Y. A. & Hörkkö, S. Immunoglobulin M type of autoantibodies to oxidized low-density lipoprotein has an inverse relation to carotid artery atherosclerosis. Circulation 108, 2107–2112 (2003).
Tsimikas, S. et al. Relationship of IgG and IgM autoantibodies to oxidized low density lipoprotein with coronary artery disease and cardiovascular events. J. Lipid Res. 48, 425–433 (2007).
Tay, C. et al. Follicular B cells promote atherosclerosis via T cell-mediated differentiation into plasma cells and secreting pathogenic immunoglobulin G. Arterioscler. Thromb. Vasc. Biol. 38, e71–e84 (2018).
Centa, M. et al. Germinal center-derived antibodies promote atherosclerosis plaque size and stability. Circulation 139, 2466–2482 (2019).
Palinski, W. et al. Cloning of monoclonal autoantibodies to epitopes of oxidized lipoproteins from apolipoprotein E-deficient mice. Demonstration of epitopes of oxidized low density lipoprotein in human plasma. J. Clin. Invest. 98, 800–814 (1996).
Fredrikson, G. N. et al. Identification of immune responses against aldehyde-modified peptide sequences in apoB associated with cardiovascular disease. Arterioscler. Thromb. Vasc. Biol. 23, 872–878 (2003).
Chou, M. Y. et al. Oxidation-specific epitopes are dominant targets of innate natural antibodies in mice and humans. J. Clin. Invest. 119, 1335–1349 (2009).
Binder, C. J., Papac-Milicevic, N. & Witztum, J. L. Innate sensing of oxidation-specific epitopes in health and disease. Nat. Rev. Immunol. 16, 485–497 (2016).
Witztum, J. L. & Lichtman, A. H. The influence of innate and adaptive immune responses on atherosclerosis. Annu. Rev. Pathol. 9, 73–102 (2014).
Que, X. et al. Oxidized phospholipids are proinflammatory and proatherogenic in hypercholesterolaemic mice. Nature 558, 301–306 (2018).
Victora, G. D. & Nussenzweig, M. C. Germinal centers. Annu. Rev. Immunol. 30, 429–457 (2012).
Shaw, P. X. et al. Natural antibodies with the T15 idiotype may act in atherosclerosis, apoptotic clearance, and protective immunity. J. Clin. Invest. 105, 1731–1740 (2000).
Tsimikas, S. & Witztum, J. L. Measuring circulating oxidized low-density lipoprotein to evaluate coronary risk. Circulation 103, 1930–1932 (2001).
Busse, C. E., Czogiel, I., Braun, P., Arndt, P. F. & Wardemann, H. Single-cell based high-throughput sequencing of full-length immunoglobulin heavy and light chain genes. Eur. J. Immunol. 44, 597–603 (2014).
Yoshida, A., Rzhetsky, A., Hsu, L. C. & Chang, C. Human aldehyde dehydrogenase gene family. Eur. J. Biochem. 251, 549–557 (1998).
Hanna, V. S. & Hafez, E. A. A. Synopsis of arachidonic acid metabolism: A review. J. Adv. Res. 11, 23–32 (2018).
Sonnweber, T., Pizzini, A., Nairz, M., Weiss, G. & Tancevski, I. Arachidonic acid metabolites in cardiovascular and metabolic diseases. Int. J. Mol. Sci. 19, 3285 (2018).
Hoefer, I. E. et al. Novel methodologies for biomarker discovery in atherosclerosis. Eur. Heart J. 36, 2635–2642 (2015).
Kavurma, M. M., Rayner, K. J. & Karunakaran, D. The walking dead: macrophage inflammation and death in atherosclerosis. Curr. Opin. Lipidol. 28, 91–98 (2017).
Bäck, M., Yurdagul, A., Jr, Tabas, I., Öörni, K. & Kovanen, P. T. Inflammation and its resolution in atherosclerosis: mediators and therapeutic opportunities. Nat. Rev. Cardiol. 16, 389–406 (2019).
Martínez-López, D. et al. Complement C5 protein as a marker of subclinical atherosclerosis. J. Am. Coll. Cardiol. 75, 1926–1941 (2020).
Murugan, R., Imkeller, K., Busse, C. E. & Wardemann, H. Direct high-throughput amplification and sequencing of immunoglobulin genes from single human B cells. Eur. J. Immunol. 45, 2698–2700 (2015).
Imkeller, K., Arndt, P. F., Wardemann, H. & Busse, C. E. sciReptor: analysis of single-cell level immunoglobulin repertoires. BMC Bioinformatics 17, 67 (2016).
Rubelt, F. et al. Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data. Nat. Immunol. 18, 1274–1278 (2017).
Tiller, T., Busse, C. E. & Wardemann, H. Cloning and expression of murine Ig genes from single B cells. J. Immunol. Methods 350, 183–193 (2009).
Bothwell, A. L. et al. Heavy chain variable region contribution to the NPb family of antibodies: somatic mutation evident in a γ2a variable region. Cell 24, 625–637 (1981).
Wardemann, H. et al. Predominant autoantibody production by early human B cell precursors. Science 301, 1374–1377 (2003).
Villarroya-Beltri, C. et al. Sumoylated hnRNPA2B1 controls the sorting of miRNAs into exosomes through binding to specific motifs. Nat. Commun. 4, 2980 (2013).
Martínez-Bartolomé, S. et al. Properties of average score distributions of SEQUEST: the probability ratio method. Mol. Cell. Proteomics 7, 1135–1145 (2008).
Navarro, P. & Vázquez, J. A refined method to calculate false discovery rates for peptide identification using decoy databases. J. Proteome Res. 8, 1792–1796 (2009).
Bonzon-Kulichenko, E., Garcia-Marques, F., Trevisan-Herraz, M. & Vázquez, 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).
Wiśniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).
Navarro, P. et al. General statistical framework for quantitative proteomics by stable isotope labeling. J. Proteome Res. 13, 1234–1247 (2014).
García-Marqués, 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).
Trevisan-Herraz, M. et al. SanXoT: a modular and versatile package for the quantitative analysis of high-throughput proteomics experiments. Bioinformatics 35, 1594–1596 (2019).
Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).
Matyash, V., Liebisch, G., Kurzchalia, T. V., Shevchenko, A. & Schwudke, D. Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J. Lipid Res. 49, 1137–1146 (2008).
Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).
Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinformatics 68, e86 (2019).
Gil-de-la-Fuente, A. et al. CEU Mass Mediator 3.0: a metabolite annotation tool. J. Proteome Res. 18, 797–802 (2019).
Fahy, E. et al. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50 (Suppl), S9–S14 (2009).
Fahy, E. et al. A comprehensive classification system for lipids. J. Lipid Res. 46, 839–862 (2005).
Liebisch, G. et al. Shorthand notation for lipid structures derived from mass spectrometry. J. Lipid Res. 54, 1523–1530 (2013).
Venegas-Pino, D. E., Banko, N., Khan, M. I., Shi, Y. & Werstuck, G. H. Quantitative analysis and characterization of atherosclerotic lesions in the murine aortic sinus. J. Vis. Exp. 82, 50933 (2013).
We thank all members of the B Cell Biology Laboratory for useful discussions; M. C. Nussenzweig, J. C. Escola-Gil, D. Sancho and V. G. de Yébenes for critical reading of the manuscript; S. Bartlett for English editing; V. Labrador for help with microscopy and image analysis; R. Tarifa for help with metabolomics/lipidomics analysis; and D. Sancho for providing plasma samples from Apoe−/− HFD mice. C.L. was a fellow of the research training program funded by Ministerio de Economía y Competitividad (SVP-2014-068289); P.D. was supported by an AECC grant (AIO 2012, Ayudas a Investigadores en Oncología 2012); A.S.-B. is a Juan de la Cierva researcher (IJC2018-035279-I); I.M.-F. was a fellow of the research training program funded by Ministerio de Economía y Competitividad (SVP-2014-068216); and A.R.R. and J.V. are supported by Centro Nacional de Investigaciones Cardiovasculares (CNIC). The project leading to these results has received funding from la Caixa Banking Foundation under the project code HR17-00247 and from SAF2016-75511-R and PID2019-106773RB-I00 grants to A.R.R. (Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016 Programa Estatal de I+D+i Orientada a los Retos de la Sociedad Retos Investigación: Proyectos I+D+i 2016, Ministerio de Economía, Industria y Competitividad) and co-funding by Fondo Europeo de Desarrollo Regional (FEDER) and by projects PGC2018-097019-B-I00 from the Ministerio de Ciencia, Innovación y Universidades and PRB3 (IPT17/0019 - ISCIII-SGEFI/ERDF, ProteoRed) from the Carlos III Institute of Health-Fondo de Investigación Sanitaria to J.V. The CNIC is supported by the Ministerio de Economía, Industria y Competitividad (MEIC) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).
An international patent application entitled “Antibodies for the diagnosis and/or treatment of atherosclerosis” (PCT/EP2020/076541) was filed by applicants Centro Nacional de Investigaciones Cardiovasculares Carlos III (F.S.P.) and German Cancer Research Center (DKFZ) on 24 September 2020, authored by A.R.R., C.L., H.W. and C.E.B., to protect the use of antibodies identified in this study. Status: pending. An international patent application entitled “Method for the diagnosis and/or treatment of atherosclerosis” (PCT/EP2020/076549) was filed by applicants: Centro Nacional de Investigaciones Cardiovasculares Carlos III (F.S.P.), Fundación Instituto de Investigación Sanitaria Fundación Jiménez Díaz and Universidad Autónoma de Madrid on 24 September 2020, authored by A.R.R., C.L., J.V., J.L.M.-V., A.S.-B., P.D. and E.B.-K., to protect the use of ALDH4A1 as an atherosclerosis biomarker. Status: pending.
Peer review information Nature thanks Christoph Binder, Patrick Wilson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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 Flow cytometry analysis of the humoral response associated with atherosclerosis development.
a, Representative flow cytometry plots showing the gating strategy used to analyse the humoral response associated to atherosclerosis in the spleen of Ldlr−/− mice. b, Quantification of the absolute number of germinal centre B cells (Fas+GL7+, gated on B220+) (n = 10 Ldlr−/− ND, n = 11 Ldlr−/− HFD mice), T follicular helper (Tfh) cells (CXCR5+PD-1+, gated on CD4+) (n = 10 mice), memory B cells (PD-L2+, gated on B220+) (n = 10 mice), plasma cells (CD138+Igκ+) (n = 9 Ldlr−/− ND, n = 10 Ldlr−/− HFD mice), and class-switched B cells (IgG2b (n = 5 mice), IgG2c (n = 5 mice), IgG1 (n = 7 mice)) in spleens of mice from the indicated groups. Statistical analysis was done with two-tailed unpaired Student’s t-test.
Extended Data Fig. 2 Germinal centre response and antibody production during atherosclerosis.
Ldlr−/− or Ldlr+/+ mice were fed either a normal diet (ND) or a high-fat diet (HFD), and the germinal centre response in the spleen was assessed by flow cytometry. a, b, Analysis of the germinal centre B cells (Fas+GL7+, gated on B220+) in spleen from Ldlr−/− ND and Ldlr−/− HFD at 4 weeks (n = 3 mice), 8 weeks (n = 3 mice), 12 weeks (n = 3 mice), 16 weeks (n = 3 mice) and 20 weeks (n = 2 mice) of diet treatment. Representative plots (a) and their quantification (b) are shown. Data are presented as mean values +/− s.d. Statistical analysis was done with two-way ANOVA. c, B cells expressing class-switched immunoglobulins (IgG2b (n = 5 mice), IgG2c (n = 5 mice), and IgG1 (n = 8 Ldlr−/− ND, n = 7 Ldlr−/− HFD mice) after 16 weeks of ND/HFD treatment. Statistical analysis was done with two-tailed unpaired Student’s t-test. d, Plasma from Ldlr−/− mice fed the ND (n = 8) or HFD (n = 8) was collected at different time points (0 and 16 weeks), and total IgM and IgG levels were determined by ELISA. e, Plasma from Ldlr−/− mice fed the ND or HFD was collected at different time points (0, 4, 8, 12, 16 weeks), and IgM antibody titres specific for MDA-LDL (n = 4 Ldlr−/− ND, n = 8 Ldlr−/− HFD mice), LDL (n = 4 Ldlr−/− ND, n = 8 Ldlr−/− HFD mice), and Hsp60 (n = 9 Ldlr−/− ND, n = 7 Ldlr−/− HFD mice) were determined by ELISA. (d, e) Data correspond to the relative absorbance at 450 nm. Statistical analysis was done with two-way ANOVA.
Extended Data Fig. 3 Identification of atherosclerosis-associated antibodies by single-cell sequencing and cloning of immunoglobulin genes.
a, Frequency of germinal centre B cells (Fas+GL7+, gated on B220+) (n = 10 Ldlr+/+ ND, n = 11 Ldlr+/+ HFD, n = 16 Ldlr−/− ND/HFD mice), T follicular helper (Tfh) cells (CXCR5+PD-1+, gated on CD4+) (n = 8 Ldlr+/+ ND/HFD, n = 15 Ldlr−/− ND/HFD mice), and B cells expressing class-switched immunoglobulins (IgG2b (n = 10 Ldlr+/+ ND, n = 11 Ldlr+/+ HFD, n = 10 Ldlr−/− ND/HFD mice), IgG1 (n = 10 Ldlr+/+ ND, n = 11 Ldlr+/+ HFD, n = 13 Ldlr−/− ND, n = 12 Ldlr−/− HFD mice)) in the spleen of Ldlr+/+ and Ldlr−/− mice after 16 weeks of ND/HFD treatment. Statistical analysis was done with one-way ANOVA. b, Representative Oil-Red-stained en face aortas from Ldlr+/+ or Ldlr−/− mice fed with ND or HFD for 16 weeks. c, Cell isolation and antibody expression-cloning strategy. Individual spleen germinal centre B cells and plasma cells from Ldlr+/+ ND (n = 2, purple), Ldlr+/+ HFD (n = 3, orange), Ldlr−/− ND (n = 6, grey), and Ldlr−/− HFD (n = 6, green) were isolated by single-cell sorting. IgH and IgL cDNAs from sorted cells were then PCR amplified and sequenced. Successful paired (IgH + IgL) sequences were obtained for a total of 1727 individual cells. 56 antibodies representative of expanded B cell clones (clusters) from atherogenic mice (Ldlr−/− HFD) were cloned into expression vectors containing the human IgG1 constant region for IgH and the kappa constant region for IgL, as described37. Expression vectors were transfected in eukaryotic cells, and antibodies were harvested from supernatants for further analysis. As controls, 25 antibodies from Ldlr+/+ ND mice were cloned and expressed in eukaryotic cells. d, Left Distribution of the 1727 events in the four groups of mice. Right Number of events per mouse. e, Left Overall proportion of germinal centre B cells and plasma cells in the sequenced events. Right Proportion of germinal centre B cells and plasma cells in each mouse group. f–h, Data summarize the Igh and Igl gene sequence analysis from single germinal centre B cells and plasma cells from Ldlr+/+ ND mice (138 antibodies) and Ldlr−/− HFD mice (805 antibodies). Igh and Igl V gene family (f) and J gene usage (g). h, IgH and IgL CDR3 amino acid number of the antibodies from Ldlr+/+ ND (n = 138 antibodies) and Ldlr−/− HFD (n = 805 antibodies) mice. Statistical analysis was done with two-sided Fisher exact test.
Extended Data Fig. 4 Analysis of clonally related B cell clusters.
a, Circos plot representation of the molecular features of the identified expanded clonally related cells. b, Cluster summary. Bars show cluster size (number of antibodies). Checked boxes underneath indicate the population or origin (PC/germinal centre), the presence of CSR and SHM, and the number of mice sharing the cluster. Green-checked boxes indicate the antibodies cloned for subsequent analysis. c, Antibodies cloned from control mice (n = 25) and atherogenic mice (n = 56) were tested by ELISA for their reactivity against MDA-LDL, native LDL, and Hsp60. The charts show OD405 values at 4 μg/ml antibody and at three consecutive 1:4 dilutions. Red lines represent the non-reactive negative control antibody mGO5339. The proportion of antibodies showing reactivity is shown below each graph. d, Antibodies were tested for polyreactivity with insulin, dsDNA, and LPS. As controls, we used the highly polyreactive ED38 antibody (green lines), low-polyreactive JB40 antibody (purple lines), and non-polyreactive mGO53 antibody (red lines)39.
Extended Data Fig. 5 Plaque reactivity of antibodies cloned from Ldlr−/− HFD mice.
a, Representative staining of aortic sinus cryosections from Ldlr−/− HFD mice with the 18 plaque reactive antibodies from Ldlr−/− HFD mice. The left panel of each image pair shows merged antibody staining (red) and Dapi (blue). The right panels show the antibody signal alone (white). 40x magnification; scale bar = 50 μm. b, Sections stained with B1.8 and mGO53 control antibodies. 10x magnification; scale bar = 200 μm. a, b, Maximum intensity Z-projection images are shown.
Extended Data Figure 6 Immunofluorescence analysis of A12 reactivity.
a, Representative staining with A12 antibody (white) and fibronectin (red) in aortic sinus cryosections from Ldlr−/− HFD mice. b, Representative staining with A12 antibody (red) in aortic root cryosections from Ldlr−/− HFD mice. c, Representative staining with A12 antibody (red) in aortic sinus cryosections from Apoe−/− HFD mice. d, A12 staining (green) on spleen sections from wild-type and Ldlr−/− HFD mice, co-stained with PNA (red). e, A12 staining (green) on liver sections from wild-type and Ldlr−/− HFD mice. Scale bar 10x= 200 µm; 20x= 100 µm; 40x= 50 µm. a–e, Maximum intensity Z-projection images are shown.
Extended Data Fig. 7 ALDH4A1 mitochondrial auto-antigen is recognized by A12 antibody and correlates with atherosclerosis in mice and humans.
a, Enrichment of ALDH4A1 in each immunoprecipitation (PSM IP/PSM input) (n = 3 independent experiments). b, ELISA validation of A12 specificity for ALDH4A1. Plates were coated with ALDH4A1-FLAG and incubated either with mGO53 antibody39 (negative control), A12, or anti-ALDH4A1 (positive control). Data represent the relative absorbance at 405 nm. c, Competition immunoassay of A12 antibody (1 μg/ml) binding to plated mouse ALDH4A1-FLAG protein in the presence or absence of increasing amounts (0, 50, 100, 200, 400 μg/ml) of the indicated competitors (BSA, MDA-LDL or ALDH4A1-FLAG). Results are shown as ratios of A12 binding to ALDH4A1-FLAG in the presence (B) or absence (B0) of the competitor. d, ELISA determination of anti-ALDH4A1 IgM antibody levels in plasma from Ldlr+/+ ND mice (purple, n = 5) and Ldlr−/− HFD mice (green, n = 8) at the indicated times of ND or HFD feeding. Statistical analysis was done with two-way ANOVA. e, ELISA determination of anti-ALDH4A1 IgG antibody levels in plasma from Ldlr+/+ ND mice (purple, n = 10) and Ldlr−/− HFD mice (green, n = 10) after 16 weeks of diet treatment. Statistical analysis was done with two-tailed unpaired Student’s t-test. f, Representative immunohistochemistry staining with anti-ALDH4A1 antibody on aortic sinus sections from wild type and Ldlr−/− HFD mice. Scale bars, 5x = 200 μm; 20x = 50 μm. g, ALDH4A1 staining in liver sections from wild type and Ldlr−/− HFD mice. Scale bar = 50 μm. h, Quantification of the abundance of ALDH4A1 protein, all mitochondrial proteins and ACTB actin in the medial and intimal layers of human samples (healthy (n = 6 (ALDH4A1), n = 9 (mitochondrial proteins and ACTB)); fatty streak lesion (n = 4 (ALDH4A1), n = 7 (mitochondrial proteins and ACTB) for media, n = 6 for intima)); and fibrolipid lesion (n = 8 (ALDH4A1), n = 11 (mitochondrial proteins and ACTB) for media, n = 12 for intima)). i, Logistic regression analysis adjusted with cardiovascular risk factors was performed with atherosclerosis presence as dependent variable.
Extended Data Fig. 8 ALDH4A1 triggers a T-cell dependent B cell response.
6-7-week-old wild type (a, b) or Ldlr−/− (c, d) mice were immunized with ALDH4A1-FLAG protein plus alum (ALDH4A1 group; a and b, n = 8, c and d, n = 9). Control mice were injected with alum only (ALUM group; a and b, n = 8, c and d, n = 8) or with PBS (PBS group; a and b, n = 8, c and d, n = 8). Mice were sacrificed and the immune response in lymph nodes was analysed by flow cytometry and ELISA. a, c, Left, representative flow cytometry plots of germinal centre B cells (Fas+GL7+, gated on B220+) and IgG1+ B cells (gated on B220+). Right, quantification. b, d, ELISA quantification of IgG1 antibodies specific for ALDH4A1 protein. Plates were coated with ALDH4A1-FLAG protein or IKAROS-FLAG as control protein and incubated with plasma from mice from the indicated immunization groups. Plots represents the relative absorbance at 450 nm. Statistical analysis was done with one-way ANOVA.
Extended Data Fig. 9 Evaluation of atheroma plaque and liver composition after A12 antibody treatment.
a, b, Ldlr−/− mice were fed with HFD for 12 weeks, infused with hA12-IgG1 (n = 5 mice), hB18-IgG1 (n = 4 mice) or PBS (n = 4 mice) and sacrificed 3 or 7 days later. Representative staining of aortic sinus (a) and liver (b) cryosections with anti-human IgG antibody is shown. Scale bar 10x = 200 μm. Maximum intensity Z-projection images are shown. c, Representative MAC-2 (red) and SMA-1 (green) immunofluorescence in aortic sinus cryosections from Ldlr−/− HFD mice treated with PBS, B1.8, or A12. Scale bar 10x = 200 μm. Maximum intensity Z-projection images are shown. d, Plaque macrophage content (% MAC-2+) in Ldlr−/− HFD mice treated with PBS (n = 14), B1.8 (n = 15) or A12 (n = 15). e, Plaque smooth muscle cell content (% SMA-1+) in Ldlr−/− HFD mice treated with PBS (n = 14), B1.8 (n = 15) or A12 (n = 15). f, Picrosirius Red staining of aortic sinus cryosections from Ldlr−/− HFD mice treated with PBS, B1.8, or A12. Scale bar = 200 μm. g, Plaque collagen content in Ldlr−/− HFD mice treated with PBS (n = 5), B1.8 (n = 6) or A12 (n = 5).
Extended Data Fig. 10 High-throughput quantitative proteomics of liver from A12-treated mice.
Liver from A12-treated mice (n = 6) as well as mice treated with PBS (n = 6) or with the unrelated control antibody B1.8 (n = 6) were subjected to proteomics using multiplexed TMT isobaric labelling. a, Principal component analysis (PCA) showed that A12 infusion promotes in the liver a distinct proteome shift, compared to livers from PBS or B1.8 infused mice. b, Correlation network analysis using Cytoscape grouped protein abundance changes into four significant clusters (lower panel). Two of them contained proteins that showed a coordinated statistically significant increase (clusters 1 and 2) or decrease (clusters 3 and 4) after A12 treatment in relation to the control treatments (B1.8 or PBS) (upper panels), according to the two-sample Kolmogorov–Smirnov test. c, Functional association network and enrichment analysis using STRING revealed that each of these clusters contained groups of functionally-related proteins that were significantly enriched in several biological pathways. The increased proteins in cluster 1 belonged to carbohydrate, amino acid and lipid metabolic pathways, while most of the decreased proteins of cluster 4 belonged to immune system and inflammation pathways. Cluster 3 also contained proteins related to carbohydrate metabolism. Other enriched functional categories are also indicated in the panels. d, Clustering result of statistically significant triglycerides (TGs). Heat map was obtained by MetaboAnalyst (distance measure using Euclidean, and clustering algorithm using complete). TGs were putatively identified by searching the m/z against Ceu Mass Mediator (http://ceumass.eps.uspceu.es/mediator/) and considering retention time (RT) prediction and isotopic pattern distribution score in Freestyle (Thermo).
This file contains Supplementary Tables 1-5.
Rights and permissions
About this article
Cite this article
Lorenzo, C., Delgado, P., Busse, C.E. et al. ALDH4A1 is an atherosclerosis auto-antigen targeted by protective antibodies. Nature 589, 287–292 (2021). https://doi.org/10.1038/s41586-020-2993-2
This article is cited by
Antibodies in action: the role of humoral immunity in the fight against atherosclerosis
Immunity & Ageing (2022)
GPR55 in B cells limits atherosclerosis development and regulates plasma cell maturation
Nature Cardiovascular Research (2022)
The why and how of adaptive immune responses in ischemic cardiovascular disease
Nature Cardiovascular Research (2022)
GPR55 is a key player for B-cell-mediated atheroprotection
Nature Cardiovascular Research (2022)
The changing landscape of atherosclerosis
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.