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Gong M, Zhang Y, Chen N, Ma LL, Feng XM, Yan YX. Proteomics in Cardiovascular disease. Clin Chim Acta 2024; 557:117877. [PMID: 38537675 DOI: 10.1016/j.cca.2024.117877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/14/2024] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
This study focuses on recent advances in proteomics and provides an up-to-date use of this technology in identifying cardiovascular disease (CVD) biomarkers. A total of eight electronic databases (PubMed, EMBASE, Web of Science, Cochrane Library, Wanfang, Vip, Sinomed, and CNKI) were searched and five were used for integrative analysis of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR) and 1 secondary indicator area under the curve (AUC). This systematic review and integrative analysis summarized potential biomarkers previously identified by proteomics. The integrative analysis suggested that proteomics technology had high clinical value in CVD diagnosis. The findings provided new possible directions for the prevention or diagnosis of CVD.
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Affiliation(s)
- Miao Gong
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China; Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yu Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China; Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Ning Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China; Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Lin-Lin Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China; Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xu-Man Feng
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China; Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yu-Xiang Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China; Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
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Huang X, Bai S, Luo Y. Advances in research on biomarkers associated with acute myocardial infarction: A review. Medicine (Baltimore) 2024; 103:e37793. [PMID: 38608048 PMCID: PMC11018244 DOI: 10.1097/md.0000000000037793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 03/14/2024] [Indexed: 04/14/2024] Open
Abstract
Acute myocardial infarction (AMI), the most severe cardiovascular event in clinical settings, imposes a significant burden with its annual increase in morbidity and mortality rates. However, it is noteworthy that mortality due to AMI in developed countries has experienced a decline, largely attributable to the advancements in medical interventions such as percutaneous coronary intervention. This trend highlights the importance of accurate diagnosis and effective treatment to preserve the myocardium at risk and improve patient outcomes. Conventional biomarkers such as myoglobin, creatine kinase isoenzymes, and troponin have been instrumental in the diagnosis of AMI. However, recent years have witnessed the emergence of new biomarkers demonstrating the potential to further enhance the accuracy of AMI diagnosis. This literature review focuses on the recent advancements in biomarker research in the context of AMI diagnosis.
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Affiliation(s)
| | - Suwen Bai
- Central Laboratory, The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People’s Hospital of Shenzhen, Shenzhen, China
| | - Yumei Luo
- Guangdong Medical University, Zhanjiang, China
- Cardiology Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People’s Hospital of Shenzhen, Shenzhen, China
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Mavrogeorgis E, Valkenburg S, Siwy J, Latosinska A, Glorieux G, Mischak H, Jankowski J. Integration of Urinary Peptidome and Fecal Microbiome to Explore Patient Clustering in Chronic Kidney Disease. Proteomes 2024; 12:11. [PMID: 38651370 PMCID: PMC11036268 DOI: 10.3390/proteomes12020011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/15/2024] [Accepted: 03/22/2024] [Indexed: 04/25/2024] Open
Abstract
Millions of people worldwide currently suffer from chronic kidney disease (CKD), requiring kidney replacement therapy at the end stage. Endeavors to better understand CKD pathophysiology from an omics perspective have revealed major molecular players in several sample sources. Focusing on non-invasive sources, gut microbial communities appear to be disturbed in CKD, while numerous human urinary peptides are also dysregulated. Nevertheless, studies often focus on isolated omics techniques, thus potentially missing the complementary pathophysiological information that multidisciplinary approaches could provide. To this end, human urinary peptidome was analyzed and integrated with clinical and fecal microbiome (16S sequencing) data collected from 110 Non-CKD or CKD individuals (Early, Moderate, or Advanced CKD stage) that were not undergoing dialysis. Participants were visualized in a three-dimensional space using different combinations of clinical and molecular data. The most impactful clinical variables to discriminate patient groups in the reduced dataspace were, among others, serum urea, haemoglobin, total blood protein, urinary albumin, urinary erythrocytes, blood pressure, cholesterol measures, body mass index, Bristol stool score, and smoking; relevant variables were also microbial taxa, including Roseburia, Butyricicoccus, Flavonifractor, Burkholderiales, Holdemania, Synergistaceae, Enterorhabdus, and Senegalimassilia; urinary peptidome fragments were predominantly derived from proteins of collagen origin; among the non-collagen parental proteins were FXYD2, MGP, FGA, APOA1, and CD99. The urinary peptidome appeared to capture substantial variation in the CKD context. Integrating clinical and molecular data contributed to an improved cohort separation compared to clinical data alone, indicating, once again, the added value of this combined information in clinical practice.
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Affiliation(s)
- Emmanouil Mavrogeorgis
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (E.M.); (J.S.); (A.L.); (H.M.)
- Institute for Molecular Cardiovascular Research (IMCAR), RWTH Aachen University Hospital, 52074 Aachen, Germany
| | - Sophie Valkenburg
- Nephrology Unit, Department of Internal Medicine and Pediatrics, Ghent University Hospital, 9000 Ghent, Belgium; (S.V.); (G.G.)
| | - Justyna Siwy
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (E.M.); (J.S.); (A.L.); (H.M.)
| | - Agnieszka Latosinska
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (E.M.); (J.S.); (A.L.); (H.M.)
| | - Griet Glorieux
- Nephrology Unit, Department of Internal Medicine and Pediatrics, Ghent University Hospital, 9000 Ghent, Belgium; (S.V.); (G.G.)
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (E.M.); (J.S.); (A.L.); (H.M.)
| | - Joachim Jankowski
- Institute for Molecular Cardiovascular Research (IMCAR), RWTH Aachen University Hospital, 52074 Aachen, Germany
- Experimental Vascular Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, 6229 Maastricht, The Netherlands
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4
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Mone P, Tesorio T, De Donato A, Cioppa A, Jankauskas SS, Salemme L, Santulli G. A novel urinary proteomic classifier predicts the risk of coronary artery disease. Eur J Prev Cardiol 2023; 30:1535-1536. [PMID: 37075225 PMCID: PMC10562135 DOI: 10.1093/eurjpc/zwad123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 04/21/2023]
Affiliation(s)
- Pasquale Mone
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Neuroimmunology and Inflammation (INI), Albert Einstein College of Medicine, 1300 Morris Park Avenue, 10461 New York City, NY 10461, USA
- University of Campania ‘Luigi Vanvitelli’, Naples, Italy
| | | | | | - Angelo Cioppa
- ‘Montevergine’ Clinic, Mercogliano (Avellino), Italy
| | - Stanislovas S Jankauskas
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Neuroimmunology and Inflammation (INI), Albert Einstein College of Medicine, 1300 Morris Park Avenue, 10461 New York City, NY 10461, USA
- University of Campania ‘Luigi Vanvitelli’, Naples, Italy
| | - Luigi Salemme
- ‘Montevergine’ Clinic, Mercogliano (Avellino), Italy
| | - Gaetano Santulli
- Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Einstein Institute for Neuroimmunology and Inflammation (INI), Albert Einstein College of Medicine, 1300 Morris Park Avenue, 10461 New York City, NY 10461, USA
- Department of Molecular Pharmacology, Einstein-Mount Sinai Diabetes Research Center (ES-DRC), Fleischer Institute for Diabetes and Metabolism (FIDAM), Einstein Institute for Aging Research, Albert Einstein College of Medicine, 1300 Morris Park Avenue, 10461 New York City, NY 10461, USA
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Wei D, Melgarejo JD, Van Aelst L, Vanassche T, Verhamme P, Janssens S, Peter K, Zhang ZY. Prediction of coronary artery disease using urinary proteomics. Eur J Prev Cardiol 2023; 30:1537-1546. [PMID: 36943304 DOI: 10.1093/eurjpc/zwad087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/13/2023] [Accepted: 03/20/2023] [Indexed: 03/23/2023]
Abstract
AIMS Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD. METHODS AND RESULTS Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly). CONCLUSION A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.
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Affiliation(s)
- Dongmei Wei
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium
| | - Jesus D Melgarejo
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium
| | - Lucas Van Aelst
- Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Thomas Vanassche
- Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Peter Verhamme
- Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Stefan Janssens
- Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Karlheinz Peter
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne VIC 3004, Australia
- Department of Cardiology, The Alfred Hospital, 55 Commercial Rd, Melbourne VIC 3004, Australia
| | - Zhen-Yu Zhang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium
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Keller F, Beige J, Siwy J, Mebazaa A, An D, Mischak H, Schanstra JP, Mokou M, Perco P, Staessen JA, Vlahou A, Latosinska A. Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios. J Transl Med 2023; 21:663. [PMID: 37741989 PMCID: PMC10518109 DOI: 10.1186/s12967-023-04508-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/07/2023] [Indexed: 09/25/2023] Open
Abstract
BACKGROUND There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. METHODS Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. RESULTS In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17-1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47-1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39-1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). CONCLUSION The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death.
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Affiliation(s)
- Felix Keller
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, 6020, Innsbruck, Austria
| | - Joachim Beige
- Martin-Luther-University Halle-Wittenberg, 06108, Halle (Saale), Germany
- Kuratorium for Dialysis and Transplantation, 04129, Leipzig, Germany
| | - Justyna Siwy
- Mosaiques Diagnostics GmbH, 30659, Hannover, Germany
| | - Alexandre Mebazaa
- Department of Anaesthesiology and Critical Care, Hôpital Lariboisière, AP-HP, 75010, Paris, France
| | - Dewei An
- Non-Profit Research Association Alliance for the Promotion of Preventive Medicine, 2800, Mechelen, Belgium
| | | | - Joost P Schanstra
- Institute of Cardiovascular and Metabolic Disease, U1297, Institut National de la Santé et de la Recherche Médicale, 31432, Toulouse, France
- Université Toulouse III Paul-Sabatier, 31062, Toulouse, France
| | - Marika Mokou
- Mosaiques Diagnostics GmbH, 30659, Hannover, Germany
| | - Paul Perco
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, 6020, Innsbruck, Austria
| | - Jan A Staessen
- Non-Profit Research Association Alliance for the Promotion of Preventive Medicine, 2800, Mechelen, Belgium
| | - Antonia Vlahou
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527, Athens, Greece
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7
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Jaimes Campos MA, Andújar I, Keller F, Mayer G, Rossing P, Staessen JA, Delles C, Beige J, Glorieux G, Clark AL, Mullen W, Schanstra JP, Vlahou A, Rossing K, Peter K, Ortiz A, Campbell A, Persson F, Latosinska A, Mischak H, Siwy J, Jankowski J. Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study. Pharmaceuticals (Basel) 2023; 16:1298. [PMID: 37765106 PMCID: PMC10537115 DOI: 10.3390/ph16091298] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient's urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted.
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Affiliation(s)
- Mayra Alejandra Jaimes Campos
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.A.J.C.); (A.L.); (H.M.); (J.S.)
- Institute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Iván Andújar
- Proteomic Laboratory, Center for Genetic Engineering and Biotechnology, Havana 10600, Cuba
| | - Felix Keller
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, 6020 Innsbruck, Austria; (F.K.); (G.M.)
| | - Gert Mayer
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, 6020 Innsbruck, Austria; (F.K.); (G.M.)
| | - Peter Rossing
- Steno Diabetes Center Copenhagen, 2730 Herlev, Denmark; (P.R.); (F.P.)
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark;
| | - Jan A. Staessen
- Non-Profit Research Institute Alliance for the Promotion of Preventive Medicine, 2800 Mechlin, Belgium;
| | - Christian Delles
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK; (C.D.); (W.M.)
| | - Joachim Beige
- Division of Nephrology and KfH Renal Unit, Hospital St Georg, 04129 Leipzig, Germany;
- Medical Clinic 2, Martin-Luther-University Halle/Wittenberg, 06112 Halle, Germany
| | - Griet Glorieux
- Nephrology Section, Department of Internal Medicine, Ghent University Hospital, 9000 Ghent, Belgium;
| | - Andrew L. Clark
- Hull University Teaching Hospitals NHS Trust, Castle Hill Hospital, Cottingham HU16 5JQ, UK;
| | - William Mullen
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK; (C.D.); (W.M.)
| | - Joost P. Schanstra
- Institut National de la Santé et de la Recherche Médicale, Institute of Cardiovascular and Metabolic Disease, UMRS 1297, 31432 Toulouse, France;
- Renal Fibrosis, Université Toulouse III Paul-Sabatier, Route de Narbonne, 31062 Toulouse, France
| | - Antonia Vlahou
- Centre of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), 115 27 Athens, Greece;
| | - Kasper Rossing
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark;
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Karlheinz Peter
- Atherothrombosis and Vascular Biology Program, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia;
- Department of Physiology, Anatomy, Microbiology, La Trobe University, Melbourne, VIC 3083, Australia
- Department of Medicine and Immunology, Monash University, Melbourne, VIC 3800, Australia
- Department of Cardiometabolic Health, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alberto Ortiz
- Instituto de Investigación Sanitaria de la Fundación Jiménez Díaz UAM, 28040 Madrid, Spain;
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH16 4SB, UK;
| | - Frederik Persson
- Steno Diabetes Center Copenhagen, 2730 Herlev, Denmark; (P.R.); (F.P.)
| | - Agnieszka Latosinska
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.A.J.C.); (A.L.); (H.M.); (J.S.)
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.A.J.C.); (A.L.); (H.M.); (J.S.)
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK; (C.D.); (W.M.)
| | - Justyna Siwy
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.A.J.C.); (A.L.); (H.M.); (J.S.)
| | - Joachim Jankowski
- Institute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, 6211 Maastricht, The Netherlands
- Aachen-Maastricht Institute for Cardiorenal Disease (AMICARE), University Hospital RWTH Aachen, 52074 Aachen, Germany
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Wendt R, Siwy J, He T, Latosinska A, Wiech T, Zipfel PF, Tserga A, Vlahou A, Rupprecht H, Catanese L, Mischak H, Beige J. Molecular Mapping of Urinary Complement Peptides in Kidney Diseases. Proteomes 2021; 9:proteomes9040049. [PMID: 34941814 PMCID: PMC8709096 DOI: 10.3390/proteomes9040049] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/03/2021] [Accepted: 12/08/2021] [Indexed: 02/07/2023] Open
Abstract
Defective complement activation has been associated with various types of kidney disease. This led to the hypothesis that specific urine complement fragments may be associated with kidney disease etiologies, and disease progression may be reflected by changes in these complement fragments. We investigated the occurrence of complement fragments in urine, their association with kidney function and disease etiology in 16,027 subjects, using mass spectrometry based peptidomics data from the Human Urinary Proteome/Peptidome Database. Twenty-three different urinary peptides originating from complement proteins C3, C4 and factor B (CFB) could be identified. Most C3-derived peptides showed inverse association with estimated glomerular filtration rate (eGFR), while the majority of peptides derived from CFB demonstrated positive association with eGFR. Several peptides derived from the complement proteins C3, C4 and CFB were found significantly associated with specific kidney disease etiologies. These peptides may depict disease-specific complement activation and could serve as non-invasive biomarkers to support development of complement interventions through assessing complement activity for patients’ stratification and monitoring of drug impact. Further investigation of these complement peptides may provide additional insight into disease pathophysiology and could possibly guide therapeutic decisions, especially when targeting complement factors.
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Affiliation(s)
- Ralph Wendt
- Department of Nephrology and Kuratorium for Dialysis and Transplantation (KfH) Renal Unit, Hospital St. Georg, 04129 Leipzig, Germany;
| | - Justyna Siwy
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (J.S.); (T.H.); (A.L.); (H.M.)
| | - Tianlin He
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (J.S.); (T.H.); (A.L.); (H.M.)
| | - Agnieszka Latosinska
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (J.S.); (T.H.); (A.L.); (H.M.)
| | - Thorsten Wiech
- Nephropathology Section, Institute of Pathology, University Medical Center, 20246 Hamburg, Germany;
| | - Peter F. Zipfel
- Institute of Microbiology, Friedrich-Schiller-University, 07743 Jena, Germany;
- Department of Infection Biology, Leibniz Institute for Natural Product Researach and Infection Biology, 07745 Jena, Germany
| | - Aggeliki Tserga
- Biomedical Research Foundation, Academy of Athens, Department of Biotechnology, 11527 Athens, Greece; (A.T.); (A.V.)
| | - Antonia Vlahou
- Biomedical Research Foundation, Academy of Athens, Department of Biotechnology, 11527 Athens, Greece; (A.T.); (A.V.)
| | - Harald Rupprecht
- Department of Nephrology, Klinikum Bayreuth GmbH, 95447 Bayreuth, Germany; (H.R.); (L.C.)
| | - Lorenzo Catanese
- Department of Nephrology, Klinikum Bayreuth GmbH, 95447 Bayreuth, Germany; (H.R.); (L.C.)
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (J.S.); (T.H.); (A.L.); (H.M.)
| | - Joachim Beige
- Department of Nephrology and Kuratorium for Dialysis and Transplantation (KfH) Renal Unit, Hospital St. Georg, 04129 Leipzig, Germany;
- Department of Internal Medicine II, Martin-Luther-University Halle-Wittenberg, 06108 Halle (Saale), Germany
- Correspondence: ; Tel.: +49-341-909-4896
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9
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Rotbain Curovic V, Magalhães P, He T, Hansen TW, Mischak H, Rossing P. Urinary peptidome and diabetic retinopathy in the DIRECT-Protect 1 and 2 trials. Diabet Med 2021; 38:e14634. [PMID: 34228837 DOI: 10.1111/dme.14634] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Given the association of diabetic retinopathy (DR) and kidney disease, we investigated the urinary peptidome to presence and deterioration of DR in a post hoc analysis of trials investigating the effect of candesartan on progression of DR in type 1 and type 2 diabetes, respectively. METHODS Baseline urinary peptidomic analysis was performed on a random selection of 783 and 792 subjects in two randomized controlled trials, DIRECT-Protect 1 and 2, respectively. End points were two-step (RET2) and three-step (RET3) change in Early Treatment of Diabetic Retinopathy Study protocol (ETDRS) defined level. Peptide levels were correlated to baseline EDTRS level in a discovery set of 2/3 of the participants from DIRECT-Protect 1. The identified peptides were then validated cross-sectionally in the remaining 1/3 from DIRECT-Protect 1. Thereafter, peptides identified in the discovery set were assessed in the entire DIRECT-Protect 1 and 2 cohorts and significant peptides were tested longitudinally. RESULTS Follow-up ranged 4.0-4.7 years. 24 peptides were associated with baseline DR in the discovery set. COL3A1 (seq: NTG~) and COL4A1 (seq: DGA~) were associated with baseline DR in the validation set (Rho: -.223, p < 0.001 and Rho: -.141, p = 0.024). Neither was significantly associated with end points. Assessing the 24 identified peptides in the entire cohorts, several collagen peptides were associated with baseline DR and end points; however, there was no overlap across diabetes types. CONCLUSIONS We identified several urinary peptides (mainly collagen) associated with the presence of DR, however they could not be conclusively associated with worsening of DR.
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Affiliation(s)
| | | | - Tianlin He
- Mosaiques Diagnostics, Hannover, Germany
- Institute for Molecular Cardiovascular Research, University of Aachen, Aachen, Germany
| | | | | | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- University of Copenhagen, Copenhagen, Denmark
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10
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Pathophysiological Implications of Urinary Peptides in Hepatocellular Carcinoma. Cancers (Basel) 2021; 13:cancers13153786. [PMID: 34359689 PMCID: PMC8345155 DOI: 10.3390/cancers13153786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/21/2021] [Accepted: 07/26/2021] [Indexed: 11/23/2022] Open
Abstract
Simple Summary In this study, the application of capillary electrophoresis mass spectrometry enabled identification of 31 urinary peptides significantly associated with hepatocellular carcinoma diagnosis and prognosis. Further assessment of these peptides lead to prediction of cellular proteases involved in their development namely Meprin A subunit α and Kallikrein-6. Subsequent identification of the proteases was verified by immunohistochemistry in normal liver, cirrhosis and hepatocellular carcinoma. Histopathological assessment of the proteases revealed numerical gradient staining signifying their involvement in liver fibrosis and hepatocellular carcinoma formation. The discovered urinary peptides offered a potential noninvasive tool for diagnosis and prognosis of hepatocellular carcinoma. Abstract Hepatocellular carcinoma (HCC) is known to be associated with protein alterations and extracellular fibrous deposition. We investigated the urinary proteomic profiles of HCC patients in this prospective cross sectional multicentre study. 195 patients were recruited from the UK (Coventry) and Germany (Hannover) between 1 January 2013 and 30 June 2019. Out of these, 57 were HCC patients with a background of liver cirrhosis (LC) and 138 were non-HCC controls; 72 patients with LC, 57 with non-cirrhotic liver disease and 9 with normal liver function. Analysis of the urine samples was performed by capillary electrophoresis (CE) coupled to mass spectrometry (MS). Peptide sequences were obtained and 31 specific peptide markers for HCC were identified and further integrated into a multivariate classification model. The peptide model demonstrated 79.5% sensitivity and 85.1% specificity (95% CI: 0.81–0.93, p < 0.0001) for HCC and 4.1-fold increased risk of death (95% CI: 1.7–9.8, p = 0.0005). Proteases potentially involved in HCC progression were mapped to the N- and C-terminal sequence motifs of the CE-MS peptide markers. In silico protease prediction revealed that kallikrein-6 (KLK6) elicits increased activity, whilst Meprin A subunit α (MEP1A) has reduced activity in HCC compared to the controls. Tissue expression of KLK6 and MEP1A was subsequently verified by immunohistochemistry.
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Palasubramaniam J, Wang X, Peter K. Myocardial Infarction-From Atherosclerosis to Thrombosis. Arterioscler Thromb Vasc Biol 2019; 39:e176-e185. [PMID: 31339782 DOI: 10.1161/atvbaha.119.312578] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Jathushan Palasubramaniam
- From the Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia (J.P., X.W., K.P.).,Department of Medicine, Monash University, Melbourne, Australia (J.P., X.W., K.P.).,Department of Cardiology, Alfred Hospital, Melbourne, Australia (J.P., K.P.)
| | - Xiaowei Wang
- From the Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia (J.P., X.W., K.P.).,Department of Medicine, Monash University, Melbourne, Australia (J.P., X.W., K.P.)
| | - Karlheinz Peter
- From the Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia (J.P., X.W., K.P.).,Department of Medicine, Monash University, Melbourne, Australia (J.P., X.W., K.P.).,Department of Cardiology, Alfred Hospital, Melbourne, Australia (J.P., K.P.)
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12
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Latosinska A, Siwy J, Mischak H, Frantzi M. Peptidomics and proteomics based on CE‐MS as a robust tool in clinical application: The past, the present, and the future. Electrophoresis 2019; 40:2294-2308. [DOI: 10.1002/elps.201900091] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 04/16/2019] [Accepted: 04/16/2019] [Indexed: 12/23/2022]
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13
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McFadyen JD, Meikle PJ, Peter K. Platelet lipidomics: a window of opportunity to assess cardiovascular risk? Eur Heart J 2019; 38:2006-2008. [PMID: 28520938 DOI: 10.1093/eurheartj/ehx258] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Affiliation(s)
- James D McFadyen
- Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Peter J Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Karlheinz Peter
- Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
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14
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Watanabe Y, Hirao Y, Kasuga K, Tokutake T, Semizu Y, Kitamura K, Ikeuchi T, Nakamura K, Yamamoto T. Molecular Network Analysis of the Urinary Proteome of Alzheimer's Disease Patients. Dement Geriatr Cogn Dis Extra 2019; 9:53-65. [PMID: 31043964 PMCID: PMC6477484 DOI: 10.1159/000496100] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 12/07/2018] [Indexed: 12/27/2022] Open
Abstract
Background/Aims The identification of predictive biomarkers for Alzheimer's disease (AD) from urine would aid in screening for the disease, but information about biological and pathophysiological changes in the urine of AD patients is limited. This study aimed to explore the comprehensive profile and molecular network relations of urinary proteins in AD patients. Methods Urine samples collected from 18 AD patients and 18 age- and sex-matched cognitively normal controls were analyzed by mass spectrometry and semiquantified with the normalized spectral index method. Bioinformatics analyses were performed on proteins which significantly increased by more than 2-fold or decreased by less than 0.5-fold compared to the control (p < 0.05) using DAVID bioinformatics resources and KeyMolnet software. Results The levels of 109 proteins significantly differed between AD patients and controls. Among these, annotation clusters related to lysosomes, complement activation, and gluconeogenesis were significantly enriched. The molecular relation networks derived from these proteins were mainly associated with pathways of lipoprotein metabolism, heat shock protein 90 signaling, matrix metalloproteinase signaling, and redox regulation by thioredoxin. Conclusion Our findings suggest that changes in the urinary proteome of AD patients reflect systemic changes related to AD pathophysiology.
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Affiliation(s)
- Yumi Watanabe
- Division of Preventive Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | | | - Kensaku Kasuga
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Takayoshi Tokutake
- Department of Neurology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Yuka Semizu
- Division of Preventive Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kaori Kitamura
- Division of Preventive Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Kazutoshi Nakamura
- Division of Preventive Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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Sánchez-Juanes F, González-Buitrago JM. Sample Treatment for Urine Proteomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1073:125-135. [PMID: 31236841 DOI: 10.1007/978-3-030-12298-0_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Urine is a biological fluid that can be collected noninvasively in relatively large quantities which can be used for the search of biomarkers of disease, both diseases of the urological tract and systemic diseases. One of the most important aspects in proteomic studies is sample treatment before further analysis. Methods of preparation of a urine sample depend on the techniques that will be used later for separation and identification of the proteins. Also, urine preparation should be as simple as possible to increase reproducibility. Normal urine has a much diluted protein concentration with a high-salt content, which interferes with proteomic analysis. Thus, an initial step in the handling of urine sample should be to concentrate and eliminate salts. As range of protein concentrations in urine spans several orders of magnitude, effective proteomic analyses require either removal of most abundant protein or enrichment of the less abundant ones. In this chapter, we discuss the aspects related to the collection and treatment of urine for proteomic studies.
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Affiliation(s)
- Fernando Sánchez-Juanes
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Complejo Asistencial Universitario de Salamanca, Salamanca, Spain.,Departamento de Bioquímica y Biología Molecular, Universidad de Salamanca, Salamanca, Spain
| | - José Manuel González-Buitrago
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Complejo Asistencial Universitario de Salamanca, Salamanca, Spain. .,Departamento de Bioquímica y Biología Molecular, Universidad de Salamanca, Salamanca, Spain. .,Servicio de Análisis Clínicos/Bioquímica Clínica, Complejo Asistencial Universitario de Salamanca, Salamanca, Spain.
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Oellgaard J, Gæde P, Persson F, Rossing P, Parving HH, Pedersen O. Application of urinary proteomics as possible risk predictor of renal and cardiovascular complications in patients with type 2-diabetes and microalbuminuria. J Diabetes Complications 2018; 32:1133-1140. [PMID: 30282584 DOI: 10.1016/j.jdiacomp.2018.09.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 09/13/2018] [Accepted: 09/18/2018] [Indexed: 01/30/2023]
Abstract
BACKGROUND Analyses of the urinary proteome have been proposed as a novel approach for early assessment of increased risk of renal- or cardiovascular disease. Here we investigate the potentials of various classifiers derived from urinary proteomics for prediction of renal and cardiovascular comorbidities in patients with type 2-diabetes. METHODS The study was a post hoc analysis of the randomized controlled Steno-2 trial comparing intensified multifactorial intervention to conventional treatment of type 2-diabetes and microalbuminuria. 151 diabetic patients with persistent microalbuminuria were included in year 1995 and followed for up to 19 years. For renal outcomes, two classifiers (CKD273 and a novel, GFR-based classifier) and for cardiovascular outcomes, three classifiers (CAD238, ACSP and ACSP75) were applied. Renal endpoints were progression to macroalbuminuria, impaired renal function (GFR < 45 ml/min/1.73 m2) or progression to end stage renal disease (ESRD) or death. Cardiovascular endpoints were coronary artery disease and a composite endpoint of incident death of cardiovascular disease, myocardial infarction or revascularization, stroke, amputation or peripheral revascularization. RESULTS CKD273 was not consistently associated with renal outcomes. The GFR-based classifier was associated with impaired renal function, but lost significance in extensively adjusted models. Both the ACSP75 and ACSP-scores, but not the CAD238-score were inversely associated (opposing the hypothesis) with cardiovascular endpoints. None of the classifiers improved prediction of any outcome on top of standard risk factors. CONCLUSIONS Risk-scores based upon urinary proteomics did not improve prediction of renal and cardiovascular endpoints on top of standard risk factors such as age and GFR during long-term (19 years) follow up in patients with type 2-diabetes and microalbuminuria.
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Affiliation(s)
- Jens Oellgaard
- Slagelse Hospital, Slagelse, Denmark; University of Southern Denmark, Odense, Denmark; Steno Diabetes Center, Gentofte, Denmark.
| | - Peter Gæde
- Slagelse Hospital, Slagelse, Denmark; University of Southern Denmark, Odense, Denmark.
| | | | - Peter Rossing
- Steno Diabetes Center, Gentofte, Denmark; University of Copenhagen, Denmark; Aarhus University, Aarhus, Denmark.
| | - Hans-Henrik Parving
- University of Copenhagen, Denmark; Department of Medical Endocrinology, Rigshospitalet, Denmark.
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Copenhagen, Denmark.
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17
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Huang QF, Van Keer J, Zhang ZY, Trenson S, Nkuipou-Kenfack E, Van Aelst LNL, Yang WY, Thijs L, Wei FF, Ciarka A, Vanhaecke J, Janssens S, Van Cleemput J, Mischak H, Staessen JA. Urinary proteomic signatures associated with β-blockade and heart rate in heart transplant recipients. PLoS One 2018; 13:e0204439. [PMID: 30248148 PMCID: PMC6152976 DOI: 10.1371/journal.pone.0204439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 09/08/2018] [Indexed: 01/14/2023] Open
Abstract
Objectives Heart transplant (HTx) recipients have a high heart rate (HR), because of graft denervation and are frequently started on β-blockade (BB). We assessed whether BB and HR post HTx are associated with a specific urinary proteomic signature. Methods In 336 HTx patients (mean age, 56.8 years; 22.3% women), we analyzed cross-sectional data obtained 7.3 years (median) after HTx. We recorded medication use, measured HR during right heart catheterization, and applied capillary electrophoresis coupled with mass spectrometry to determine the multidimensional urinary classifiers HF1 and HF2 (known to be associated with left ventricular dysfunction), ACSP75 (acute coronary syndrome) and CKD273 (renal dysfunction) and 48 sequenced urinary peptides revealing the parental proteins. Results In adjusted analyses, HF1, HF2 and CKD273 (p ≤ 0.024) were higher in BB users than non-users with a similar trend for ACSP75 (p = 0.06). Patients started on BB within 1 year after HTx and non-users had similar HF1 and HF2 levels (p ≥ 0.098), whereas starting BB later was associated with higher HF1 and HF2 compared with non-users (p ≤ 0.014). There were no differences in the urinary biomarkers (p ≥ 0.27) according to HR. BB use was associated with higher urinary levels of collagen II and III fragments and non-use with higher levels of collagen I fragments. Conclusions BB use, but not HR, is associated with a urinary proteomic signature that is usually associated with worse outcome, because unhealthier conditions probably lead to initiation of BB. Starting BB early after HTx surgery might be beneficial.
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Affiliation(s)
- Qi-Fang Huang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
- Center for Epidemiological Studies and Clinical Trials and Center for Vascular Evaluations, Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jan Van Keer
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Zhen-Yu Zhang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
- Department of Cardiology, Shanghai General Hospital, Shanghai, China
| | - Sander Trenson
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | | | | | - Wen-Yi Yang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
- Department of Cardiology, Shanghai General Hospital, Shanghai, China
| | - Lutgarde Thijs
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Fang-Fei Wei
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Agnieszka Ciarka
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Johan Vanhaecke
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Stefan Janssens
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | | | - Harald Mischak
- Mosaiques Diagnostics GmbH. Hannover, Germany
- BHF Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Jan A. Staessen
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- * E-mail: ,
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18
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Lygirou V, Latosinska A, Makridakis M, Mullen W, Delles C, Schanstra JP, Zoidakis J, Pieske B, Mischak H, Vlahou A. Plasma proteomic analysis reveals altered protein abundances in cardiovascular disease. J Transl Med 2018; 16:104. [PMID: 29665821 PMCID: PMC5905170 DOI: 10.1186/s12967-018-1476-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 04/06/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Cardiovascular disease (CVD) describes the pathological conditions of the heart and blood vessels. Despite the large number of studies on CVD and its etiology, its key modulators remain largely unknown. To this end, we performed a comprehensive proteomic analysis of blood plasma, with the scope to identify disease-associated changes after placing them in the context of existing knowledge, and generate a well characterized dataset for further use in CVD multi-omics integrative analysis. METHODS LC-MS/MS was employed to analyze plasma from 32 subjects (19 cases of various CVD phenotypes and 13 controls) in two steps: discovery (13 cases and 8 controls) and test (6 cases and 5 controls) set analysis. Following label-free quantification, the detected proteins were correlated to existing plasma proteomics datasets (plasma proteome database; PPD) and functionally annotated (Cytoscape, Ingenuity Pathway Analysis). Differential expression was defined based on identification confidence (≥ 2 peptides per protein), statistical significance (Mann-Whitney p value ≤ 0.05) and a minimum of twofold change. RESULTS Peptides detected in at least 50% of samples per group were considered, resulting in a total of 3796 identified proteins (838 proteins based on ≥ 2 peptides). Pathway annotation confirmed the functional relevance of the findings (representation of complement cascade, fibrin clot formation, platelet degranulation, etc.). Correlation of the relative abundance of the proteins identified in the discovery set with their reported concentrations in the PPD was significant, confirming the validity of the quantification method. The discovery set analysis revealed 100 differentially expressed proteins between cases and controls, 39 of which were verified (≥ twofold change) in the test set. These included proteins already studied in the context of CVD (such as apolipoprotein B, alpha-2-macroglobulin), as well as novel findings (such as low density lipoprotein receptor related protein 2 [LRP2], protein SZT2) for which a mechanism of action is suggested. CONCLUSIONS This proteomic study provides a comprehensive dataset to be used for integrative and functional studies in the field. The observed protein changes reflect known CVD-related processes (e.g. lipid uptake, inflammation) but also novel hypotheses for further investigation including a potential pleiotropic role of LPR2 but also links of SZT2 to CVD.
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Affiliation(s)
- Vasiliki Lygirou
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou Street, 115 27, Athens, Greece
| | | | - Manousos Makridakis
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou Street, 115 27, Athens, Greece
| | - William Mullen
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | - Christian Delles
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | - Joost P Schanstra
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, Toulouse, France.,Université Toulouse III Paul-Sabatier, Toulouse, France
| | - Jerome Zoidakis
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou Street, 115 27, Athens, Greece
| | - Burkert Pieske
- Deutsches Herzzentrum Berlin, Augustenburger Pl. 1, 13353, Berlin, Germany
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, Rotenburger Straße 20, 30659, Hannover, Germany
| | - Antonia Vlahou
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou Street, 115 27, Athens, Greece.
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Currie GE, von Scholten BJ, Mary S, Flores Guerrero JL, Lindhardt M, Reinhard H, Jacobsen PK, Mullen W, Parving HH, Mischak H, Rossing P, Delles C. Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria. Cardiovasc Diabetol 2018; 17:50. [PMID: 29625564 PMCID: PMC5889591 DOI: 10.1186/s12933-018-0697-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/02/2018] [Indexed: 01/01/2023] Open
Abstract
Background The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers. Electronic supplementary material The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gemma E Currie
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.
| | | | - Sheon Mary
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | - Jose-Luis Flores Guerrero
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | | | | | | | - William Mullen
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | | | - Harald Mischak
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.,Mosaiques Diagnostics, Hanover, Germany
| | - Peter Rossing
- Steno Diabetes Center, Gentofte, Copenhagen, Denmark.,HEALTH, University of Aarhus, Aarhus, Denmark.,Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Delles
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
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Röthlisberger S, Pedroza-Diaz J. Urine protein biomarkers for detection of cardiovascular disease and their use for the clinic. Expert Rev Proteomics 2017; 14:1091-1103. [DOI: 10.1080/14789450.2017.1394188] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sarah Röthlisberger
- Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Medellín, Colombia
| | - Johanna Pedroza-Diaz
- Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Medellín, Colombia
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Latosinska A, Frantzi M, Vlahou A, Merseburger AS, Mischak H. Clinical Proteomics for Precision Medicine: The Bladder Cancer Case. Proteomics Clin Appl 2017; 12. [DOI: 10.1002/prca.201700074] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 08/10/2017] [Indexed: 12/15/2022]
Affiliation(s)
| | | | - Antonia Vlahou
- Biotechnology Division; Biomedical Research Foundation; Academy of Athens; Athens Greece
| | - Axel S. Merseburger
- Department of Urology; Campus Lübeck; University Hospital Schleswig-Holstein; Lübeck Germany
| | - Harald Mischak
- Mosaiques Diagnostics GmbH; Hannover Germany
- BHF Glasgow Cardiovascular Research Centre; University of Glasgow; Glasgow UK
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Huang QF, Trenson S, Zhang ZY, Yang WY, Van Aelst L, Nkuipou-Kenfack E, Wei FF, Mujaj B, Thijs L, Ciarka A, Zoidakis J, Droogné W, Vlahou A, Janssens S, Vanhaecke J, Van Cleemput J, Staessen JA. Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET)-Rationale and database description. PLoS One 2017; 12:e0184443. [PMID: 28880921 PMCID: PMC5589218 DOI: 10.1371/journal.pone.0184443] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 08/23/2017] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVES Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET; NCT03152422) aims: (i) to construct new multidimensional urinary proteomic (UP) classifiers that after heart transplantation (HTx) help in detecting graft vasculopathy, monitoring immune system activity and graft performance, and in adjusting immunosuppression; (ii) to sequence UP peptide fragments and to identify key proteins mediating HTx-related complications; (iii) to validate UP classifiers by demonstrating analogy between UP profiles and tissue proteomic signatures (TP) in diseased explanted hearts, to be compared with normal donor hearts; (iv) and to identify new drug targets. This article describes the uPROPHET database construction, follow-up strategies and baseline characteristics of the HTx patients. METHODS HTx patients enrolled at the University Hospital Gasthuisberg (Leuven) collected mid-morning urine samples. Cardiac biopsies were obtained at HTx. UP and TP methods and the statistical work flow in pursuit of the research objectives are described in detail in the Data supplement. RESULTS Of 352 participants in the UP study (24.4% women), 38.9%, 40.3%, 5.7% and 15.1% had ischemic, dilated, hypertrophic or other cardiomyopathy. The median interval between HTx and first UP assessment (baseline) was 7.8 years. At baseline, mean values were 56.5 years for age, 25.2 kg/m2 for body mass index, 142.3/84.8 mm Hg and 124.2/79.8 mm Hg for office and 24-h ambulatory systolic/diastolic pressure, and 58.6 mL/min/1.73 m2 for the estimated glomerular filtration rate. Of all patients, 37.2% and 6.5% had a history of mild (grade = 1B) or severe (grade ≥ 2) cellular rejection. Anti-body mediated rejection had occurred in 6.2% patients. The number of follow-up urine samples available for future analyses totals over 950. The TP study currently includes biopsies from 7 healthy donors and 15, 14, and 3 patients with ischemic, dilated, and hypertrophic cardiomyopathy. CONCLUSIONS uPROPHET constitutes a solid resources for UP and TP research in the field of HTx and has the ambition to lay the foundation for the clinical application of UP in risk stratification in HTx patients.
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Affiliation(s)
- Qi-Fang Huang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
- Center for Epidemiological Studies and Clinical Trials and Center for Vascular Evaluations, Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Sander Trenson
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Zhen-Yu Zhang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Wen-Yi Yang
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Lucas Van Aelst
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | | | - Fang-Fei Wei
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Blerim Mujaj
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Lutgarde Thijs
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Agnieszka Ciarka
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Jerome Zoidakis
- Biotechnology Division, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Walter Droogné
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Antonia Vlahou
- Biotechnology Division, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Stefan Janssens
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Johan Vanhaecke
- Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | | | - Jan A. Staessen
- Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
- R&D Group VitaK, Maastricht University, Maastricht, The Netherlands
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