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Tsare EPG, Klapa MI, Moschonas NK. Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis. Hum Genomics 2024; 18:15. [PMID: 38326862 DOI: 10.1186/s40246-023-00565-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/12/2023] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. METHODS The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. RESULTS The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. CONCLUSIONS The implemented workflow could be used for other multifactorial diseases.
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Affiliation(s)
- Evridiki-Pandora G Tsare
- Department of General Biology, School of Medicine, University of Patras, Patras, Greece
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece
| | - Maria I Klapa
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece.
| | - Nicholas K Moschonas
- Department of General Biology, School of Medicine, University of Patras, Patras, Greece.
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece.
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Radhika A, Burgula S, Badapanda C, Hussain T, Naushad SM. Elucidation of genetic determinants of dyslipidaemia using a global screening array for the early detection of coronary artery disease. Mamm Genome 2023; 34:632-643. [PMID: 37668737 DOI: 10.1007/s00335-023-10017-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 08/16/2023] [Indexed: 09/06/2023]
Abstract
Dyslipidemia is a major risk factor for the development of coronary artery disease (CAD). Understanding the genetic determinants of dyslipidemia can provide valuable information on the pathogenesis of CAD and aid in the development of early detection strategies. In this study, we used a Global Screening Array (GSA) to elucidate the genetic factors associated with dyslipidemia and their potential role in the prediction of CAD. We conducted a GSA-based association study in 265 subjects to identify the genetic loci associated with dyslipidemia traits using Multiple Linear Regression (MLR) and Logistic Regression (LR), Classification and Regression Tree (CART), and Manhattan plots. We identified an association between dyslipidemia and variants identified in genes such as JCAD, GLIS3, CD38, FN1, CELSR2, MTNR1B, GIPR, DYM, APOB, APOE, ADCY5. The MLR models explained 62%, 71%, and 81% of the variability in HDL, LDL, and triglycerides, respectively. The Area Under the Curve (AUC) values in the LR models of HDL, LDL, and triglycerides were 1.00, 0.94, and 0.95, respectively. CART models identified novel gene-gene interactions influencing the risk for dyslipidemia. To conclude, we have identified the association of 12 SNVs with dyslipidemia and demonstrated their clinical utility in four different models such as MLR, LR, CART, and Manhattan plots. The identified genetic variants and associated pathways shed light on the underlying biology of dyslipidemia and offer potential avenues for precision medicine strategies in the management of CAD.
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Affiliation(s)
- Ananthaneni Radhika
- Genomics Division, Yoda Lifeline Diagnostics Pvt Ltd, 6-3-862/A, Lal Bungalow Add On, Ameerpet, Hyderabad, 500016, India
- Department of Microbiology, Osmania University, Taranaka, Hyderabad, 500007, India
| | - Sandeepta Burgula
- Department of Microbiology, Osmania University, Taranaka, Hyderabad, 500007, India.
| | - Chandan Badapanda
- Genomics Division, Yoda Lifeline Diagnostics Pvt Ltd, 6-3-862/A, Lal Bungalow Add On, Ameerpet, Hyderabad, 500016, India
| | - Tajamul Hussain
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, 11451, Riyadh, Saudi Arabia
- Center of Excellence in Biotechnology Research, King Saud University, 11451, Riyadh, Saudi Arabia
| | - Shaik Mohammad Naushad
- Genomics Division, Yoda Lifeline Diagnostics Pvt Ltd, 6-3-862/A, Lal Bungalow Add On, Ameerpet, Hyderabad, 500016, India.
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Zhang G, Cui X, Qin Z, Wang Z, Lu Y, Xu Y, Xu S, Tang L, Zhang L, Liu G, Wang X, Zhang J, Tang J. Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability. iScience 2023; 26:107587. [PMID: 37664595 PMCID: PMC10470306 DOI: 10.1016/j.isci.2023.107587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 05/02/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.
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Affiliation(s)
- Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Xiaolin Cui
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China
| | - Zhen Qin
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Zeyu Wang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Yongzheng Lu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Yanyan Xu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Shuai Xu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Laiyi Tang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Li Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Gangqiong Liu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Xiaofang Wang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Jinying Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
| | - Junnan Tang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, Henan 450052, China
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