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Mutithu DW, Aremu OO, Mokaila D, Bana T, Familusi M, Taylor L, Martin LJ, Heathfield LJ, Kirwan JA, Wiesner L, Adeola HA, Lumngwena EN, Manganyi R, Skatulla S, Naidoo R, Ntusi NAB. A study protocol to characterise pathophysiological and molecular markers of rheumatic heart disease and degenerative aortic stenosis using multiparametric cardiovascular imaging and multiomics techniques. PLoS One 2024; 19:e0303496. [PMID: 38739622 PMCID: PMC11090351 DOI: 10.1371/journal.pone.0303496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
INTRODUCTION Rheumatic heart disease (RHD), degenerative aortic stenosis (AS), and congenital valve diseases are prevalent in sub-Saharan Africa. Many knowledge gaps remain in understanding disease mechanisms, stratifying phenotypes, and prognostication. Therefore, we aimed to characterise patients through clinical profiling, imaging, histology, and molecular biomarkers to improve our understanding of the pathophysiology, diagnosis, and prognosis of RHD and AS. METHODS In this cross-sectional, case-controlled study, we plan to recruit RHD and AS patients and compare them to matched controls. Living participants will undergo clinical assessment, echocardiography, CMR and blood sampling for circulatory biomarker analyses. Tissue samples will be obtained from patients undergoing valve replacement, while healthy tissues will be obtained from cadavers. Immunohistology, proteomics, metabolomics, and transcriptome analyses will be used to analyse circulatory- and tissue-specific biomarkers. Univariate and multivariate statistical analyses will be used for hypothesis testing and identification of important biomarkers. In summary, this study aims to delineate the pathophysiology of RHD and degenerative AS using multiparametric CMR imaging. In addition to discover novel biomarkers and explore the pathomechanisms associated with RHD and AS through high-throughput profiling of the tissue and blood proteome and metabolome and provide a proof of concept of the suitability of using cadaveric tissues as controls for cardiovascular disease studies.
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Affiliation(s)
- Daniel W. Mutithu
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
| | - Olukayode O. Aremu
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
| | - Dipolelo Mokaila
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
| | - Tasnim Bana
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
| | - Mary Familusi
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Department of Civil Engineering, University of Cape Town, Cape Town, South Africa
| | - Laura Taylor
- Division of Forensic Medicine and Toxicology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Lorna J. Martin
- Division of Forensic Medicine and Toxicology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Laura J. Heathfield
- Division of Forensic Medicine and Toxicology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Jennifer A. Kirwan
- Metabolomics Platform, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Center (MDC) for Molecular Medicine, Helmholtz Association, Berlin, Germany
| | - Lubbe Wiesner
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Henry A. Adeola
- Division of Dermatology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Evelyn N. Lumngwena
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
- School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Rodgers Manganyi
- Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Sebastian Skatulla
- Department of Civil Engineering, University of Cape Town, Cape Town, South Africa
| | - Richard Naidoo
- Division of Anatomical Pathology, Department of Pathology, University of Cape Town and National Health Laboratory Service, Cape Town, South Africa
| | - Ntobeko A. B. Ntusi
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Disease Research, University of Cape Town, Cape Town, South Africa
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Metabolomics: A New Tool in Our Understanding of Congenital Heart Disease. CHILDREN (BASEL, SWITZERLAND) 2022; 9:children9121803. [PMID: 36553246 PMCID: PMC9776621 DOI: 10.3390/children9121803] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022]
Abstract
Although the genetic origins underpinning congenital heart disease (CHD) have been extensively studied, genes, by themselves, do not entirely predict phenotypes, which result from the complex interplay between genes and the environment. Consequently, genes merely suggest the potential occurrence of a specific phenotype, but they cannot predict what will happen in reality. This task can be revealed by metabolomics, the most promising of the "omics sciences". Though metabolomics applied to CHD is still in its infant phase, it has already been applied to CHD prenatal diagnosis, as well as to predict outcomes after cardiac surgery. Particular metabolomic fingerprints have been identified for some of the specific CHD subtypes. The hallmarks of CHD-related pulmonary arterial hypertension have also been discovered. This review, which is presented in a narrative format, due to the heterogeneity of the selected papers, aims to provide the readers with a synopsis of the literature on metabolomics in the CHD setting.
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Migdadi L, Telfah A, Hergenröder R, Wöhler C. Novelty Detection for Metabolic Dynamics Established On Breast Cancer Tissue Using 2D NMR TOCSY Spectra. Comput Struct Biotechnol J 2022; 20:2965-2977. [PMID: 35782733 PMCID: PMC9213235 DOI: 10.1016/j.csbj.2022.05.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 11/30/2022] Open
Abstract
Automatic novelty detection of metabolites of 2D-TOCSY NMR spectra. Metabolic profiling of the dynamics changes in Breast cancer tissue sample. Accurate and fast automatic multicomponent peak assignment of 2D NMR spectrum. One- and multi- novelty detection of metabolites.
Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.
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Affiliation(s)
- Lubaba Migdadi
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V, 44139 Dortmund, Germany
- Image Analysis Group, TU Dortmund, 44227 Dortmund, Germany
- Corresponding author.
| | - Ahmad Telfah
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V, 44139 Dortmund, Germany
| | - Roland Hergenröder
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V, 44139 Dortmund, Germany
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