CoChat
Sign Up
Cited by 8

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Identification of the Transcription Factor ATF3 as a Direct and Indirect Regulator of the LDLR

Bauer, Sabine, Eigenmann, Jana, Zhao, Yuqi, Fleig, Julia, Hawe, Johann S., Pan, Calvin, Bongiovanni, Dario, Wengert, Simon, Ma, Angela, Lusis, Aldons J., Kovacic, Jason C., Björkegren, Johan L. M., Maegdefessel, Lars, Schunkert, Heribert, von Scheidt, Moritz·2022·Metabolites
DOI 10.3390/metabo12090840PMC PMC9504235
Open this paper in CoChat — free account

Abstract

Coronary artery disease (CAD) is a complex, multifactorial disease caused, in particular, by inflammation and cholesterol metabolism. At the molecular level, the role of tissue-specific signaling pathways leading to CAD is still largely unexplored. This study relied on two main resources: (1) genes with impact on atherosclerosis/CAD, and (2) liver-specific transcriptome analyses from human and mouse studies. The transcription factor activating transcription factor 3 (ATF3) was identified as a key regulator of a liver network relevant to atherosclerosis and linked to inflammation and cholesterol metabolism. ATF3 was predicted to be a direct and indirect (via MAF BZIP Transcription Factor F (MAFF)) regulator of low-density lipoprotein receptor (LDLR). Chromatin immunoprecipitation DNA sequencing (ChIP-seq) data from human liver cells revealed an ATF3 binding motif in the promoter regions of MAFF and LDLR. siRNA knockdown of ATF3 in human Hep3B liver cells significantly upregulated LDLR expression (p < 0.01). Inflammation induced by lipopolysaccharide (LPS) stimulation resulted in significant upregulation of ATF3 (p < 0.01) and subsequent downregulation of LDLR (p < 0.001). Liver-specific expression data from human CAD patients undergoing coronary artery bypass grafting (CABG) surgery (STARNET) and mouse models (HMDP) confirmed the regulatory role of ATF3 in the homeostasis of cholesterol metabolism. This study suggests that ATF3 might be a promising treatment candidate for lowering LDL cholesterol and reducing cardiovascular risk.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/metabo12090840

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Transcription of LDLR mRNA is regulated by several transcription factors including co-activation by EGR1 and cEBPβ, ATF3, MAFF, and CREB.

    “Transcription of LDLR mRNA is regulated by several transcription factors including co-activation by EGR1 and cEBPβ21, activating transcription factor 3 (ATF3)22, MAFF23, and CREB24.”

    From Pathways and Molecular Mechanisms Governing LDL Receptor Regulation (2025) · cites this paper as [22]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 75

  1. Virani, 2020, Heart disease and stroke statistics—2020 update: A report from the American Heart Association, Circulation, 141, e139, 10.1161/CIR.0000000000000757 · DOI · Paper page
  2. Townsend, 2016, Cardiovascular disease in Europe: Epidemiological update 2016, Eur. Heart J., 37, 3232, 10.1093/eurheartj/ehw334 · DOI
  3. Susser, 2022, Through the layers: How macrophages drive atherosclerosis across the vessel wall, J. Clin. Investig., 132, e157011, 10.1172/JCI157011 · DOI
  4. Ross, 1995, Cell biology of atherosclerosis, Annu. Rev. Physiol., 57, 791, 10.1146/annurev.ph.57.030195.004043 · DOI
  5. Myers, J., Kokkinos, P., and Nyelin, E. (2019). Physical activity, cardiorespiratory fitness, and the metabolic syndrome. Nutrients, 11. · DOI
  6. Parsanathan, 2020, Novel invasive and noninvasive cardiac-specific biomarkers in obesity and cardiovascular diseases, Metab. Syndr. Relat. Disord., 18, 10, 10.1089/met.2019.0073 · DOI
  7. Padro, 2021, The role of triglycerides in the origin and progression of atherosclerosis, Clín. Investig. Arterioscler., 33, 20, 10.1016/j.arteri.2021.02.007 · DOI
  8. Barbalho, 2022, Metabolic syndrome and cardiovascular diseases: Going beyond traditional risk factors, Diabetes/Metab. Res. Rev., 38, e3502, 10.1002/dmrr.3502 · DOI
  9. Ference, 2017, Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel, Eur. Heart J., 38, 2459, 10.1093/eurheartj/ehx144 · DOI · Paper page
  10. Geovanini, 2018, Atherosclerosis and inflammation: Overview and updates, Clin. Sci., 132, 1243, 10.1042/CS20180306 · DOI
  11. Kessler, 2016, The impact of genome-wide association studies on the pathophysiology and therapy of cardiovascular disease, EMBO Mol. Med., 8, 688, 10.15252/emmm.201506174 · DOI
  12. Shu, L., Chan, K.H.K., Zhang, G., Huan, T., Kurt, Z., Zhao, Y., Codoni, V., Trégouët, D.-A., Consortium, C., and Yang, J. (2017). Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States. PLoS Genet., 13. · DOI
  13. Ghosh, 2015, Systems genetics analysis of genome-wide association study reveals novel associations between key biological processes and coronary artery disease, Arterioscler. Thromb. Vasc. Biol., 35, 1712, 10.1161/ATVBAHA.115.305513 · DOI
  14. Zhao, 2017, Applications and limitations of mouse models for understanding human atherosclerosis, Cell Metab., 25, 248, 10.1016/j.cmet.2016.11.001 · DOI
  15. Vos, 2018, Recycling the LDL receptor to combat atherosclerosis, Aging, 10, 3638, 10.18632/aging.101681 · DOI
  16. Ishibashi, 1994, Massive xanthomatosis and atherosclerosis in cholesterol-fed low density lipoprotein receptor-negative mice, J. Clin. Investig., 93, 1885, 10.1172/JCI117179 · DOI
  17. Attie, 2005, Dual regulation of the LDL receptor—Some clarity and new questions, Cell Metab., 1, 290, 10.1016/j.cmet.2005.04.006 · DOI · Paper page
  18. Goldstein, 2006, Protein sensors for membrane sterols, Cell, 124, 35, 10.1016/j.cell.2005.12.022 · DOI · Paper page
  19. Soutar, 2007, Mechanisms of Disease: Genetic causes of familial hypercholesterolemia, Nat. Clin. Pract. Cardiovasc. Med., 4, 214, 10.1038/ncpcardio0836 · DOI · Paper page
  20. Zhao, 2021, Transcription factor MAFF (MAF basic leucine zipper transcription factor F) regulates an atherosclerosis relevant network connecting inflammation and cholesterol metabolism, Circulation, 143, 1809, 10.1161/CIRCULATIONAHA.120.050186 · DOI · Paper page
  21. Gilchrist, 2006, Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4, Nature, 441, 173, 10.1038/nature04768 · DOI
  22. Zmuda, 2010, The roles of ATF3, an adaptive-response gene, in high-fat-diet-induced diabetes and pancreatic β-cell dysfunction, Mol. Endocrinol., 24, 1423, 10.1210/me.2009-0463 · DOI
  23. Xu, 2021, Hepatocyte ATF3 protects against atherosclerosis by regulating HDL and bile acid metabolism, Nat. Metab., 3, 59, 10.1038/s42255-020-00331-1 · DOI
  24. Ku, 2020, Master regulator activating transcription factor 3 (ATF3) in metabolic homeostasis and cancer, Front. Endocrinol., 11, 556, 10.3389/fendo.2020.00556 · DOI · Paper page
  25. Jadhav, 2017, Activating transcription factor 3 in immune response and metabolic regulation, Liver Res., 1, 96, 10.1016/j.livres.2017.08.001 · DOI
  26. Zhou, 2018, Activating transcription factor 3 in cardiovascular diseases: A potential therapeutic target, Basic Res. Cardiol., 113, 37, 10.1007/s00395-018-0698-6 · DOI
  27. Thompson, 2009, ATF3 transcription factor and its emerging roles in immunity and cancer, J. Mol. Med., 87, 1053, 10.1007/s00109-009-0520-x · DOI
  28. Hai, 2010, ATF3, a hub of the cellular adaptive-response network, in the pathogenesis of diseases: Is modulation of inflammation a unifying component?, Gene Expr. J. Liver Res., 15, 1, 10.3727/105221610X12819686555015 · DOI
  29. Hai, 1999, ATF3 and stress responses, Gene Expr. J. Liver Res., 7, 321
  30. Nawa, 2002, Expression of transcriptional repressor ATF3/LRF1 in human atherosclerosis: Colocalization and possible involvement in cell death of vascular endothelial cells, Atherosclerosis, 161, 281, 10.1016/S0021-9150(01)00639-6 · DOI
  31. Gold, 2012, ATF3 protects against atherosclerosis by suppressing 25-hydroxycholesterol–induced lipid body formation, J. Exp. Med., 209, 807, 10.1084/jem.20111202 · DOI
  32. Schunkert, 2011, Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease, Nat. Genet., 43, 333, 10.1038/ng.784 · DOI · Paper page
  33. Coronary Artery Disease (C4D) Genetics Consortium (2011). A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat. Genet., 43, 339–344. · DOI · Paper page
  34. Samani, 2007, Genomewide association analysis of coronary artery disease, N. Engl. J. Med., 357, 443, 10.1056/NEJMoa072366 · DOI · Paper page
  35. Nikpay, 2015, A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease, Nat. Genet., 47, 1121, 10.1038/ng.3396 · DOI · Paper page
  36. McPherson, 2016, Genetics of coronary artery disease, Circ. Res., 118, 564, 10.1161/CIRCRESAHA.115.306566 · DOI
  37. Howson, 2017, Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms, Nat. Genet., 49, 1113, 10.1038/ng.3874 · DOI
  38. Nelson, 2017, Association analyses based on false discovery rate implicate new loci for coronary artery disease, Nat. Genet., 49, 1385, 10.1038/ng.3913 · DOI
  39. Civelek, 2015, Prediction of causal candidate genes in coronary artery disease loci, Arterioscler. Thromb. Vasc. Biol., 35, 2207, 10.1161/ATVBAHA.115.306108 · DOI · Paper page
  40. Schunkert, 2008, Repeated replication and a prospective meta-analysis of the association between chromosome 9p21. 3 and coronary artery disease, Circulation, 117, 1675, 10.1161/CIRCULATIONAHA.107.730614 · DOI
  41. Bonaterra, 2012, Growth differentiation factor-15 deficiency inhibits atherosclerosis progression by regulating interleukin-6–dependent inflammatory response to vascular injury, J. Am. Heart Assoc., 1, e002550, 10.1161/JAHA.112.002550 · DOI
  42. Qing, 2014, Deficiency of the NR4A orphan nuclear receptor NOR1 in hematopoietic stem cells accelerates atherosclerosis, Stem Cells, 32, 2419, 10.1002/stem.1747 · DOI
  43. Arndt, 2015, Heterozygous deficiency of Tribbles homolog-1 gene (Trib1) increases atherosclerotic lesions in ApoE-knockout mice, Clin. Chem. Lab. Med., 53, P068
  44. Kubota, 2021, Serum anti-SERPINE1 antibody as a potential biomarker of acute cerebral infarction, Sci. Rep., 11, 21772, 10.1038/s41598-021-01176-8 · DOI
  45. Varbo, 2011, TRIB1 and GCKR polymorphisms, lipid levels, and risk of ischemic heart disease in the general population, Arterioscler. Thromb. Vasc. Biol., 31, 451, 10.1161/ATVBAHA.110.216333 · DOI
  46. Johnston, 2019, Myeloid Tribbles 1 induces early atherosclerosis via enhanced foam cell expansion, Sci. Adv., 5, eaax9183, 10.1126/sciadv.aax9183 · DOI
  47. Cai, 2022, High expression of SGK1 in thrombosis of acute ST-segment elevation myocardial infarction: Based on proteomics analysis of intracoronary thrombosis, Rev. Port. Cardiol., 41, 271, 10.1016/j.repc.2021.02.023 · DOI
  48. Yasuda, 2007, Association of single nucleotide polymorphisms in endothelin family genes with the progression of atherosclerosis in patients with essential hypertension, J. Hum. Hypertens., 21, 883, 10.1038/sj.jhh.1002234 · DOI
  49. Marcos-Jubilar, M., Orbe, J., Roncal, C., Machado, F.J., Rodriguez, J.A., Fernández-Montero, A., Colina, I., Rodil, R., Pastrana, J.C., and Páramo, J.A. (2021). Association of SDF1 and MMP12 with Atherosclerosis and Inflammation: Clinical and Experimental Study. Life, 11. · DOI
  50. Blaschke, 2004, Egr-1 is a major vascular pathogenic transcription factor in atherosclerosis and restenosis, Rev. Endocr. Metab. Disord., 5, 249, 10.1023/B:REMD.0000032413.88756.ee · DOI
  51. Ley, 2001, VCAM-1 is critical in atherosclerosis, J. Clin. Investig., 107, 1209, 10.1172/JCI13005 · DOI
  52. Freigang, 2013, Fatty acid–induced mitochondrial uncoupling elicits inflammasome-independent IL-1α and sterile vascular inflammation in atherosclerosis, Nat. Immunol., 14, 1045, 10.1038/ni.2704 · DOI
  53. Hettwer, J., Hinterdobler, J., Miritsch, B., Deutsch, M.-A., Li, X., Mauersberger, C., Moggio, A., Braster, Q., Gram, H., and Robertson, A.A. (Cardiovasc. Res., 2021). Interleukin-1β suppression dampens inflammatory leucocyte production and uptake in atherosclerosis, Cardiovasc. Res., online ahead of print. · DOI
  54. Koplev, 2022, A mechanistic framework for cardiometabolic and coronary artery diseases, Nat. Cardiovasc. Res., 1, 85, 10.1038/s44161-021-00009-1 · DOI
  55. Ghazalpour, 2012, Hybrid mouse diversity panel: A panel of inbred mouse strains suitable for analysis of complex genetic traits, Mamm. Genome, 23, 680, 10.1007/s00335-012-9411-5 · DOI
  56. Bennett, B.J., Davis, R.C., Civelek, M., Orozco, L., Wu, J., Qi, H., Pan, C., Packard, R.R.S., Eskin, E., and Yan, M. (2015). Genetic architecture of atherosclerosis in mice: A systems genetics analysis of common inbred strains. PLoS Genet., 11. · DOI
  57. Willemsen, 2022, Proteasome dysfunction disrupts adipogenesis and induces inflammation via ATF3, Mol. Metab., 62, 101518, 10.1016/j.molmet.2022.101518 · DOI
  58. Peng, J., Le, C., Xia, B., Wang, J., Liu, J., Li, Z., Zhang, Q., Zhang, Q., Wang, J., and Wan, C. (2021). Research on the correlation between activating transcription factor 3 expression in the human coronary artery and atherosclerotic plaque stability. BMC Cardiovasc. Disord., 21. · DOI
  59. Lai, 2013, ATF3 protects against LPS-induced inflammation in mice via inhibiting HMGB1 expression, Evid.-Based Complementary Altern. Med., 2013, 716481, 10.1155/2013/716481 · DOI
  60. Kwon, 2015, Activating transcription factor 3 represses inflammatory responses by binding to the p65 subunit of NF-κB, Sci. Rep., 5, 14470, 10.1038/srep14470 · DOI
  61. Kong, Q., Zou, J., Zhang, Z., Pan, R., Zhang, Z.Y., Han, S., Xu, Y., Gao, Y., and Meng, Z.-X. (2022). BAF60a Deficiency in Macrophage Promotes Diet-Induced Obesity and Metabolic Inflammation. Diabetes, db220114. · DOI
  62. Kim, 2017, Activating transcription factor 3 is a target molecule linking hepatic steatosis to impaired glucose homeostasis, J. Hepatol., 67, 349, 10.1016/j.jhep.2017.03.023 · DOI
  63. Desvergne, 2006, Transcriptional regulation of metabolism, Physiol. Rev., 86, 465, 10.1152/physrev.00025.2005 · DOI
  64. Belalcazar, 2004, Liver gene expression associated with diet and lesion, Physiol Genom., 19, 131, 10.1152/physiolgenomics.00146.2003 · DOI
  65. Zhu, 2008, Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks, Nat. Genet., 40, 854, 10.1038/ng.167 · DOI
  66. Deloukas, 2013, Large-scale association analysis identifies new risk loci for coronary artery disease, Nat. Genet., 45, 25, 10.1038/ng.2480 · DOI · Paper page
  67. Astle, 2016, The allelic landscape of human blood cell trait variation and links to common complex disease, Cell, 167, 1415, 10.1016/j.cell.2016.10.042 · DOI · Paper page
  68. Webb, 2017, Wellcome Trust Case Control Consortium; MORGAM Investigators; Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators. Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease, JACC, 69, 823, 10.1016/j.jacc.2016.11.056 · DOI
  69. Derry, J.M., Zhong, H., Molony, C., MacNeil, D., Guhathakurta, D., Zhang, B., Mudgett, J., Small, K., El Fertak, L., and Guimond, A. (2010). Identification of genes and networks driving cardiovascular and metabolic phenotypes in a mouse F2 intercross. PLoS ONE, 5. · DOI
  70. Wang, 2007, Identification of pathways for atherosclerosis in mice: Integration of quantitative trait locus analysis and global gene expression data, Circ. Res., 101, e11, 10.1161/CIRCRESAHA.107.152975 · DOI
  71. Schadt, E.E., Molony, C., Chudin, E., Hao, K., Yang, X., Lum, P.Y., Kasarskis, A., Zhang, B., Wang, S., and Suver, C. (2008). Mapping the genetic architecture of gene expression in human liver. PLoS Biol., 6. · DOI
  72. Tu, Z., Keller, M.P., Zhang, C., Rabaglia, M.E., Greenawalt, D.M., Yang, X., Wang, I.-M., Dai, H., Bruss, M.D., and Lum, P.Y. (2012). Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets. PLoS Genet., 8. · DOI
  73. Wang, 2012, Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers, Mol. Syst. Biol., 8, 594, 10.1038/msb.2012.24 · DOI
  74. Yang, 2010, Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver, Genome Res., 20, 1020, 10.1101/gr.103341.109 · DOI
  75. Shu, L., Zhao, Y., Kurt, Z., Byars, S.G., Tukiainen, T., Kettunen, J., Orozco, L.D., Pellegrini, M., Lusis, A.J., and Ripatti, S. (2016). Mergeomics: Multidimensional data integration to identify pathogenic perturbations to biological systems. BMC Genom., 17. · DOI

Cited by

Sorted by citations

Metadata

Authors
Bauer, S., Eigenmann, J., Zhao, Y., Fleig, J., Hawe, J. S., Pan, C., Bongiovanni, D., Wengert, S., Ma, A., Lusis, A. J., Kovacic, J. C., Björkegren, J. L. M., Maegdefessel, L., Schunkert, H., & von Scheidt, M.
Affiliations
  • Department of Cardiology, German Heart Centre Munich, Technical University Munich, 80636 Munich, Germany
  • Department of Integrative Biology and Physiology, Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
  • Departments of Medicine, Human Genetics, Microbiology, Immunology and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
  • DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
  • Helmholtz Pioneer Campus, Helmholtz Center Munich, 85764 Neuherberg, Germany
  • Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
  • Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, NSW 2010, Australia
ORCID
Venue
Metabolites
Publisher
MDPI AG
Published
2022-09-06
Volume
12
Issue
9
Pages
840
ISSN
2218-1989
DOI
10.3390/metabo12090840
PMC
PMC9504235
Cited by
8
References
4
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
National Institutes of Health (HL147883) (HL147883, R01 DK117850, R01 HL148577, R01 HL144651)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review