The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Transcription Factor MAFF (MAF Basic Leucine Zipper Transcription Factor F) Regulates an Atherosclerosis Relevant Network Connecting Inflammation and Cholesterol Metabolism
Abstract
Background: Coronary artery disease (CAD) is a multifactorial condition with both genetic and exogenous causes. The contribution of tissue-specific functional networks to the development of atherosclerosis remains largely unclear. The aim of this study was to identify and characterize central regulators and networks leading to atherosclerosis. Methods: Based on several hundred genes known to affect atherosclerosis risk in mouse (as demonstrated in knockout models) and human (as shown by genome-wide association studies), liver gene regulatory networks were modeled. The hierarchical order and regulatory directions of genes within the network were based on Bayesian prediction models, as well as experimental studies including chromatin immunoprecipitation DNA-sequencing, chromatin immunoprecipitation mass spectrometry, overexpression, small interfering RNA knockdown in mouse and human liver cells, and knockout mouse experiments. Bioinformatics and correlation analyses were used to clarify associations between central genes and CAD phenotypes in both human and mouse. Results: The transcription factor MAFF (MAF basic leucine zipper transcription factor F) interacted as a key driver of a liver network with 3 human genes at CAD genome-wide association studies loci and 11 atherosclerotic murine genes. Most importantly, expression levels of the low-density lipoprotein receptor (LDLR) gene correlated with MAFF in 600 CAD patients undergoing bypass surgery (STARNET [Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task]) and a hybrid mouse diversity panel involving 105 different inbred mouse strains. Molecular mechanisms of MAFF were tested in noninflammatory conditions and showed positive correlation between MAFF and LDLR in vitro and in vivo. Interestingly, after lipopolysaccharide stimulation (inflammatory conditions), an inverse correlation between MAFF and LDLR in vitro and in vivo was observed. Chromatin immunoprecipitation mass spectrometry revealed that the human CAD genome-wide association studies candidate BACH1 (BTB domain and CNC homolog 1) assists MAFF in the presence of lipopolysaccharide stimulation with respective heterodimers binding at the MAF recognition element of the LDLR promoter to transcriptionally downregulate LDLR expression. Conclusions: The transcription factor MAFF was identified as a novel central regulator of an atherosclerosis/CAD-relevant liver network. MAFF triggered context-specific expression of LDLR and other genes known to affect CAD risk. Our results suggest that MAFF is a missing link between inflammation, lipid and lipoprotein metabolism, and a possible treatment target.
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.
Integrity signals
Registry
Cites
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.
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 [23]
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
Sequence Variations in PCSK9, Low LDL, and Protection against Coronary Heart Disease
Cohen, J. C., Boerwinkle, E., Mosley, T. H., & Hobbs, H. H. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa054013 · Cited by 3,181
A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447
Genomewide Association Analysis of Coronary Artery Disease
Samani, N. J., Erdmann, J., Hall, A. S., Hengstenberg, C., Mangino, M., Mayer, B., Dixon, R. J., Meitinger, T., Braund, P., Wichmann, H.-E., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa072366 · Cited by 2,044
Self-citationGenetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease
Khera, A. V., Emdin, C. A., Drake, I., Natarajan, P., Bick, A. G., Cook, N. R., Chasman, D. I., Baber, U., Mehran, R., Rader, D. J., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1605086 · Cited by 1,460
Tissue-specific expression and regulation of sexually dimorphic genes in mice
Yang, X., Schadt, E. E., Wang, S., Wang, H., Arnold, A. P., Ingram-Drake, L., Drake, T. A., & Lusis, A. J. (2006) · Genome Research · doi:10.1101/gr.5217506 · Cited by 922
Self-citationFerence, B. A., Yoo, W., Alesh, I., Mahajan, N., Mirowska, K. K., Mewada, A., Kahn, J., Afonso, L., Williams, K. A., & Flack, J. M. (2012) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.09.017 · Cited by 898
Franzén, O., Ermel, R., Cohain, A., Akers, N. K., Di Narzo, A., Talukdar, H. A., Foroughi-Asl, H., Giambartolomei, C., Fullard, J. F., Sukhavasi, K., et al. (2016) · Science · doi:10.1126/science.aad6970 · Cited by 288
Self-citationIntegrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease
Mäkinen, V.-P., Civelek, M., Meng, Q., Zhang, B., Zhu, J., Levian, C., Huan, T., Segrè, A. V., Ghosh, S., Vivar, J., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004502 · Cited by 196
Self-citation
Flagged first, then by citations
Research works printed but not linked: 74
- 10.1161/CIR.0000000000000366 · DOI
- 10.1093/eurheartj/ehu378 · DOI
- 10.15252/emmm.201506174 · DOI
- 10.1371/journal.pgen.1007040 · DOI
- 10.1161/ATVBAHA.115.305513 · DOI
- 10.1016/j.cmet.2016.11.001 · DOI
- 10.1093/eurheartj/ehx144 · DOI · Paper page
- 10.1038/nm.2538 · DOI · Paper page
- 10.1042/CS20180306 · DOI
- 10.1093/eurheartj/eht571 · DOI · Paper page
- 10.1056/NEJM199901143400207 · DOI · Paper page
- 10.1016/S0140-6736(05)67394-1 · DOI · Paper page
- 10.1056/NEJMoa1707914 · DOI · Paper page
- 10.1016/S0140-6736(09)60447-5 · DOI · Paper page
- 10.1038/ng.784 · DOI · Paper page
- 10.1038/ng.782 · DOI · Paper page
- 10.1038/ng.2480 · DOI · Paper page
- 10.1161/CIRCRESAHA.115.306566 · DOI
- 10.1038/ng.3874 · DOI
- 10.1038/ng.3913 · DOI
- 10.1016/j.jacc.2016.11.056 · DOI
- 10.1161/ATVBAHA.115.306108 · DOI · Paper page
- 10.1161/CIRCULATIONAHA.107.730614 · DOI
- 10.1038/ng.167 · DOI
- 10.1371/journal.pone.0014319 · DOI
- 10.1161/CIRCRESAHA.107.152975 · DOI
- 10.1371/journal.pbio.0060107 · DOI
- 10.1371/journal.pgen.1003107 · DOI
- 10.1371/journal.pgen.1004502 · DOI · Paper page
- 10.1038/msb.2012.24 · DOI
- 10.1101/gr.103341.109 · DOI
- 10.1186/s12864-016-3198-9 · DOI
- 10.1186/gb-2008-9-9-r137 · DOI · Paper page
- 10.1101/gr.136184.111 · DOI · Paper page
- 10.1016/j.molcel.2010.05.004 · DOI · Paper page
- 10.1093/bioinformatics/btr189 · DOI
- 10.1021/bi301248j · DOI
- 10.1093/nar/gkw1129 · DOI
- 10.1038/nprot.2016.169 · DOI
- 10.1002/prot.25219 · DOI
- 10.1093/cvr/cvy084 · DOI
- 10.4014/jmb.1210.10057 · DOI
- 10.1161/01.ATV.0000064374.15232.C3 · DOI · Paper page
- 10.1161/CIRCULATIONAHA.105.605121 · DOI
- 10.1084/jem.20111202 · DOI
- 10.1016/j.carpath.2014.01.006 · DOI
- 10.1161/JAHA.112.002550 · DOI
- 10.1172/JCI117179 · DOI
- 10.1002/stem.1747 · DOI
- 10.1161/JAHA.113.000134 · DOI
- 10.1073/pnas.0808650105 · DOI
- 10.1073/pnas.0709011104 · DOI
- 10.1093/cvr/cvq278 · DOI
- 10.1515/cclm-2015-0823 · DOI
- 10.1038/aps.2013.10 · DOI
- 10.1016/j.jmb.2007.11.074 · DOI
- 10.1007/s00335-012-9411-5 · DOI
- 10.1371/journal.pgen.1005711 · DOI
- 10.1093/nar/gkw868 · DOI
- 10.1038/nature13985 · DOI
- 10.1155/2018/1347969 · DOI
- 10.1161/CIRCRESAHA.117.312086 · DOI · Paper page
- 10.1007/s00392-018-1324-1 · DOI
- 10.1016/j.ejphar.2017.05.010 · DOI · Paper page
- 10.1016/j.berh.2016.10.006 · DOI
- 10.1016/j.semarthrit.2005.03.004 · DOI
- 10.1016/j.autrev.2016.07.026 · DOI
- 10.1136/annrheumdis-2016-210275 · DOI
- 10.1038/nrrheum.2015.40 · DOI
- 10.1186/s13075-020-02307-8 · DOI
- 10.1161/ATVBAHA.115.306725 · DOI
- 10.1016/j.gene.2016.03.058 · DOI
- 10.1095/biolreprod.105.045450 · DOI
- 10.1016/j.molcel.2013.08.018 · DOI
Cited by
Sorted by citations
Pathways and Molecular Mechanisms Governing LDL Receptor Regulation
Schmidt, H. M., Jarrett, K. E., de Aguiar Vallim, T. Q., & Tarling, E. J. (2025) · Circulation Research · doi:10.1161/circresaha.124.323578 · Cited by 33
Jiang, W., Cheung, R. Y. K., Chung, C. Y., Chan, S. S. C., & Choy, K. W. (2024) · Genes · doi:10.3390/genes16010005 · Cited by 9
Metadata
- Authors
- von Scheidt, M., Zhao, Y., de Aguiar Vallim, T. Q., Che, N., Wierer, M., Seldin, M. M., Franzén, O., Kurt, Z., Pang, S., Bongiovanni, D., Yamamoto, M., Edwards, P. A., Ruusalepp, A., Kovacic, J. C., Mann, M., Björkegren, J. L. M., Lusis, A. J., Yang, X., & Schunkert, H.
- Affiliations
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (M.v.S., S.P., H.S.).
- Departments of Medicine (T.Q.d.A.V., N.C., P.A.E., A.J.L.), David Geffen School of Medicine, University of California, Los Angeles.
- Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Martinsried, Germany (M.W., M.M.).
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine (M.M.S.).
- Integrated Cardio Metabolic Centre, Karolinska Institutet, Novum, Huddinge, Sweden (O.F., J.L.M.B.).
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom (Z.K.).
- Deutsches Zentrum für Herz- und Kreislauferkrankungen, Partner Site Munich Heart Alliance, Germany (M.v.S., D.B., H.S.).
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan (M.Y.).
- Department of Cardiac Surgery, Tartu University Hospital, Estonia (A.R.).
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York (J.C.K., J.L.M.B.).
- Department of Integrative Biology and Physiology, Institute for Quantitative and Computational Biosciences (Y.Z., X.Y.), David Geffen School of Medicine, University of California, Los Angeles.
- Venue
- Circulation
- Published
- 2021-05-04
- Volume
- 143
- Issue
- 18
- Pages
- 1809-1823
- ISSN
- 0009-7322
- 1524-4539
- PMC
- PMC8124091
- Cited by
- 68
- References
- 8
- Language
- en
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