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Frischknecht L, Schaab J, Schmauch E, Yalamanoglu A, Arnold DD, Schwaiger J, Gruner C, Buechel RR, Franzen DP, Kolios AG, Nilsson J. Assessment of treatment response in cardiac sarcoidosis based on myocardial 18F-FDG uptake. Front Immunol 2023; 14:1286684. [PMID: 38077350 PMCID: PMC10704456 DOI: 10.3389/fimmu.2023.1286684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023] Open
Abstract
Objective Immunosuppressive therapy for cardiac sarcoidosis (CS) still largely consists of corticosteroid monotherapy. However, high relapse rates after tapering and insufficient efficacy are significant problems. The objective of this study was to investigate the efficacy and safety of non-biological and biological disease-modifying anti-rheumatic drugs (nb/bDMARDs) considering control of myocardial inflammation assessed by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) of the heart. Methods We conducted a retrospective analysis of treatment response to nb/bDMARDs of all CS patients seen in the sarcoidosis center of the University Hospital Zurich between January 2016 and December 2020. Results We identified 50 patients with CS. Forty-five patients with at least one follow-up PET/CT scan were followed up for a mean of 20.5 ± 12.8 months. Most of the patients were treated with prednisone and concomitant nb/bDMARDs. At the first follow-up PET/CT scan after approximately 6.7 ± 3 months, only adalimumab showed a significant reduction in cardiac metabolic activity. Furthermore, comparing all serial follow-up PET/CT scans (143), tumor necrosis factor inhibitor (TNFi)-based therapies showed statistically significant better suppression of myocardial 18F-FDG uptake compared to other treatment regimens. On the last follow-up, most adalimumab-treated patients were inactive (n = 15, 48%) or remitting (n = 11, 35%), and only five patients (16%) were progressive. TNFi was safe even in patients with severely reduced left ventricular ejection fraction (LVEF), and a significant improvement in LVEF under TNFi treatment was observed. Conclusion TNFi shows better control of myocardial inflammation compared to nbDMARDs and corticosteroid monotherapies in patients with CS. TNFi was efficient and safe even in patients with severely reduced LVEF.
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Affiliation(s)
- Lukas Frischknecht
- Department of Immunology, University Hospital Zurich (USZ), Zurich, Switzerland
| | - Jan Schaab
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Eloi Schmauch
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Artturi Ilmari (A.I) Virtanen Institute, University of Eastern Finland, Kuopio, Finland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Ayla Yalamanoglu
- Department of Immunology, University Hospital Zurich (USZ), Zurich, Switzerland
| | - Dennis D. Arnold
- Department of Immunology, University Hospital Zurich (USZ), Zurich, Switzerland
| | - Judith Schwaiger
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Christiane Gruner
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Ronny R. Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Daniel P. Franzen
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| | - Antonios G.A. Kolios
- Department of Immunology, University Hospital Zurich (USZ), Zurich, Switzerland
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | - Jakob Nilsson
- Department of Immunology, University Hospital Zurich (USZ), Zurich, Switzerland
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Welch GM, Boix CA, Schmauch E, Davila-Velderrain J, Victor MB, Dileep V, Bozzelli PL, Su Q, Cheng JD, Lee A, Leary NS, Pfenning AR, Kellis M, Tsai LH. Neurons burdened by DNA double-strand breaks incite microglia activation through antiviral-like signaling in neurodegeneration. Sci Adv 2022; 8:eabo4662. [PMID: 36170369 PMCID: PMC9519048 DOI: 10.1126/sciadv.abo4662] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 07/26/2022] [Indexed: 05/13/2023]
Abstract
DNA double-strand breaks (DSBs) are linked to neurodegeneration and senescence. However, it is not clear how DSB-bearing neurons influence neuroinflammation associated with neurodegeneration. Here, we characterize DSB-bearing neurons from the CK-p25 mouse model of neurodegeneration using single-nucleus, bulk, and spatial transcriptomic techniques. DSB-bearing neurons enter a late-stage DNA damage response marked by nuclear factor κB (NFκB)-activated senescent and antiviral immune pathways. In humans, Alzheimer's disease pathology is closely associated with immune activation in excitatory neurons. Spatial transcriptomics reveal that regions of CK-p25 brain tissue dense with DSB-bearing neurons harbor signatures of inflammatory microglia, which is ameliorated by NFκB knockdown in neurons. Inhibition of NFκB in DSB-bearing neurons also reduces microglia activation in organotypic mouse brain slice culture. In conclusion, DSBs activate immune pathways in neurons, which in turn adopt a senescence-associated secretory phenotype to elicit microglia activation. These findings highlight a previously unidentified role for neurons in the mechanism of disease-associated neuroinflammation.
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Affiliation(s)
- Gwyneth M. Welch
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Carles A. Boix
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eloi Schmauch
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jose Davila-Velderrain
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matheus B. Victor
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vishnu Dileep
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - P. Lorenzo Bozzelli
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qiao Su
- Departments of Computational Biology and Biology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jemmie D. Cheng
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Audrey Lee
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Noelle S. Leary
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andreas R. Pfenning
- Departments of Computational Biology and Biology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
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Schmauch E, Levonen AL, Linna-Kuosmanen S. Global MicroRNA Profiling of Vascular Endothelial Cells. Methods Mol Biol 2022; 2475:157-186. [PMID: 35451756 DOI: 10.1007/978-1-0716-2217-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MicroRNA sequencing (miRNA-seq) enables the detection and characterization of the cell miRNome, including miRNA isoforms (isomiRs) and novel miRNA species. In roughly half of the cases, the most abundant isomiR in the cells is not the reference miRNA given in miRBase, which highlights the importance of isomiR-specific analysis. Here, we describe a gel-free protocol for global miRNA profiling in vascular endothelial cells and the main steps of the subsequent data analysis with two alternative analysis methods. In addition to endothelial cells, the protocol is suitable for other cell and tissue types and has been successfully used to obtain miRNA-seq data from human cardiac tissue, plasma, pericardial fluid, and biofluid exosomes.
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Affiliation(s)
- Eloi Schmauch
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anna-Liisa Levonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Suvi Linna-Kuosmanen
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Schmauch E, Linna-Kuosmanen S, Galani K, Kang K, Adsera C, Park Y, Hollmen M, Gunn J, Kiviniemi T, Garcia-Cardena G, Kellis M. Single-cell transcriptional and epigenomic dissection of human heart in health and coronary artery disease reveals cell-type-specific driver genes and pathways. Atherosclerosis 2021. [DOI: 10.1016/j.atherosclerosis.2021.06.156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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