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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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Functionally enigmatic genes: a case study of the brain ignorome. PLoS One 2014; 9:e88889. [PMID: 24523945 PMCID: PMC3921226 DOI: 10.1371/journal.pone.0088889] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2013] [Accepted: 01/14/2014] [Indexed: 12/26/2022] Open
Abstract
What proportion of genes with intense and selective expression in specific tissues, cells, or systems are still almost completely uncharacterized with respect to biological function? In what ways do these functionally enigmatic genes differ from well-studied genes? To address these two questions, we devised a computational approach that defines so-called ignoromes. As proof of principle, we extracted and analyzed a large subset of genes with intense and selective expression in brain. We find that publications associated with this set are highly skewed--the top 5% of genes absorb 70% of the relevant literature. In contrast, approximately 20% of genes have essentially no neuroscience literature. Analysis of the ignorome over the past decade demonstrates that it is stubbornly persistent, and the rapid expansion of the neuroscience literature has not had the expected effect on numbers of these genes. Surprisingly, ignorome genes do not differ from well-studied genes in terms of connectivity in coexpression networks. Nor do they differ with respect to numbers of orthologs, paralogs, or protein domains. The major distinguishing characteristic between these sets of genes is date of discovery, early discovery being associated with greater research momentum--a genomic bandwagon effect. Finally we ask to what extent massive genomic, imaging, and phenotype data sets can be used to provide high-throughput functional annotation for an entire ignorome. In a majority of cases we have been able to extract and add significant information for these neglected genes. In several cases--ELMOD1, TMEM88B, and DZANK1--we have exploited sequence polymorphisms, large phenome data sets, and reverse genetic methods to evaluate the function of ignorome genes.
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