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Topriceanu CC, Moon JC, Raja AA, Captur G, Ho CY. Phenotypic Spectrum of Subclinical Sarcomere-Related Hypertrophic Cardiomyopathy and Transition to Overt Disease. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004580. [PMID: 38910555 PMCID: PMC11335455 DOI: 10.1161/circgen.124.004580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Genetic hypertrophic cardiomyopathy (HCM) is classically caused by pathogenic/likely pathogenic variants in sarcomere genes (G+). Currently, HCM is diagnosed if there is unexplained left ventricular (LV) hypertrophy with LV wall thickness ≥15 mm in probands or ≥13 mm in at-risk relatives. Although LV hypertrophy is a key feature, this binary metric does not encompass the full constellation of phenotypic features, particularly in the subclinical stage of the disease. Subtle phenotypic manifestations can be identified in sarcomere variant carriers with normal LV wall thickness, before diagnosis with HCM (G+/LV hypertrophy-; subclinical HCM). We conducted a systematic review to summarize current knowledge about the phenotypic spectrum of subclinical HCM and factors influencing penetrance and expressivity. Although the mechanisms driving the development of LV hypertrophy are yet to be elucidated, activation of profibrotic pathways, impaired relaxation, abnormal Ca2+ signaling, altered myocardial energetics, and microvascular dysfunction have all been identified in subclinical HCM. Progression from subclinical to clinically overt HCM may be more likely if early phenotypic manifestations are present, including ECG abnormalities, longer mitral valve leaflets, lower global E' velocities on Doppler echocardiography, and higher serum N-terminal propeptide of B-type natriuretic peptide. Longitudinal studies of variant carriers are critically needed to improve our understanding of penetrance, characterize the transition to disease, identify risk predictors of phenotypic evolution, and guide the development of novel treatment strategies aimed at influencing disease trajectory.
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Affiliation(s)
- Constantin-Cristian Topriceanu
- Dept of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- UCL Institute of Cardiovascular Science
- Cardiac MRI Unit, Barts Heart Centre, London, United Kingdom
| | - James C. Moon
- UCL Institute of Cardiovascular Science
- Cardiac MRI Unit, Barts Heart Centre, London, United Kingdom
| | - Anna Axelsson Raja
- Dept of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Gabriella Captur
- UCL Institute of Cardiovascular Science
- UCL MRC Unit for Lifelong Health and Ageing University College London
- The Royal Free Hospital, Centre for Inherited Heart Muscle Conditions, Cardiology Dept, London, United Kingdom
| | - Carolyn Y. Ho
- Dept of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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Captur G, Doykov I, Chung SC, Field E, Barnes A, Zhang E, Heenan I, Norrish G, Moon JC, Elliott PM, Heywood WE, Mills K, Kaski JP. Novel Multiplexed Plasma Biomarker Panel Has Diagnostic and Prognostic Potential in Children With Hypertrophic Cardiomyopathy. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004448. [PMID: 38847081 PMCID: PMC11188636 DOI: 10.1161/circgen.123.004448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 04/16/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Hypertrophic cardiomyopathy (HCM) is defined clinically by pathological left ventricular hypertrophy. We have previously developed a plasma proteomics biomarker panel that correlates with clinical markers of disease severity and sudden cardiac death risk in adult patients with HCM. The aim of this study was to investigate the utility of adult biomarkers and perform new discoveries in proteomics for childhood-onset HCM. METHODS Fifty-nine protein biomarkers were identified from an exploratory plasma proteomics screen in children with HCM and augmented into our existing multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay. The association of these biomarkers with clinical phenotypes and outcomes was prospectively tested in plasma collected from 148 children with HCM and 50 healthy controls. Machine learning techniques were used to develop novel pediatric plasma proteomic biomarker panels. RESULTS Four previously identified adult HCM markers (aldolase fructose-bisphosphate A, complement C3a, talin-1, and thrombospondin 1) and 3 new markers (glycogen phosphorylase B, lipoprotein a and profilin 1) were elevated in pediatric HCM. Using supervised machine learning applied to training (n=137) and validation cohorts (n=61), this 7-biomarker panel differentiated HCM from healthy controls with an area under the curve of 1.0 in the training data set (sensitivity 100% [95% CI, 95-100]; specificity 100% [95% CI, 96-100]) and 0.82 in the validation data set (sensitivity 75% [95% CI, 59-86]; specificity 88% [95% CI, 75-94]). Reduced circulating levels of 4 other peptides (apolipoprotein L1, complement 5b, immunoglobulin heavy constant epsilon, and serum amyloid A4) found in children with high sudden cardiac death risk provided complete separation from the low and intermediate risk groups and predicted mortality and adverse arrhythmic outcomes (hazard ratio, 2.04 [95% CI, 1.0-4.2]; P=0.044). CONCLUSIONS In children, a 7-biomarker proteomics panel can distinguish HCM from controls with high sensitivity and specificity, and another 4-biomarker panel identifies those at high risk of adverse arrhythmic outcomes, including sudden cardiac death.
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Affiliation(s)
- Gabriella Captur
- UCL MRC Unit for Lifelong Health & Ageing, UCL, London, United Kingdom (G.C.)
- UCL Institute of Cardiovascular Science, UCL, London, United Kingdom (G.C., J.C.M., P.M.E.)
- The Royal Free Hospital, Centre for Inherited Heart Muscle Conditions, Cardiology Department, UCL, London, United Kingdom (G.C.)
| | - Ivan Doykov
- Translational Mass Spectrometry Research Group, UCL Institute of Child Health, London, United Kingdom (I.D., E.Z., W.E.H., K.M.)
| | - Sheng-Chia Chung
- UCL Institute of Health Informatics Research, Division of Infection and Immunity, London, United Kingdom (S.-C.C.)
| | - Ella Field
- Centre for Paediatric Inherited & Rare Cardiovascular Disease, Institute of Cardiovascular Science, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
- Centre for Inherited Cardiovascular Diseases, Great Ormond Street Hospital, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
| | - Annabelle Barnes
- Centre for Paediatric Inherited & Rare Cardiovascular Disease, Institute of Cardiovascular Science, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
- Centre for Inherited Cardiovascular Diseases, Great Ormond Street Hospital, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
| | - Enpei Zhang
- Translational Mass Spectrometry Research Group, UCL Institute of Child Health, London, United Kingdom (I.D., E.Z., W.E.H., K.M.)
- UCL Medical School, University College London, London, United Kingdom (E.Z.)
| | - Imogen Heenan
- Centre for Paediatric Inherited & Rare Cardiovascular Disease, Institute of Cardiovascular Science, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
- Centre for Inherited Cardiovascular Diseases, Great Ormond Street Hospital, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
| | - Gabrielle Norrish
- Centre for Paediatric Inherited & Rare Cardiovascular Disease, Institute of Cardiovascular Science, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
- Centre for Inherited Cardiovascular Diseases, Great Ormond Street Hospital, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
| | - James C. Moon
- Barts Heart Centre, the Cardiovascular Magnetic Resonance Unit, London, United Kingdom (J.C.M.)
| | - Perry M. Elliott
- Barts Heart Centre, the Inherited Cardiovascular Diseases Unit, St Bartholomew’s Hospital, London, United Kingdom (P.M.E.)
| | - Wendy E. Heywood
- Translational Mass Spectrometry Research Group, UCL Institute of Child Health, London, United Kingdom (I.D., E.Z., W.E.H., K.M.)
| | - Kevin Mills
- Translational Mass Spectrometry Research Group, UCL Institute of Child Health, London, United Kingdom (I.D., E.Z., W.E.H., K.M.)
| | - Juan Pablo Kaski
- Centre for Paediatric Inherited & Rare Cardiovascular Disease, Institute of Cardiovascular Science, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
- Centre for Inherited Cardiovascular Diseases, Great Ormond Street Hospital, London, United Kingdom (E.F., A.B., I.H., G.N., J.P.K.)
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WANG Z, LI Y, WANG D, MA B, MIAO L, REN J, LIU J, LIU J. Proteomics analysis of coronary atherosclerotic heart disease with different Traditional Chinese Medicine syndrome types before and after percutaneous coronary intervention. J TRADIT CHIN MED 2024; 44:554-563. [PMID: 38767640 PMCID: PMC11077157 DOI: 10.19852/j.cnki.jtcm.20240408.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/25/2023] [Indexed: 05/22/2024]
Abstract
OBJECTIVE To investigate the underlying protein molecular mechanisms of "Qi stagnation and blood stasis syndrome" (QS) and "Qi deficiency and blood stasis syndrome" (QD), as two subtypes of coronary artery disease (CAD) in Traditional Chinese Medicine (TCM), following percutaneous coronary intervention (PCI). METHODS In this study, a total of 227 CAD patients with QS and 211 CAD patients with QD were enrolled; all participants underwent PCI. Label-free quantification proteomics were employed to analyze the changes in serum in two subtypes of CAD patients before and 6 months after PCI, aiming to elucidate the intervention mechanism of PCI in treating CAD characterized by two different TCM syndromes. RESULTS Biochemical analysis revealed significant changes in tumor necrosis factor-α, high density lipoprotein cholesterol, blood stasis clinical symptoms observation, and Gensini levels in both patient groups post-PCI; Proteomic analysis identified 79 and 95 differentially expressed proteins in the QS and QD patient groups, respectively, compared to their control groups. complement C8 alpha chain, complement factor H, apolipoprotein H, apolipoprotein B, plasminogen, carbonic anhydrase 2, and complement factor I were altered in both comparison groups. Furthermore, enrichment analysis demonstrated that cell adhesion and connectivity-related processes underwent changes in QS patients post-PCI, whereas lipid metabolism-related pathways, including the peroxisome proliferator-activated receptor signaling pathway and extracellular matrix receptor interaction, underwent changes in the QD group. The protein-protein interaction network analysis further enriched 52 node proteins, including apolipoprotein B, lipoprotein (a), complement C5, apolipoprotein A4, complement C8 alpha chain, complement C8 beta chain, complement C8 gamma chain, apolipoprotein H, apolipoprotein A-Ⅱ, albumin, complement C4-B, apolipoprotein C3, among others. The functional network of these proteins is posited to contribute to the pathophysiology of CAD characterized by TCM syndromes. CONCLUSION The current quantitative proteomic study has preliminarily identified biomarkers of CAD in different TCM subtypes treated with PCI, potentially laying the groundwork for understanding the protein profiles associated with the treatment of various TCM subtypes of CAD.
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Affiliation(s)
- Zhibo WANG
- 1 Beijing Key Laboratory of Pharmacology of Chinese Materia Region, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center of Cardiovascular Disease of Traditional Chinese Medicine, Beijing 100000, China
| | - Ying LI
- 1 Beijing Key Laboratory of Pharmacology of Chinese Materia Region, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center of Cardiovascular Disease of Traditional Chinese Medicine, Beijing 100000, China
| | - Daoping WANG
- 2 the National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Science, Beijing 100098, China
| | - Bo MA
- 1 Beijing Key Laboratory of Pharmacology of Chinese Materia Region, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center of Cardiovascular Disease of Traditional Chinese Medicine, Beijing 100000, China
| | - Lan MIAO
- 1 Beijing Key Laboratory of Pharmacology of Chinese Materia Region, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center of Cardiovascular Disease of Traditional Chinese Medicine, Beijing 100000, China
| | - Junguo REN
- 1 Beijing Key Laboratory of Pharmacology of Chinese Materia Region, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center of Cardiovascular Disease of Traditional Chinese Medicine, Beijing 100000, China
| | - Jinghua LIU
- 3 Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - Jianxun LIU
- 1 Beijing Key Laboratory of Pharmacology of Chinese Materia Region, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center of Cardiovascular Disease of Traditional Chinese Medicine, Beijing 100000, China
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Österberg AW, Östman-Smith I, Green H, Gunnarsson C, Fredrikson M, Liuba P, Fernlund E. Biomarkers and Proteomics in Sarcomeric Hypertrophic Cardiomyopathy in the Young-FGF-21 Highly Associated with Overt Disease. J Cardiovasc Dev Dis 2024; 11:105. [PMID: 38667723 PMCID: PMC11050055 DOI: 10.3390/jcdd11040105] [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: 02/27/2024] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
Background: Any difference in biomarkers between genotype-positive individuals with overt hypertrophic cardiomyopathy (HCM), and genotype-positive but phenotype-negative individuals (G+P-) in HCM-associated pathways might shed light on pathophysiological mechanisms. We studied this in young HCM patients. Methods: 29 HCM patients, 17 G+P--individuals, and age- and sex-matched controls were prospectively included. We analyzed 184 cardiovascular disease-associated proteins by two proximity extension assays, categorized into biological pathways, and analyzed with multivariate logistic regression analysis. Significant proteins were dichotomized into groups above/below median concentration in control group. Results: Dichotomized values of significant proteins showed high odds ratio (OR) in overt HCMphenotype for Fibroblast growth factor-21 (FGF-21) 10 (p = 0.001), P-selectin glycoprotein ligand-1 (PSGL-1) OR 8.6 (p = 0.005), and Galectin-9 (Gal-9) OR 5.91 (p = 0.004). For G+P-, however, angiopoietin-1 receptor (TIE2) was notably raised, OR 65.5 (p = 0.004), whereas metalloproteinase inhibitor 4 (TIMP4) involved in proteolysis, in contrast, had reduced OR 0.06 (p = 0.013). Conclusions: This study is one of the first in young HCM patients and G+P- individuals. We found significantly increased OR for HCM in FGF-21 involved in RAS-MAPK pathway, associated with cardiomyocyte hypertrophy. Upregulation of FGF-21 indicates involvement of the RAS-MAPK pathway in HCM regardless of genetic background, which is a novel finding.
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Affiliation(s)
- Anna Wålinder Österberg
- Crown Princess Victoria Children’s Hospital, Linköping University Hospital and Division of Pediatrics, Department of Biomedical and Clinical Sciences, Linköping University, SE-58183 Linköping, Sweden;
| | - Ingegerd Östman-Smith
- Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, SE-41680 Göteborg, Sweden;
| | - Henrik Green
- Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, SE-58185 Linköping, Sweden
| | - Cecilia Gunnarsson
- Department of Clinical Genetics, Department of Biomedical and Clinical Sciences, Centre for Rare Diseases in Southeast Region of Sweden, Linköping University, SE-58183 Linköping, Sweden;
| | - Mats Fredrikson
- Department of Clinical and Experimental Medicine, Linköping University, SE-58183 Linköping, Sweden
| | - Petru Liuba
- Paediatric Heart Centre, Skåne University Hospital and Department of Clinical Sciences, Lund University, SE-22185 Lund, Sweden;
| | - Eva Fernlund
- Crown Princess Victoria Children’s Hospital, Linköping University Hospital and Division of Pediatrics, Department of Biomedical and Clinical Sciences, Linköping University, SE-58183 Linköping, Sweden;
- Paediatric Heart Centre, Skåne University Hospital and Department of Clinical Sciences, Lund University, SE-22185 Lund, Sweden;
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Wang Y, Huang H, Weng H, Jia C, Liao B, Long Y, Yu F, Nie Y. Talin mechanotransduction in disease. Int J Biochem Cell Biol 2024; 166:106490. [PMID: 37914021 DOI: 10.1016/j.biocel.2023.106490] [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: 07/29/2023] [Revised: 10/26/2023] [Accepted: 10/26/2023] [Indexed: 11/03/2023]
Abstract
Talin protein (Talin 1/2) is a mechanosensitive cytoskeleton protein. The unique structure of the Talin plays a vital role in transmitting mechanical forces. Talin proteins connect the extracellular matrix to the cytoskeleton by linking to integrins and actin, thereby mediating the conversion of mechanical signals into biochemical signals and influencing disease progression as potential diagnostic indicators, therapeutic targets, and prognostic indicators of various diseases. Most studies in recent years have confirmed that mechanical forces also have a crucial role in the development of disease, and Talin has been found to play a role in several diseases. Still, more studies need to be done on how Talin is involved in mechanical signaling in disease. This review focuses on the mechanical signaling of Talin in disease, aiming to summarize the mechanisms by which Talin plays a role in disease and to provide references for further studies.
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Affiliation(s)
- Yingzi Wang
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, China
| | - Haozhong Huang
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, China
| | - Huimin Weng
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, China
| | - Chunsen Jia
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, China
| | - Bin Liao
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, China; Metabolic Vascular Disease Key Laboratory of Sichuan Province, China; Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, China; Key Laboratory of Cardiovascular Remodeling and Dysfunction, Luzhou, China
| | - Yang Long
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China; Sichuan Clinical Research Center for Nephropathy, Luzhou, China
| | - Fengxu Yu
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, China; Metabolic Vascular Disease Key Laboratory of Sichuan Province, China; Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, China; Key Laboratory of Cardiovascular Remodeling and Dysfunction, Luzhou, China
| | - Yongmei Nie
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, China; Metabolic Vascular Disease Key Laboratory of Sichuan Province, China; Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, China; Key Laboratory of Cardiovascular Remodeling and Dysfunction, Luzhou, China.
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Lumish HS, Liang LW, Hasegawa K, Maurer MS, Fifer MA, Reilly MP, Shimada YJ. Prediction of worsening heart failure in hypertrophic cardiomyopathy using plasma proteomics. Heart 2023; 109:1837-1843. [PMID: 37451849 PMCID: PMC10843738 DOI: 10.1136/heartjnl-2023-322644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
OBJECTIVE Heart failure (HF) is one of the most common and lifestyle-limiting complications of hypertrophic cardiomyopathy (HCM). Prediction of worsening HF using clinical measures alone remains limited. Moreover, the mechanisms by which patients with HCM develop worsening HF have not been elucidated. Therefore, the aim of this study was to develop a plasma proteomics-based model to predict worsening HF among patients with HCM and to identify signalling pathways that are differentially regulated in those who subsequently develop worsening HF. METHODS In this multi-centre, prospective cohort study of 389 patients with HCM, plasma proteomics profiling of 4986 proteins was performed at enrolment. A proteomics-based random forest model was developed to predict worsening HF using data from one institution (training set, n=268). This model was externally validated in patients from a different institution (test set, n=121). Pathway analysis of proteins significantly dysregulated in patients who subsequently developed worsening HF compared with those who did not was executed, using a false discovery rate (FDR) threshold of <0.001. RESULTS Using the 11-protein proteomics-based model derived from the training set, the area under the receiver-operating characteristic curve to predict worsening HF was 0.87 (95% CI: 0.76 to 0.98) in the test set. Pathway analysis revealed that the Ras-MAPK pathway (FDR<0.00001) and related pathways were dysregulated in patients who subsequently developed worsening HF. CONCLUSIONS The present study with comprehensive plasma proteomics profiling demonstrated a high accuracy to predict worsening HF in patients with HCM and identified the Ras-MAPK and related signalling pathways as potential underlying mechanisms.
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Affiliation(s)
- Heidi S Lumish
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Lusha W Liang
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mathew S Maurer
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael A Fifer
- Division of Cardiology, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York, USA
| | - Yuichi J Shimada
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
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Chen S, Zou Y, Song C, Cao K, Cai K, Wu Y, Zhang Z, Geng D, Sun W, Ouyang N, Zhang N, Li Z, Sun G, Zhang Y, Sun Y, Zhang Y. The role of glycolytic metabolic pathways in cardiovascular disease and potential therapeutic approaches. Basic Res Cardiol 2023; 118:48. [PMID: 37938421 PMCID: PMC10632287 DOI: 10.1007/s00395-023-01018-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 10/20/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Cardiovascular disease (CVD) is a major threat to human health, accounting for 46% of non-communicable disease deaths. Glycolysis is a conserved and rigorous biological process that breaks down glucose into pyruvate, and its primary function is to provide the body with the energy and intermediate products needed for life activities. The non-glycolytic actions of enzymes associated with the glycolytic pathway have long been found to be associated with the development of CVD, typically exemplified by metabolic remodeling in heart failure, which is a condition in which the heart exhibits a rapid adaptive response to hypoxic and hypoxic conditions, occurring early in the course of heart failure. It is mainly characterized by a decrease in oxidative phosphorylation and a rise in the glycolytic pathway, and the rise in glycolysis is considered a hallmark of metabolic remodeling. In addition to this, the glycolytic metabolic pathway is the main source of energy for cardiomyocytes during ischemia-reperfusion. Not only that, the auxiliary pathways of glycolysis, such as the polyol pathway, hexosamine pathway, and pentose phosphate pathway, are also closely related to CVD. Therefore, targeting glycolysis is very attractive for therapeutic intervention in CVD. However, the relationship between glycolytic pathway and CVD is very complex, and some preclinical studies have confirmed that targeting glycolysis does have a certain degree of efficacy, but its specific role in the development of CVD has yet to be explored. This article aims to summarize the current knowledge regarding the glycolytic pathway and its key enzymes (including hexokinase (HK), phosphoglucose isomerase (PGI), phosphofructokinase-1 (PFK1), aldolase (Aldolase), phosphoglycerate metatase (PGAM), enolase (ENO) pyruvate kinase (PKM) lactate dehydrogenase (LDH)) for their role in cardiovascular diseases (e.g., heart failure, myocardial infarction, atherosclerosis) and possible emerging therapeutic targets.
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Affiliation(s)
- Shuxian Chen
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China
| | - Yuanming Zou
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China
| | - Chunyu Song
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China
| | - Kexin Cao
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China
| | - Kexin Cai
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China
| | - Yanjiao Wu
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China
| | - Zhaobo Zhang
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China
| | - Danxi Geng
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China
| | - Wei Sun
- Department of Thyroid Surgery, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China.
| | - Nanxiang Ouyang
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China.
| | - Naijin Zhang
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China.
- Institute of Health Sciences, China Medical University, 77 Puhe Road, Shenbei New District, Shenyang, 110122, Liaoning Province, People's Republic of China.
- Key Laboratory of Reproductive and Genetic Medicine, China Medical University, National Health Commission, 77 Puhe Road, Shenbei New District, Shenyang, 110122, Liaoning Province, People's Republic of China.
| | - Zhao Li
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China.
| | - Guozhe Sun
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China.
| | - Yixiao Zhang
- Department of Urology Surgery, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning Province, People's Republic of China.
| | - Yingxian Sun
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China.
- Institute of Health Sciences, China Medical University, 77 Puhe Road, Shenbei New District, Shenyang, 110122, Liaoning Province, People's Republic of China.
| | - Ying Zhang
- Department of Cardiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China.
- Institute of Health Sciences, China Medical University, 77 Puhe Road, Shenbei New District, Shenyang, 110122, Liaoning Province, People's Republic of China.
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Hällqvist J, Pinto RC, Heywood WE, Cordey J, Foulkes AJM, Slattery CF, Leckey CA, Murphy EC, Zetterberg H, Schott JM, Mills K, Paterson RW. A Multiplexed Urinary Biomarker Panel Has Potential for Alzheimer's Disease Diagnosis Using Targeted Proteomics and Machine Learning. Int J Mol Sci 2023; 24:13758. [PMID: 37762058 PMCID: PMC10531486 DOI: 10.3390/ijms241813758] [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: 08/10/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
As disease-modifying therapies are now available for Alzheimer's disease (AD), accessible, accurate and affordable biomarkers to support diagnosis are urgently needed. We sought to develop a mass spectrometry-based urine test as a high-throughput screening tool for diagnosing AD. We collected urine from a discovery cohort (n = 11) of well-characterised individuals with AD (n = 6) and their asymptomatic, CSF biomarker-negative study partners (n = 5) and used untargeted proteomics for biomarker discovery. Protein biomarkers identified were taken forward to develop a high-throughput, multiplexed and targeted proteomic assay which was tested on an independent cohort (n = 21). The panel of proteins identified are known to be involved in AD pathogenesis. In comparing AD and controls, a panel of proteins including MIEN1, TNFB, VCAM1, REG1B and ABCA7 had a classification accuracy of 86%. These proteins have been previously implicated in AD pathogenesis. This suggests that urine-targeted mass spectrometry has potential utility as a diagnostic screening tool in AD.
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Affiliation(s)
- Jenny Hällqvist
- Translational Mass Spectrometry Research Group, Genetics and Genomic Medicine, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK; (J.H.); (K.M.)
| | - Rui C. Pinto
- Faculty of Medicine, School of Public Health, Imperial College London, London SW7 2BX, UK
| | - Wendy E. Heywood
- Translational Mass Spectrometry Research Group, Genetics and Genomic Medicine, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK; (J.H.); (K.M.)
| | - Jonjo Cordey
- Translational Mass Spectrometry Research Group, Genetics and Genomic Medicine, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK; (J.H.); (K.M.)
| | | | | | - Claire A. Leckey
- Translational Mass Spectrometry Research Group, Genetics and Genomic Medicine, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK; (J.H.); (K.M.)
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Eimear C. Murphy
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, S-431 80 Mölndal, Sweden
- UK Dementia Research Institute, UCL, London WC1E 6BT, UK
| | - Jonathan M. Schott
- National Hospital for Neurology and Neurosurgery, Queen Square London, London WC1N 3BG, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Kevin Mills
- Translational Mass Spectrometry Research Group, Genetics and Genomic Medicine, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK; (J.H.); (K.M.)
| | - Ross W. Paterson
- National Hospital for Neurology and Neurosurgery, Queen Square London, London WC1N 3BG, UK
- Darent Valley Hospital, Dartford DA2 8DA, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, S-431 80 Mölndal, Sweden
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9
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Affiliation(s)
- George Joy
- Institute of Cardiovascular Science, University College London, London, UK.
- Barts Heart Centre, St Bartholomew's Hospital, London, UK.
| | - James C Moon
- Institute of Cardiovascular Science, University College London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, London, UK
| | - Luis R Lopes
- Institute of Cardiovascular Science, University College London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, London, UK
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10
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Shimada YJ, Raita Y, Liang LW, Maurer MS, Hasegawa K, Fifer MA, Reilly MP. Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003546. [PMID: 36252118 PMCID: PMC9771902 DOI: 10.1161/circgen.121.003546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 06/24/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE. METHODS In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001. RESULTS The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated. CONCLUSIONS This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.
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Affiliation(s)
- Yuichi J. Shimada
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Yoshihiko Raita
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Lusha W. Liang
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | - Mathew S. Maurer
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Michael A. Fifer
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Muredach P. Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, NY
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11
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Liang LW, Raita Y, Hasegawa K, Fifer MA, Maurer MS, Reilly MP, Shimada YJ. Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy. Heart 2022; 108:1807-1814. [PMID: 35351822 PMCID: PMC9741498 DOI: 10.1136/heartjnl-2021-320729] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/08/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease, likely encompassing several subtypes of disease with distinct biological mechanisms (ie, molecular subtypes). Current models based solely on clinical data have yielded limited accuracy in predicting the risk of major adverse cardiovascular events (MACE). Our aim in this study was to derive molecular subtypes in our multicentre prospective cohort of patients with HCM using proteomics profiling and to examine their longitudinal associations with MACE. METHODS We applied unsupervised machine learning methods to plasma proteomics profiling data of 1681 proteins from 258 patients with HCM who were prospectively followed for a median of 2.8 years. The primary outcome was MACE, defined as a composite of arrhythmia, heart failure, stroke and sudden cardiac death. RESULTS We identified four molecular subtypes of HCM. Time-to-event analysis revealed significant differences in MACE-free survival among the four molecular subtypes (plogrank=0.007). Compared with the reference group with the lowest risk of MACE (molecular subtype A), patients in molecular subtype D had a higher risk of subsequently developing MACE, with an HR of 3.41 (95% CI 1.54 to 7.55, p=0.003). Pathway analysis of proteins differentially regulated in molecular subtype D demonstrated an upregulation of the Ras/mitogen-activated protein kinase and associated pathways, as well as pathways related to inflammation and fibrosis (eg, transforming growth factor-β pathway). CONCLUSIONS Our prospective plasma proteomics study not only exhibited the presence of HCM molecular subtypes but also identified pathobiological mechanisms associated with a distinct high-risk subtype of HCM.
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Affiliation(s)
- Lusha W Liang
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York City, New York, USA
| | - Yoshihiko Raita
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael A Fifer
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mathew S Maurer
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York City, New York, USA
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York City, New York, USA
- Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York City, New York, USA
| | - Yuichi J Shimada
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York City, New York, USA
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12
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Desaire H, Go EP, Hua D. Advances, obstacles, and opportunities for machine learning in proteomics. CELL REPORTS. PHYSICAL SCIENCE 2022; 3:101069. [PMID: 36381226 PMCID: PMC9648337 DOI: 10.1016/j.xcrp.2022.101069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.
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Affiliation(s)
- Heather Desaire
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
| | - Eden P. Go
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
| | - David Hua
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
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13
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Jansen M, Algül S, Bosman LP, Michels M, van der Velden J, de Boer RA, van Tintelen JP, Asselbergs FW, Baas AF. Blood-based biomarkers for the prediction of hypertrophic cardiomyopathy prognosis: a systematic review and meta-analysis. ESC Heart Fail 2022; 9:3418-3434. [PMID: 35842920 PMCID: PMC9715795 DOI: 10.1002/ehf2.14073] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 06/07/2022] [Accepted: 06/27/2022] [Indexed: 05/22/2024] Open
Abstract
AIMS Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease. HCM is an important cause of sudden cardiac death and may also lead to outflow tract obstruction and heart failure. Disease severity is highly variable and risk stratification remains limited. Therefore, we aimed to review current knowledge of prognostic blood-based biomarkers in HCM. METHODS AND RESULTS A systematic literature search was performed on PubMed, Embase, and the Cochrane library to identify studies assessing plasma or serum biomarkers for outcomes involving malignant ventricular arrhythmia, outflow tract obstruction, and heart failure. Risk of bias was assessed using the QUIPS tool. Meta-analyses were performed using the random effects method. A total of 26 unique cohort studies assessing 42 biomarkers were identified. Overall risk of bias was moderate. Thirty-two biomarkers were significantly associated to an HCM outcome in at least one study (nine biomarkers in at least two studies). In pooled analyses, cardiovascular mortality was predicted by N-terminal prohormone of brain natriuretic peptide (hazard ratio [HR] 5.38 per log[pg/mL], 95% confidence interval [CI] 2.07-14.03, P < 0.001, I2 = 0%) and high-sensitivity C-reactive protein (HR 1.30 per μg/mL, 95% CI 1.00-1.68, P = 0.05, I2 = 78%), all-cause mortality by low-density lipoprotein cholesterol (HR 0.63 per μmol/mL, 95% CI 0.49-0.80, P < 0.001, I2 = 0%), and a combined congestive heart failure, malignant ventricular arrhythmia, and stroke outcome by high-sensitivity cardiac troponin T (pooled HR 4.19 for ≥0.014 ng/mL, 95% CI 2.22-7.88, P < 0.001, I2 = 0%). Quality of evidence was low-moderate. CONCLUSIONS Several blood-based biomarkers were identified as predictors of HCM outcomes. Additional studies are required to validate their prognostic utility within current risk stratification models.
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Affiliation(s)
- Mark Jansen
- Department of Genetics, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
- Netherlands Heart InstituteUtrechtThe Netherlands
| | - Sila Algül
- Department of Physiology, Amsterdam Cardiovascular SciencesAmsterdam University Medical Center, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Laurens P. Bosman
- Netherlands Heart InstituteUtrechtThe Netherlands
- Department of Cardiology, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Michelle Michels
- Department of CardiologyThoraxcenter, Erasmus University Medical Center, Erasmus UniversityRotterdamThe Netherlands
| | - Jolanda van der Velden
- Department of Physiology, Amsterdam Cardiovascular SciencesAmsterdam University Medical Center, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Rudolf A. de Boer
- Department of CardiologyUniversity Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - J. Peter van Tintelen
- Department of Genetics, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
- Netherlands Heart InstituteUtrechtThe Netherlands
| | - Folkert W. Asselbergs
- Netherlands Heart InstituteUtrechtThe Netherlands
- Department of Cardiology, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health SciencesUniversity College LondonLondonUK
- Health Data Research UK and Institute of Health InformaticsUniversity College LondonLondonUK
| | - Annette F. Baas
- Department of Genetics, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
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Bracht T, Kleefisch D, Schork K, Witzke KE, Chen W, Bayer M, Hovanec J, Johnen G, Meier S, Ko YD, Behrens T, Brüning T, Fassunke J, Buettner R, Uszkoreit J, Adamzik M, Eisenacher M, Sitek B. Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD). Int J Mol Sci 2022; 23:ijms231911242. [PMID: 36232544 PMCID: PMC9569607 DOI: 10.3390/ijms231911242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/18/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.
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Affiliation(s)
- Thilo Bracht
- Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Correspondence: (T.B.); (B.S.); Tel.: +49-234-32-29985 (T.B.); +49-234-32-24362 (B.S.)
| | - Daniel Kleefisch
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Karin Schork
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Kathrin E. Witzke
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Weiqiang Chen
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Malte Bayer
- Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Jan Hovanec
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Georg Johnen
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Swetlana Meier
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Yon-Dschun Ko
- Department of Internal Medicine, Johanniter-Kliniken Bonn GmbH, Johanniter Krankenhaus, 53113 Bonn, Germany
| | - Thomas Behrens
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Jana Fassunke
- Institute of Pathology, Medical Faculty and Center for Molecular Medicine (CMMC), University of Cologne, 50924 Cologne, Germany
| | - Reinhard Buettner
- Institute of Pathology, Medical Faculty and Center for Molecular Medicine (CMMC), University of Cologne, 50924 Cologne, Germany
| | - Julian Uszkoreit
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Michael Adamzik
- Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
| | - Martin Eisenacher
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Barbara Sitek
- Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Correspondence: (T.B.); (B.S.); Tel.: +49-234-32-29985 (T.B.); +49-234-32-24362 (B.S.)
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15
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Lander BS, Zhao Y, Hasegawa K, Maurer MS, Tower-Rader A, Fifer MA, Reilly MP, Shimada YJ. Comprehensive Proteomics Profiling Identifies Patients With Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in the Hypertrophic Cardiomyopathy Population. Front Cardiovasc Med 2022; 9:839409. [PMID: 35783832 PMCID: PMC9247183 DOI: 10.3389/fcvm.2022.839409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 05/09/2022] [Indexed: 12/04/2022] Open
Abstract
Introduction In hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) represents myocardial fibrosis and is associated with sudden cardiac death. However, CMR requires particular expertise and is expensive and time-consuming. Therefore, it is important to specify patients with a high pre-test probability of having LGE as the utility of CMR is higher in such cases. The objective was to determine whether plasma proteomics profiling can distinguish patients with and without LGE on CMR in the HCM population. Materials and Methods We performed a multicenter case-control (LGE vs. no LGE) study of 147 patients with HCM. We performed plasma proteomics profiling of 4,979 proteins. Using the 17 most discriminant proteins, we performed logistic regression analysis with elastic net regularization to develop a discrimination model with data from one institution (the training set; n = 111) and tested the discriminative ability in independent samples from the other institution (the test set; n = 36). We calculated the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity. Results Overall, 82 of the 147 patients (56%) had LGE on CMR. The AUC of the 17-protein model was 0.83 (95% confidence interval [CI], 0.75–0.90) in the training set and 0.71 in the independent test set for validation (95% CI, 0.54–0.88). The sensitivity of the training model was 0.72 (95% CI, 0.61–0.83) and the specificity was 0.78 (95% CI, 0.66–0.90). The sensitivity was 0.71 (95% CI, 0.49–0.92) and the specificity was 0.74 (95% CI, 0.54–0.93) in the test set. Based on the discrimination model derived from the training set, patients in the test set who had high probability of having LGE had a significantly higher odds of having LGE compared to those who had low probability (odds ratio 29.6; 95% CI, 1.6–948.5; p = 0.03). Conclusions In this multi-center case-control study of patients with HCM, comprehensive proteomics profiling of 4,979 proteins demonstrated a high discriminative ability to distinguish patients with and without LGE. By identifying patients with a high pretest probability of having LGE, the present study serves as the first step to establishing a panel of circulating protein biomarkers to better inform clinical decisions regarding CMR utilization.
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Affiliation(s)
- Bradley S. Lander
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
| | - Yanling Zhao
- Department of Surgery, Columbia University Irving Medical Center, New York, NY, United States
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Mathew S. Maurer
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
| | - Albree Tower-Rader
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Michael A. Fifer
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Muredach P. Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
- Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, NY, United States
| | - Yuichi J. Shimada
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
- *Correspondence: Yuichi J. Shimada
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16
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Lou J, Chen H, Huang S, Chen P, Yu Y, Chen F. Update on risk factors and biomarkers of sudden unexplained cardiac death. J Forensic Leg Med 2022; 87:102332. [DOI: 10.1016/j.jflm.2022.102332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/21/2022] [Accepted: 03/02/2022] [Indexed: 02/01/2023]
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17
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Li J, Luu LDW, Wang X, Cui X, Huang X, Fu J, Zhu X, Li Z, Wang Y, Tai J. Metabolomic Analysis Reveals Potential Biomarkers and the Underlying Pathogenesis Involved in Mycoplasma Pneumoniae Pneumonia. Emerg Microbes Infect 2022; 11:593-605. [PMID: 35094669 PMCID: PMC8865114 DOI: 10.1080/22221751.2022.2036582] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Although previous studies have reported the use of metabolomics for infectious diseases, little is known about the potential function of plasma metabolites in children infected with Mycoplasma pneumoniae (MP). Here, a combination of liquid chromatography-quadrupole time-of-flight mass spectrometry and random forest-based classification model was used to provide a broader range of applications in MP diagnosis. In the training cohort, plasma from 63 MP pneumonia children (MPPs), 37 healthy controls (HC) and 29 infectious disease controls (IDC) was collected. After multivariate analyses, 357 metabolites were identified to be differentially expressed among MPP, HC and IDC groups, and 3 metabolites (568.5661, 459.3493 and 411.3208) had high diagnostic values. In an independent cohort with 57 blinded subjects, samples were successfully classified into different groups, demonstrating the reliability of these biomarkers for distinguishing MPPs from controls. A metabolomic signature analysis identified major classes of glycerophospholipids, sphingolipids and fatty acyls were increased in MPPs. These markedly altered metabolites are mainly involved in glycerophospholipid and sphingolipid metabolism. As the ubiquitous building blocks of eukaryotic cell membranes, dysregulated lipid metabolism indicates damage of the cellular membrane and the activation of immunity in MPPs. Moreover, lipid metabolites, differentially expressed between severe and mild MPPs, were correlated with the markers of extrapulmonary complications, suggesting that they may be involved in MPP disease severity. These findings may offer new insights into biomarker selection and the pathogenesis of MPP in children.
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Affiliation(s)
- Jieqiong Li
- Department of Respiratory Disease, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, P. R. China
| | - Laurence Don Wai Luu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
| | - Xiaoxia Wang
- Central & Clinical Laboratory of Sanya People’s Hospital, Sanya, Hainan 572000, P. R. China
| | - XiaoDai Cui
- Experimental Research Center, Capital Institute of Pediatrics, Beijing 100020, P. R. China
| | - Xiaolan Huang
- Experimental Research Center, Capital Institute of Pediatrics, Beijing 100020, P. R. China
| | - Jin Fu
- Experimental Research Center, Capital Institute of Pediatrics, Beijing 100020, P. R. China
| | - Xiong Zhu
- Central & Clinical Laboratory of Sanya People’s Hospital, Sanya, Hainan 572000, P. R. China
| | - Zhenjun Li
- State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102200, P.R. China
| | - Yi Wang
- Experimental Research Center, Capital Institute of Pediatrics, Beijing 100020, P. R. China
| | - Jun Tai
- Department of Otolaryngology, Head and Neck Surgery, Children's Hospital Capital Institute of Pediatrics, Beijing 100020, P. R. China
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18
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Sharifrazi D, Alizadehsani R, Joloudari JH, Band SS, Hussain S, Sani ZA, Hasanzadeh F, Shoeibi A, Dehzangi A, Sookhak M, Alinejad-Rokny H. CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2381-2402. [PMID: 35240789 DOI: 10.3934/mbe.2022110] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
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Affiliation(s)
- Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IR
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, AU
| | | | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, TW
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam 786004, IN
| | - Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Omid hospital, Iran University of Medical Sciences, Tehran, IR
| | | | - Afshin Shoeibi
- FPGA Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, IR
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Mehdi Sookhak
- Department of Computer Science, Texas A & M University at Corpus Christi, Corpus Christi, TX 78412, USA
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, AU
- Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, AU
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19
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Mulari S, Eskin A, Lampinen M, Nummi A, Nieminen T, Teittinen K, Ojala T, Kankainen M, Vento A, Laurikka J, Kupari M, Harjula A, Tuncbag N, Kankuri E. Ischemic Heart Disease Selectively Modifies the Right Atrial Appendage Transcriptome. Front Cardiovasc Med 2021; 8:728198. [PMID: 34926599 PMCID: PMC8674465 DOI: 10.3389/fcvm.2021.728198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 11/01/2021] [Indexed: 11/20/2022] Open
Abstract
Background: Although many pathological changes have been associated with ischemic heart disease (IHD), molecular-level alterations specific to the ischemic myocardium and their potential to reflect disease severity or therapeutic outcome remain unclear. Currently, diagnosis occurs relatively late and evaluating disease severity is largely based on clinical symptoms, various imaging modalities, or the determination of risk factors. This study aims to identify IHD-associated signature RNAs from the atrial myocardium and evaluate their ability to reflect disease severity or cardiac surgery outcomes. Methods and Results: We collected right atrial appendage (RAA) biopsies from 40 patients with invasive coronary angiography (ICA)-positive IHD undergoing coronary artery bypass surgery and from 8 patients ICA-negative for IHD (non-IHD) undergoing valvular surgery. Following RNA sequencing, RAA transcriptomes were analyzed against 429 donors from the GTEx project without cardiac disease. The IHD transcriptome was characterized by repressed RNA expression in pathways for cell-cell contacts and mitochondrial dysfunction. Increased expressions of the CSRNP3, FUT10, SHD, NAV2-AS4, and hsa-mir-181 genes resulted in significance with the complexity of coronary artery obstructions or correlated with a functional cardiac benefit from bypass surgery. Conclusions: Our results provide an atrial myocardium-focused insight into IHD signature RNAs. The specific gene expression changes characterized here, pave the way for future disease mechanism-based identification of biomarkers for early detection and treatment of IHD.
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Affiliation(s)
- Severi Mulari
- Department of Pharmacology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Arda Eskin
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University (METU), Ankara, Turkey
| | - Milla Lampinen
- Department of Pharmacology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Oral and Maxillofacial Diseases, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Annu Nummi
- Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tuomo Nieminen
- Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kari Teittinen
- Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Teija Ojala
- Department of Pharmacology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matti Kankainen
- Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Antti Vento
- Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jari Laurikka
- Department of Cardiothoracic Surgery, Heart Center, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Markku Kupari
- Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Ari Harjula
- Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nurcan Tuncbag
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University (METU), Ankara, Turkey
- Department of Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey
- School of Medicine, Koc University, Istanbul, Turkey
| | - Esko Kankuri
- Department of Pharmacology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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20
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Distinct Metabolomic Signatures in Preclinical and Obstructive Hypertrophic Cardiomyopathy. Cells 2021; 10:cells10112950. [PMID: 34831173 PMCID: PMC8616419 DOI: 10.3390/cells10112950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 12/15/2022] Open
Abstract
Hypertrophic Cardiomyopathy (HCM) is a common inherited heart disease with poor risk prediction due to incomplete penetrance and a lack of clear genotype–phenotype correlations. Advanced imaging techniques have shown altered myocardial energetics already in preclinical gene variant carriers. To determine whether disturbed myocardial energetics with the potential to serve as biomarkers are also reflected in the serum metabolome, we analyzed the serum metabolome of asymptomatic carriers in comparison to healthy controls and obstructive HCM patients (HOCM). We performed non-quantitative direct-infusion high-resolution mass spectrometry-based untargeted metabolomics on serum from fasted asymptomatic gene variant carriers, symptomatic HOCM patients and healthy controls (n = 31, 14 and 9, respectively). Biomarker panels that discriminated the groups were identified by performing multivariate modeling with gradient-boosting classifiers. For all three group-wise comparisons we identified a panel of 30 serum metabolites that best discriminated the groups. These metabolite panels performed equally well as advanced cardiac imaging modalities in distinguishing the groups. Seven metabolites were found to be predictive in two different comparisons and may play an important role in defining the disease stage. This study reveals unique metabolic signatures in serum of preclinical carriers and HOCM patients that may potentially be used for HCM risk stratification and precision therapeutics.
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21
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AN PENG, WANG ZHONGQIU, SONG PING, WU XIAXIA, DU HUI, SONG LINA. ANALYSIS OF ECHOCARDIOGRAPHIC AND MRI ANATOMICAL CHARACTERISTICS AND HEMODYNAMIC CHARACTERISTICS OF LEFT VENTRICULAR OUTFLOW TRACT STENOSIS. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421400698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Objective: To analyze the anatomical morphological and hemodynamic characteristics of left ventricular outflow tract stenosis (LVOTS) by echocardiography and MRI. Methods: The Echocardiography data of 112 patients with LVOTS were retrospectively analyzed by measuring the basal interventricular septal thickness (IVST-b), the left ventricle posterior wall thickness (LVPWT), and the peak pressure gradient of LVOTS, as well as by observing the characteristics of spectral pattern and the presence of systolic anterior motion of mitral valve leaflets. A Siemens 3.0T MRI scanner was used to scan the contrast-enhanced left ventricular (LV) volume of all cases. The obtained end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV) and ejection fraction (EF) of LV were compared with the Echocardiography results. Results: The 112 patients were divided into four groups: hypertrophic obstructive cardiomyopathy (Group I, 42 cases), hypertensive left ventricular hypertrophy (Group II, 40 cases), basal septal hypertrophy in the elderly (Group III, 21 cases), and the subaortic membrane (Group IV, 9 cases). The continuous wave (CW) Doppler characteristic of Groups I, II, and III was broadsword-shaped jet, and that of Group IV was equilateral triangle-like spectrum. The IVST-b, IVST-b/LVPWT ratio and peak pressure gradient of LVOTS of Group I was significantly higher than those in Groups II and III ([Formula: see text]). The LVPWT of the first three groups was slightly correlated with the LVOTS peak pressure gradient ([Formula: see text], respectively, [Formula: see text]). There were no statistically significant differences between Echocardiography and MRI results regarding the LV EDV, ESV, SV, and EF ([Formula: see text]), and no statistically significant differences were found between Echocardiography and MRI results regarding the myocardial thicknesses of septal, anterior, lateral, and inferior walls ([Formula: see text]). The Pearson’s correlation analysis demonstrated correlations between MRI and Echocardiography results for LV EDV, ESV, SV, and EF ([Formula: see text], 0.002, 0.009, and [Formula: see text], respectively). The MRI enhancement was shown as delayed enhancement in 52 cases, localized enhancement in 8 cases, diffuse enhancement in 6 cases, and transmural enhancement in 3 cases, with abnormal enhancement lesions distributed in the area of ventricular septum free wall junctions and ventricular septum. Conclusion: Using MRI to evaluate LV function of hypertrophic cardiomyopathy is reliable and accurate. MRI enhancement can be used for the quantitative measurement of myocardial fibrosis. Echocardiography can distinguish the stenosis types of LVOTS. The IVST-b and existence of SAM may be important anatomical characteristics determining the degree of dynamic stenosis, and MRI combined with Echocardiography can provide important detailed information.
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Affiliation(s)
- PENG AN
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, The First Clinical Medical College, 155 Hanzhong Road, Nanjing 210029, Jiangsu Province, P. R. China
| | - ZHONGQIU WANG
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, The First Clinical Medical College, 155 Hanzhong Road, Nanjing 210029, Jiangsu Province, P. R. China
| | - PING SONG
- Department of Radiology, Xiangyang NO. 1 People’s Hospital, Hubei University of Medicine, Xiangyang 441000, P. R. China
| | - XIAXIA WU
- Department of Radiology, Xiangyang NO. 1 People’s Hospital, Hubei University of Medicine, Xiangyang 441000, P. R. China
| | - HUI DU
- Department of Radiology, Xiangyang NO. 1 People’s Hospital, Hubei University of Medicine, Xiangyang 441000, P. R. China
| | - LINA SONG
- Department of Radiology, Xiangyang NO. 1 People’s Hospital, Hubei University of Medicine, Xiangyang 441000, P. R. China
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22
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Ni M, Zhou J, Zhu Z, Yuan J, Gong W, Zhu J, Zheng Z, Zhao H. A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors. Front Cell Dev Biol 2021; 9:712196. [PMID: 34527671 PMCID: PMC8437375 DOI: 10.3389/fcell.2021.712196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/09/2021] [Indexed: 12/30/2022] Open
Abstract
Background Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types. Methods In total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were divided into C1 (training and test datasets) and C2 (validation dataset) cohorts. Mass spectrometry (MS) data of all samples were acquired in data-independent acquisition (DIA) mode with an Orbitrap mass spectrometer and analyzed using DIA-NN software. The generated classifier was trained with Random Forest algorithm from the training dataset and validated in the test and validation datasets. Serum CA125 and HE4 levels were additionally determined in all patients. Finally, classification accuracy of the classifier, serum CA125 and serum HE4 in all samples were evaluated and plotted via receiver operating characteristic (ROC) analysis. Results In total, 2,199 proteins were quantified and 69 identified with differential expression in benign and malignant groups of the C1 cohort. A classifier incorporating five proteins (WFDC2, PTMA, PVRL4, FIBA, and PVRL2) was trained and validated in this study. Evaluation of the performance of the classifier revealed AUC values of 0.970 and 0.952 in the test and validation datasets, respectively. In all 132 patients, AUCs of 0.966, 0.947, and 0.979 were achieved with the classifier, serum CA125, and serum HE4, respectively. Among eight patients with early stage malignancy, 7, 6, and 4 were accurately diagnosed based on classifier, serum CA125, and serum HE4, respectively. Conclusion The novel classifier incorporating a urinary protein panel presents a promising non-invasive diagnostic biomarker for classifying benign and malignant ovarian tumors.
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Affiliation(s)
- Maowei Ni
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.,The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jie Zhou
- Department of Physiology, Zhejiang Chinese Medical University, Hangzhou, China.,Tongde Hospital of Zhejiang Province, Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, China
| | - Zhihui Zhu
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingtao Yuan
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Wangang Gong
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jianqing Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhiguo Zheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Huajun Zhao
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
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23
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Boschetti E, Zilberstein G, Righetti PG. Combinatorial peptides: A library that continuously probes low-abundance proteins. Electrophoresis 2021; 43:355-369. [PMID: 34498305 DOI: 10.1002/elps.202100131] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/31/2021] [Accepted: 08/24/2021] [Indexed: 12/12/2022]
Abstract
After a decade of experimental applications, it is the objective of this review to make a point on combinatorial peptide ligand libraries dedicated to low-abundance proteins from animals to plants and to microorganism proteomics. It is, thus, at the light of the recent technical developments and applications that we will examine the state of the art, its usage within the scientific community, and its openness to unexplored fields. The improvements of the methodology and its implementation in connection with analytical determinations of combinatorial peptide ligand library (CPLL)-treated samples are extensively reviewed and commented upon. Relevant examples covering few critical aspects describe the performance of the technology. Finally, a reflection on the technological future is attempted in particular by involving new concepts adapted to the limited availability of certain biological samples.
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Affiliation(s)
| | | | - Pier Giorgio Righetti
- Department of Chemistry Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
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24
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Shimada YJ, Raita Y, Liang LW, Maurer MS, Hasegawa K, Fifer MA, Reilly MP. Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy. Circ Heart Fail 2021; 14:e007849. [PMID: 34192899 DOI: 10.1161/circheartfailure.120.007849] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Hypertrophic cardiomyopathy (HCM) is caused by mutations in the genes coding for proteins essential in normal myocardial contraction. However, it remains unclear through which molecular pathways gene mutations mediate the development of HCM. The objectives were to determine plasma protein biomarkers of HCM and to reveal molecular pathways differentially regulated in HCM. METHODS We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma proteomics profiling of 1681 proteins. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based discrimination model with data from 1 institution (ie, the training set). We tested the discriminative ability in independent samples from the other institution (ie, the test set). As an exploratory analysis, we executed pathway analysis of significantly dysregulated proteins. Pathways with false discovery rate <0.05 were declared positive. RESULTS The study included 266 cases and 167 controls (n=308 in the training set; n=125 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.83-0.94) in the test set. Pathway analysis revealed that the Ras-MAPK (mitogen-activated protein kinase) pathway, along with its upstream and downstream pathways, was upregulated in HCM. Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated. CONCLUSIONS This study serves as the largest-scale investigation with the most comprehensive proteomics profiling in HCM, revealing circulating biomarkers and exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in HCM.
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Affiliation(s)
- Yuichi J Shimada
- Division of Cardiology, Department of Medicine (Y.J.S., L.W.L., M.S.M., M.P.R.), Columbia University Irving Medical Center, New York, NY.,Cardiology Division, Department of Medicine (Y.J.S., M.A.F.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Yoshihiko Raita
- Department of Emergency Medicine (Y.R., K.H.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Lusha W Liang
- Division of Cardiology, Department of Medicine (Y.J.S., L.W.L., M.S.M., M.P.R.), Columbia University Irving Medical Center, New York, NY
| | - Mathew S Maurer
- Division of Cardiology, Department of Medicine (Y.J.S., L.W.L., M.S.M., M.P.R.), Columbia University Irving Medical Center, New York, NY
| | - Kohei Hasegawa
- Department of Emergency Medicine (Y.R., K.H.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Michael A Fifer
- Cardiology Division, Department of Medicine (Y.J.S., M.A.F.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine (Y.J.S., L.W.L., M.S.M., M.P.R.), Columbia University Irving Medical Center, New York, NY.,Irving Institute for Clinical and Translational Research (M.P.R.), Columbia University Irving Medical Center, New York, NY
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25
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Yifan C, Jianfeng S, Jun P. Development and Validation of a Random Forest Diagnostic Model of Acute Myocardial Infarction Based on Ferroptosis-Related Genes in Circulating Endothelial Cells. Front Cardiovasc Med 2021; 8:663509. [PMID: 34262953 PMCID: PMC8274450 DOI: 10.3389/fcvm.2021.663509] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/20/2021] [Indexed: 12/22/2022] Open
Abstract
The high incidence and mortality of acute myocardial infarction (MI) drastically threaten human life and health. In the past few decades, the rise of reperfusion therapy has significantly reduced the mortality rate, but the MI diagnosis is still by means of the identification of myocardial injury markers without highly specific biomarkers of microcirculation disorders. Ferroptosis is a novel reported type of programmed cell death, which plays an important role in cancer development. Maintaining iron homeostasis in cells is essential for heart function, and its role in the pathological process of ischemic organ damages remains unclear. Being quickly detected through blood tests, circulating endothelial cells (CECs) have the potential for early judgment of early microcirculation disorders. In order to explore the role of ferroptosis-related genes in the early diagnosis of acute MI, we relied on two data sets from the GEO database to first detect eight ferroptosis-related genes differentially expressed in CECs between the MI and healthy groups in this study. After comparing different supervised learning algorithms, we constructed a random forest diagnosis model for acute MI based on these ferroptosis-related genes with a compelling diagnostic performance in both the validation (AUC = 0.8550) and test set (AUC = 0.7308), respectively. These results suggest that the ferroptosis-related genes might play an important role in the early stage of MI and have the potential as specific diagnostic biomarkers for MI.
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26
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Hinkel R, Batkai S, Bähr A, Bozoglu T, Straub S, Borchert T, Viereck J, Howe A, Hornaschewitz N, Oberberger L, Jurisch V, Kozlik-Feldmann R, Freudenthal F, Ziegler T, Weber C, Sperandio M, Engelhardt S, Laugwitz KL, Moretti A, Klymiuk N, Thum T, Kupatt C. AntimiR-132 Attenuates Myocardial Hypertrophy in an Animal Model of Percutaneous Aortic Constriction. J Am Coll Cardiol 2021; 77:2923-2935. [PMID: 34112319 DOI: 10.1016/j.jacc.2021.04.028] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/01/2021] [Accepted: 04/09/2021] [Indexed: 01/12/2023]
Abstract
BACKGROUND Pathological cardiac hypertrophy is a result of afterload-increasing pathologies including untreated hypertension and aortic stenosis. It features progressive adverse cardiac remodeling, myocardial dysfunction, capillary rarefaction, and interstitial fibrosis often leading to heart failure. OBJECTIVES This study aimed to establish a novel porcine model of pressure-overload-induced heart failure and to determine the effect of inhibition of microribonucleic acid 132 (miR-132) on heart failure development in this model. METHODS This study developed a novel porcine model of percutaneous aortic constriction by implantation of a percutaneous reduction stent in the thoracic aorta, inducing progressive remodeling at day 56 (d56) after pressure-overload induction. In this study, an antisense oligonucleotide specifically inhibiting miR-132 (antimiR-132), was regionally applied via intracoronary injection at d0 (percutaneous transverse aortic constriction induction) and d28. RESULTS At d56, antimiR-132 treatment diminished cardiomyocyte cross-sectional area (188.9 ± 2.8 vs. 258.4 ± 9.0 μm2 in untreated hypertrophic hearts) and improved global cardiac function (ejection fraction 48.9 ± 1.0% vs. 36.1 ± 1.7% in control hearts). Moreover, at d56 antimiR-132-treated hearts displayed less increase of interstitial fibrosis compared with sham-operated hearts (Δsham 1.8 ± 0.5%) than control hearts (Δsham 10.8 ± 0.6%). Of note, cardiac platelet and endothelial cell adhesion molecule 1+ capillary density was higher in the antimiR-132-treated hearts (647 ± 20 cells/mm2) compared with in the control group (485 ± 23 cells/mm2). CONCLUSIONS The inhibition of miR-132 is a valid strategy in prevention of heart failure progression in hypertrophic heart disease and may be developed as a treatment for heart failure of nonischemic origin.
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Affiliation(s)
- Rabea Hinkel
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany; Institute for Cardiovascular Prevention, Ludwig-Maximilians-University Munich, Munich, Germany; Laboratory Animal Science Unit, German Primate Centre, Goettingen, Stiftung Tierärztliche Hochschule Hannover, Hannover, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Goettingen, Munich, Germany. https://twitter.com/Rabea08515954
| | - Sandor Batkai
- Institute of Molecular and Translational Therapeutic Strategies, Hannover Medical School, Hannover, Germany; Cardior Pharmaceuticals GmbH, Hannover, Germany
| | - Andrea Bähr
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany; Institute for Cardiovascular Prevention, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Tarik Bozoglu
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany; Institute for Cardiovascular Prevention, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Sarah Straub
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | - Andrea Howe
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nadja Hornaschewitz
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany
| | - Lisa Oberberger
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany
| | - Victoria Jurisch
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Franz Freudenthal
- Products for Medicine, SRL (sociedad de responsibilidat limitada), Obajes, La Paz, Bolivia
| | - Tilman Ziegler
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Christian Weber
- Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany; Institute for Cardiovascular Prevention, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Markus Sperandio
- Walter-Brendel Centre of Experimental Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Stefan Engelhardt
- Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany; Institut für Pharmakologie und Toxikologie, Technical University of Munich, Munich, Germany
| | - Karl Ludwig Laugwitz
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Alessandra Moretti
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Nik Klymiuk
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Thomas Thum
- Institute of Molecular and Translational Therapeutic Strategies, Hannover Medical School, Hannover, Germany; Cardior Pharmaceuticals GmbH, Hannover, Germany.
| | - Christian Kupatt
- Klinik und Poliklinik für Innere Medizin I, University Clinic rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Zentrum für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany; Institute for Cardiovascular Prevention, Ludwig-Maximilians-University Munich, Munich, Germany.
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27
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Stege NM, de Boer RA, van den Berg MP, Silljé HHW. The Time Has Come to Explore Plasma Biomarkers in Genetic Cardiomyopathies. Int J Mol Sci 2021; 22:2955. [PMID: 33799487 PMCID: PMC7998409 DOI: 10.3390/ijms22062955] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/09/2021] [Accepted: 03/11/2021] [Indexed: 12/17/2022] Open
Abstract
For patients with hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) or arrhythmogenic cardiomyopathy (ACM), screening for pathogenic variants has become standard clinical practice. Genetic cascade screening also allows the identification of relatives that carry the same mutation as the proband, but disease onset and severity in mutation carriers often remains uncertain. Early detection of disease onset may allow timely treatment before irreversible changes are present. Although plasma biomarkers may aid in the prediction of disease onset, monitoring relies predominantly on identifying early clinical symptoms, on imaging techniques like echocardiography (Echo) and cardiac magnetic resonance imaging (CMR), and on (ambulatory) electrocardiography (electrocardiograms (ECGs)). In contrast to most other cardiac diseases, which are explained by a combination of risk factors and comorbidities, genetic cardiomyopathies have a clear primary genetically defined cardiac background. Cardiomyopathy cohorts could therefore have excellent value in biomarker studies and in distinguishing biomarkers related to the primary cardiac disease from those related to extracardiac, secondary organ dysfunction. Despite this advantage, biomarker investigations in cardiomyopathies are still limited, most likely due to the limited number of carriers in the past. Here, we discuss not only the potential use of established plasma biomarkers, including natriuretic peptides and troponins, but also the use of novel biomarkers, such as cardiac autoantibodies in genetic cardiomyopathy, and discuss how we can gauge biomarker studies in cardiomyopathy cohorts for heart failure at large.
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Affiliation(s)
| | | | | | - Herman H. W. Silljé
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, AB43, 9713 GZ Groningen, The Netherlands; (N.M.S.); (R.A.d.B.); (M.P.v.d.B.)
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28
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Liśkiewicz AD, Marczak Ł, Bogus K, Liśkiewicz D, Przybyła M, Lewin-Kowalik J. Proteomic and Structural Manifestations of Cardiomyopathy in Rat Models of Obesity and Weight Loss. Front Endocrinol (Lausanne) 2021; 12:568197. [PMID: 33716957 PMCID: PMC7945951 DOI: 10.3389/fendo.2021.568197] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 01/05/2021] [Indexed: 12/12/2022] Open
Abstract
Obesity cardiomyopathy increases the risk of heart failure and death. Obesity is curable, leading to the restoration of the heart phenotype, but it is not clear if there are any after-effects of obesity present after weight loss. We characterize the proteomic landscape of obesity cardiomyopathy with an evaluation of whether the cardiac phenotype is still shaped after weight loss. Cardiomyopathy was validated by cardiac hypertrophy, fibrosis, oversized myocytes, and mTOR upregulation in a rat model of cafeteria diet-induced developmental obesity. By global proteomic techniques (LC-MS/MS) a plethora of molecular changes was observed in the heart and circulation of obese animals, suggesting abnormal utilization of metabolic substrates. This was confirmed by increased levels of cardiac ACSL-1, a key enzyme for fatty acid degradation and decreased GLUT-1, a glucose transporter in obese rats. Calorie restriction and weight loss led to the normalization of the heart's size, but fibrosis was still excessive. The proteomic compositions of cardiac tissue and plasma were different after weight loss as compared to control. In addition to morphological consequences, obesity cardiomyopathy involves many proteomic changes. Weight loss provides for a partial repair of the heart's architecture, but the trace of fibrotic deposition and proteomic alterations may occur.
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Affiliation(s)
- Arkadiusz D. Liśkiewicz
- Department of Physiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
- Laboratory of Molecular Biology, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland
| | - Łukasz Marczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Katarzyna Bogus
- Department of Histology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
| | - Daniela Liśkiewicz
- Laboratory of Molecular Biology, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland
- Department for Experimental Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
| | - Marta Przybyła
- Department for Experimental Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
| | - Joanna Lewin-Kowalik
- Department of Physiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
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29
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Seshadri DR, Thom ML, Harlow ER, Gabbett TJ, Geletka BJ, Hsu JJ, Drummond CK, Phelan DM, Voos JE. Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden. Front Sports Act Living 2021; 2:630576. [PMID: 33554111 PMCID: PMC7859639 DOI: 10.3389/fspor.2020.630576] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 12/22/2020] [Indexed: 12/26/2022] Open
Abstract
Wearable sensors enable the real-time and non-invasive monitoring of biomechanical, physiological, or biochemical parameters pertinent to the performance of athletes. Sports medicine researchers compile datasets involving a multitude of parameters that can often be time consuming to analyze in order to create value in an expeditious and accurate manner. Machine learning and artificial intelligence models may aid in the clinical decision-making process for sports scientists, team physicians, and athletic trainers in translating the data acquired from wearable sensors to accurately and efficiently make decisions regarding the health, safety, and performance of athletes. This narrative review discusses the application of commercial sensors utilized by sports teams today and the emergence of descriptive analytics to monitor the internal and external workload, hydration status, sleep, cardiovascular health, and return-to-sport status of athletes. This review is written for those who are interested in the application of wearable sensor data and data science to enhance performance and reduce injury burden in athletes of all ages.
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Affiliation(s)
- Dhruv R. Seshadri
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mitchell L. Thom
- Case Western Reserve University School of Medicine, Cleveland, OH, United States
| | - Ethan R. Harlow
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
- Sports Medicine Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Tim J. Gabbett
- Gabbett Performance Solutions, Brisbane, QLD, Australia
- Centre for Health Research, University of Southern Queensland, Ipswich, QLD, Australia
| | - Benjamin J. Geletka
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
- Sports Medicine Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Jeffrey J. Hsu
- Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Colin K. Drummond
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Dermot M. Phelan
- Sports Cardiology, Hypertrophic Cardiomyopathy Program, Sanger Heart and Vascular Institute, Atrium Health, Charlotte, NC, United States
| | - James E. Voos
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
- Sports Medicine Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
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30
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Gil WS, Ávila Vidal LA, Vásquez Salguero MA, Cajiao MB, Peña CV. Genetic variant affecting the myosin light chain 2 related to familial hypertrophic cardiomyopathy. Intractable Rare Dis Res 2020; 9:229-232. [PMID: 33139982 PMCID: PMC7586884 DOI: 10.5582/irdr.2020.03042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Familial hypertrophic cardiomyopathy (FHCM) is a genetic disease characterized by left ventricle (LV) or interventricular septum hypertrophy. FHCM is a common heart disease (affecting 1 out of 500 individuals) associated with genetic variants in genes related to the sarcomere, including the MYL2 (myosin light chain 2) gene that is affected in 1 to 3% of the cases. As described in this report, the genetic mutation p.Gly87Ala, rs 397516399 in the MYL2 gene is likely pathogenic. Reported here is the case of a 37-year-old Colombian man with asymmetric septal hypertrophic cardiomyopathy and ventricular tachycardia. The man had progressive symptomatology, a family history of FHCM with a dominant inheritance pattern, a mother and 2 brothers with FHCM, and 2 brothers who died suddenly before the age of 35. A molecular panel of 17 genes for hypertrophic cardiomyopathy identified a heterozygous variant, p.Gly87Ala, of the MYL2 gene. This variant can be found in Ensembl, dbSNP, and ClinVar, where it has conflicting interpretations: it either has an uncertain significance or it is likely pathogenic. This is the first report of a Colombian case of FHCM secondary to a mutation in the MYL2 gene, highlighting the importance of molecular diagnosis, genetic counseling, and bioinformatic analysis in these patients.
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Affiliation(s)
- Wilmar Saldarriaga Gil
- Health Faculty, Universidad del Valle, Cali, Colombia
- Hospital Universitario del Valle, Cali, Colombia
- Address correspondence to:Wilmar Saldarriaga Gil, Universidad del Valle, Cali, Colombia. Calle 4b # 36-00, Health Faculty, Universidad del Valle, Cali, Colombia. E-mail:
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31
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Gruson D, Bernardini S, Dabla PK, Gouget B, Stankovic S. Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine. Clin Chim Acta 2020; 509:67-71. [PMID: 32505771 DOI: 10.1016/j.cca.2020.06.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 12/11/2022]
Abstract
Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models and in turn allows the development of complex algorithms which are capable to simulate human intelligence such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to support decision‑making and improve diagnostic and prognostic performance. Attempt has also been made to explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care, to enablecost-effectiveness with reducereadmissionand mortality rates. Our review addresses the integration of AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need of value creation services in cardiovascular medicine.
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Affiliation(s)
- Damien Gruson
- Department of Clinical Biochemistry, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium; Pôle de recherche en Endocrinologie, Diabète et Nutrition, Institut de Recherche Expérimentale et Clinique, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy.
| | - Sergio Bernardini
- Department of Experimental Medicine, University of Tor Vergata, Rome, Italy; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Pradeep Kumar Dabla
- Department of Biochemistry, G.B Pant Institute of Postgraduate Medical Education & Research, Associated to Maulana Azad Medical College, New Delhi, India; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Bernard Gouget
- President-Healthcare Division Committee, Comité Français d'accréditation (Cofrac), 75012 Paris, France; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
| | - Sanja Stankovic
- Center for Medical Biochemistry, Clinical Center of Serbia, Belgrade, Serbia; Emerging Technologies Division-MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Italy
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32
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Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements. Processes (Basel) 2020. [DOI: 10.3390/pr8060638] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Because of the promising results obtained by machine learning (ML) approaches in several fields, every day is more common, the utilization of ML to solve problems in bioinformatics. In genomics, a current issue is to detect and classify transposable elements (TEs) because of the tedious tasks involved in bioinformatics methods. Thus, ML was recently evaluated for TE datasets, demonstrating better results than bioinformatics applications. A crucial step for ML approaches is the selection of metrics that measure the realistic performance of algorithms. Each metric has specific characteristics and measures properties that may be different from the predicted results. Although the most commonly used way to compare measures is by using empirical analysis, a non-result-based methodology has been proposed, called measure invariance properties. These properties are calculated on the basis of whether a given measure changes its value under certain modifications in the confusion matrix, giving comparative parameters independent of the datasets. Measure invariance properties make metrics more or less informative, particularly on unbalanced, monomodal, or multimodal negative class datasets and for real or simulated datasets. Although several studies applied ML to detect and classify TEs, there are no works evaluating performance metrics in TE tasks. Here, we analyzed 26 different metrics utilized in binary, multiclass, and hierarchical classifications, through bibliographic sources, and their invariance properties. Then, we corroborated our findings utilizing freely available TE datasets and commonly used ML algorithms. Based on our analysis, the most suitable metrics for TE tasks must be stable, even using highly unbalanced datasets, multimodal negative class, and training datasets with errors or outliers. Based on these parameters, we conclude that the F1-score and the area under the precision-recall curve are the most informative metrics since they are calculated based on other metrics, providing insight into the development of an ML application.
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33
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Van Gool A, Corrales F, Čolović M, Krstić D, Oliver-Martos B, Martínez-Cáceres E, Jakasa I, Gajski G, Brun V, Kyriacou K, Burzynska-Pedziwiatr I, Wozniak LA, Nierkens S, Pascual García C, Katrlik J, Bojic-Trbojevic Z, Vacek J, Llorente A, Antohe F, Suica V, Suarez G, t'Kindt R, Martin P, Penque D, Martins IL, Bodoki E, Iacob BC, Aydindogan E, Timur S, Allinson J, Sutton C, Luider T, Wittfooth S, Sammar M. Analytical techniques for multiplex analysis of protein biomarkers. Expert Rev Proteomics 2020; 17:257-273. [PMID: 32427033 DOI: 10.1080/14789450.2020.1763174] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The importance of biomarkers for pharmaceutical drug development and clinical diagnostics is more significant than ever in the current shift toward personalized medicine. Biomarkers have taken a central position either as companion markers to support drug development and patient selection, or as indicators aiming to detect the earliest perturbations indicative of disease, minimizing therapeutic intervention or even enabling disease reversal. Protein biomarkers are of particular interest given their central role in biochemical pathways. Hence, capabilities to analyze multiple protein biomarkers in one assay are highly interesting for biomedical research. AREAS COVERED We here review multiple methods that are suitable for robust, high throughput, standardized, and affordable analysis of protein biomarkers in a multiplex format. We describe innovative developments in immunoassays, the vanguard of methods in clinical laboratories, and mass spectrometry, increasingly implemented for protein biomarker analysis. Moreover, emerging techniques are discussed with potentially improved protein capture, separation, and detection that will further boost multiplex analyses. EXPERT COMMENTARY The development of clinically applied multiplex protein biomarker assays is essential as multi-protein signatures provide more comprehensive information about biological systems than single biomarkers, leading to improved insights in mechanisms of disease, diagnostics, and the effect of personalized medicine.
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Affiliation(s)
- Alain Van Gool
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud Institute of Molecular Life Sciences, Radboud University Medical Center , Nijmegen, The Netherlands
| | - Fernado Corrales
- Functional Proteomics Laboratory, Centro Nacional De Biotecnología , Madrid, Spain
| | - Mirjana Čolović
- Department of Physical Chemistry, "Vinča" Institute of Nuclear Sciences, University of Belgrade , Belgrade, Serbia
| | - Danijela Krstić
- Institute of Medical Chemistry, Faculty of Medicine, University of Belgrade , Belgrade, Serbia
| | - Begona Oliver-Martos
- Neuroimmunology and Neuroinflammation Group. Instituto De Investigación Biomédica De Málaga-IBIMA. UGC Neurociencias, Hospital Regional Universitario De Málaga , Malaga, Spain
| | - Eva Martínez-Cáceres
- Immunology Division, LCMN, Germans Trias I Pujol University Hospital and Research Institute, Campus Can Ruti, Badalona, and Department of Cellular Biology, Physiology and Immunology, Universitat Autònoma De Barcelona , Cerdanyola Del Vallès, Spain
| | - Ivone Jakasa
- Laboratory for Analytical Chemistry, Department of Chemistry and Biochemistry, Faculty of Food Technology and Biotechnology, University of Zagreb , Zagreb, Croatia
| | - Goran Gajski
- Mutagenesis Unit, Institute for Medical Research and Occupational Health , Zagreb, Croatia
| | - Virginie Brun
- Université Grenoble Alpes, CEA, Inserm, IRIG, BGE , Grenoble, France
| | - Kyriacos Kyriacou
- Department of Electron Microscopy/Molecular Biology, The Cyprus School of Molecular Medicine/The Cyprus Institute of Neurology and Genetics , Nicosia, Cyprus
| | - Izabela Burzynska-Pedziwiatr
- Medical Faculty, Department of Biomedical Sciences, Chair of Medical Biology & Department of Structural Biology, Medical University of Lodz , Łódź, Poland
| | - Lucyna Alicja Wozniak
- Medical Faculty, Department of Biomedical Sciences, Chair of Medical Biology & Department of Structural Biology, Medical University of Lodz , Łódź, Poland
| | - Stephan Nierkens
- Center for Translational Immunology, University Medical Center Utrecht & Princess Máxima Center for Pediatric Oncology , Utrecht, The Netherlands
| | - César Pascual García
- Materials Research and Technology Department, Luxembourg Institute of Science and Technology (LIST) , Belvaux, Luxembourg
| | - Jaroslav Katrlik
- Department of Glycobiotechnology, Institute of Chemistry, Slovak Academy of Sciences , Bratislava, Slovakia
| | - Zanka Bojic-Trbojevic
- Laboratory for Biology of Reproduction, Institute for the Application of Nuclear Energy - INEP, University of Belgrade , Belgrade, Serbia
| | - Jan Vacek
- Department of Medical Chemistry and Biochemistry, Faculty of Medicine and Dentistry, Palacky University , Olomouc, Czech Republic
| | - Alicia Llorente
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital , Oslo, Norway
| | - Felicia Antohe
- Proteomics Department, Institute of Cellular Biology and Pathology "N. Simionescu" of the Romanian Academy , Bucharest, Romania
| | - Viorel Suica
- Proteomics Department, Institute of Cellular Biology and Pathology "N. Simionescu" of the Romanian Academy , Bucharest, Romania
| | - Guillaume Suarez
- Center for Primary Care and Public Health (Unisanté), University of Lausanne , Lausanne, Switzerland
| | - Ruben t'Kindt
- Research Institute for Chromatography (RIC) , Kortrijk, Belgium
| | - Petra Martin
- Department of Medical Oncology, Midland Regional Hospital Tullamore/St. James's Hospital , Dublin, Ireland
| | - Deborah Penque
- Human Genetics Department, Instituto Nacional De Saúde Dr Ricardo Jorge, Lisboa, Portugal and Centre for Toxicogenomics and Human Health, Universidade Nova De Lisboa , Lisbon,Portugal
| | - Ines Lanca Martins
- Human Genetics Department, Instituto Nacional De Saúde Dr Ricardo Jorge, Lisboa, Portugal and Centre for Toxicogenomics and Human Health, Universidade Nova De Lisboa , Lisbon,Portugal
| | - Ede Bodoki
- Analytical Chemistry Department, Faculty of Pharmacy, "Iuliu Hatieganu" University of Medicine and Pharmacy , Cluj-Napoca, Romania
| | - Bogdan-Cezar Iacob
- Analytical Chemistry Department, Faculty of Pharmacy, "Iuliu Hatieganu" University of Medicine and Pharmacy , Cluj-Napoca, Romania
| | - Eda Aydindogan
- Department of Chemistry, Graduate School of Sciences and Engineering, Koç University , Istanbul, Turkey
| | - Suna Timur
- Institute of Natural Sciences, Department of Biochemistry, Ege University , Izmir, Turkey
| | | | | | - Theo Luider
- Department of Neurology, Erasmus MC , Rotterdam, The Netherlands
| | | | - Marei Sammar
- Ephraim Katzir Department of Biotechnology Engineering, ORT Braude College , Karmiel, Israel
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