142 research outputs found
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Bone Morphogenetic Proteinâ2 Decreases MicroRNAâ30b and MicroRNAâ30c to Promote Vascular Smooth Muscle Cell Calcification
Background: Vascular calcification resembles bone formation and involves vascular smooth muscle cell (SMC) transition to an osteoblastâlike phenotype to express Runx2, a master osteoblast transcription factor. One possible mechanism by which Runx2 protein expression is induced is downregulation of inhibitory microRNAs (miR). Methods and Results: Human coronary artery SMCs (CASMCs) treated with bone morphogenetic proteinâ2 (BMPâ2; 100 ng/mL) demonstrated a 1.7âfold (P<0.02) increase in Runx2 protein expression at 24 hours. A miR microarray and target prediction database analysis independently identified miRâ30b and miRâ30c (miRâ30bâc) as miRs that regulate Runx2 expression. Realâtimeâpolymerase chain reaction confirmed that BMPâ2 decreased miRâ30b and miRâ30c expression. A luciferase reporter assay verified that both miRâ30b and miRâ30c bind to the 3âČâuntranslated region of Runx2 mRNA to regulate its expression. CASMCs transfected with antagomirs to downregulate miRâ30bâc demonstrated significantly increased Runx2, intracellular calcium deposition, and mineralization. Conversely, forced expression of miRâ30bâc by transfection with preâmiRâ30bâc prevented the increase in Runx2 expression and mineralization of SMCs. Calcified human coronary arteries demonstrated higher levels of BMPâ2 and lower levels of miRâ30b than did noncalcified donor coronary arteries. Conclusions: BMPâ2 downregulates miRâ30b and miRâ30c to increase Runx2 expression in CASMCs and promote mineralization. Strategies that modulate expression of miRâ30b and miRâ30c may influence vascular calcification
Lipoprotein (a) accumulates intracellularly in coronary atherectomy specimens obtained from primary and restenotic lesions after PTCA
Association Between Circulating CD4+ T Cell Methylation Signatures of Network-Oriented SOCS3 Gene and Hemodynamics in Patients Suffering Pulmonary Arterial Hypertension
Pathogenic DNA methylation changes may be involved in pulmonary arterial hypertension (PAH) onset and its progression, but there is no data on potential associations with patient-derived hemodynamic parameters. The reduced representation bisulfite sequencing (RRBS) platform identified N= 631 differentially methylated CpG sites which annotated to N= 408 genes (DMGs) in circulating CD4(+) T cells isolated from PAH patients vs. healthy controls (CTRLs). A promoter-restricted network analysis established the PAH subnetwork that included 5 hub DMGs (SOCS3, GNAS, ITGAL, NCOR2, NFIC) and 5 non-hub DMGs (NR4A2, GRM2, PGK1, STMN1, LIMS2). The functional analysis revealed that the SOCS3 gene was the most recurrent among the top ten significant pathways enriching the PAH subnetwork, including the growth hormone receptor and the interleukin-6 signaling. Correlation analysis showed that the promoter methylation levels of each network-oriented DMG were associated individually with hemodynamic parameters. In particular, SOCS3 hypomethylation was negatively associated with right atrial pressure (RAP) and positively associated with cardiac index (CI) (vertical bar r vertical bar >= 0.6). A significant upregulation of the SOCS3, ITGAL, NFIC, NCOR2, and PGK1 mRNA levels (qRT-PCR) in peripheral blood mononuclear cells from PAH patients vs. CTRLs was found (P <= 0.05). By immunoblotting, a significant upregulation of the SOCS3 protein was confirmed in PAH patients vs. CTRLs (P < 0.01). This is the first network-oriented study which integrates circulating CD4(+) T cell DNA methylation signatures, hemodynamic parameters, and validation experiments in PAH patients at first diagnosis or early follow-up. Our data suggests that SOCS3 gene might be involved in PAH pathogenesis and serve as potential prognostic biomarker
Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD
Background: The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes. Results: We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches. Conclusion: Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms
Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside
Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine
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An integrated clinical program and crowdsourcing strategy for genomic sequencing and Mendelian disease gene discovery.
Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs
MicroRNA Dysregulation in Pulmonary Arteries from COPD: Relationships with Vascular Remodeling
Pulmonary vascular remodeling is an angiogenic-related process involving changes in smooth muscle cell (SMC) homeostasis, which is frequently observed in chronic obstructive pulmonary disease (COPD). MicroRNAs (miRNAs) are small, noncoding RNAs that regulate mRNA expression levels of many genes, leading to the manifestation of cell identity and specific cellular phenotypes. Here, we evaluate the miRNA expression profiles of pulmonary arteries (PAs) of patients with COPD and its relationship with the regulation of SMC phenotypic change. miRNA expression profiles from PAs of 12 patients with COPD, 9 smokers with normal lung function (SK), and 7 nonsmokers (NS) were analyzed using TaqMan Low-Density Arrays. In patients with COPD, expression levels of miR-98, miR-139-5p, miR-146b-5p, and miR-451 were upregulated, as compared with NS. In contrast, miR-197, miR-204, miR-485-3p, and miR-627 were downregulated. miRNA-197 expression correlated with both airflow obstruction and PA intimal enlargement. In an in vitro model of SMC differentiation, miR-197 expression was associated with an SMC contractile phenotype. miR-197 inhibition blocked the acquisition of contractile markers in SMCs and promoted a proliferative/migratory phenotype measured by both cell cycle analysis and wound-healing assay. Using luciferase assays, Western blot, and quantitative PCR, we confirmed that miR-197 targets the transcription factor E2F1. In PAs from patients with COPD, levels of E2F1 were increased as compared with NS. In PAs of patients with COPD, remodeling of the vessel wall is associated with downregulation of miR-197, which regulates SMC phenotype. The effect of miR-197 on PAs might be mediated, at least in part, by the key proproliferative factor, E2F1
Selenoprotein gene nomenclature
The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4 and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine-R-sulfoxide reductase 1) and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15 kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV) and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates
Recent Advances in Systems and Network Medicine:Meeting Report from the First International Conference in Systems and Network Medicine
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