Dataset Information

Linking gene expression and functional network data in human heart failure.

ABSTRACT: BACKGROUND: Gene expression profiling and the analysis of protein-protein interaction (PPI) networks may support the identification of disease bio-markers and potential drug targets. Thus, a step forward in the development of systems approaches to medicine is the integrative analysis of these data sources in specific pathological conditions. We report such an integrative bioinformatics analysis in human heart failure (HF). A global PPI network in HF was assembled, which by itself represents a useful compendium of the current status of human HF-relevant interactions. This provided the basis for the analysis of interaction connectivity patterns in relation to a HF gene expression data set. RESULTS: Relationships between the significance of the differentiation of gene expression and connectivity degrees in the PPI network were established. In addition, relationships between gene co-expression and PPI network connectivity were analysed. Highly-connected proteins are not necessarily encoded by genes significantly differentially expressed. Genes that are not significantly differentially expressed may encode proteins that exhibit diverse network connectivity patterns. Furthermore, genes that were not defined as significantly differentially expressed may encode proteins with many interacting partners. Genes encoding network hubs may exhibit weak co-expression with the genes encoding their interacting protein partners. We also found that hubs and superhubs display a significant diversity of co-expression patterns in comparison to peripheral nodes. Gene Ontology (GO) analysis established that highly-connected proteins are likely to be engaged in higher level GO biological process terms, while low-connectivity proteins tend to be engaged in more specific disease-related processes. CONCLUSION: This investigation supports the hypothesis that the integrative analysis of differential gene expression and PPI network analysis may facilitate a better understanding of functional roles and the identification of potential drug targets in human heart failure.

SUBMITTER: Camargo A

PROVIDER: S-EPMC2147076 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Linking gene expression and functional network data in human heart failure.

Camargo Anyela A Azuaje Francisco F

PloS one 20071219 12

<h4>Background</h4>Gene expression profiling and the analysis of protein-protein interaction (PPI) networks may support the identification of disease bio-markers and potential drug targets. Thus, a step forward in the development of systems approaches to medicine is the integrative analysis of these data sources in specific pathological conditions. We report such an integrative bioinformatics analysis in human heart failure (HF). A global PPI network in HF was assembled, which by itself represen ...[more]

PMID: 18094754

Similar Datasets

Project description:BACKGROUND: Although dilated cardiomyopathy (DCM) is a leading cause of heart failure (HF), the mechanism underlying DCM is not well understood. Previously, it has been demonstrated that an integrative analysis of gene expression and protein-protein interaction (PPI) networks can provide insights into the molecular mechanisms of various diseases. In this study we develop a systems approach by linking public available gene expression data on ischemic dilated cardiomyopathy (ICM), a main pathological form of DCM, with data from a layered PPI network. We propose that the use of a layered PPI network, as opposed to a traditional PPI network, provides unique insights into the mechanism of DCM. METHODS: Four Cytoscape plugins including BionetBuilder, NetworkAnalyzer, Cerebral and GenePro were used to establish the layered PPI network, which was based upon validated subcellular protein localization data retrieved from the HRPD and Entrez Gene databases. The DAVID function annotation clustering tool was used for gene ontology (GO) analysis. RESULTS: The assembled layered PPI network was divided into four layers: extracellular, plasma membrane, cytoplasm and nucleus. The characteristics of the gene expression pattern of the four layers were compared. In the extracellular and plasma membrane layers, there were more proteins encoded by down-regulated genes than by up-regulated genes, but in the other two layers, the opposite trend was found. GO analysis established that proteins encoded by up-regulated genes, reflecting significantly over-represented biological processes, were mainly located in the nucleus and cytoplasm layers, while proteins encoded by down-regulated genes were mainly located in the extracellular and plasma membrane layers. The PPI network analysis revealed that the Janus family tyrosine kinase-signal transducer and activator of transcription (Jak-STAT) signaling pathway might play an important role in the development of ICM and could be exploited as a therapeutic target of ICM. In addition, glycogen synthase kinase 3 beta (GSK3B) may also be a potential candidate target, but more evidence is required. CONCLUSION: This study illustrated that by incorporating subcellular localization information into a PPI network based analysis, one can derive greater insights into the mechanisms underlying ICM.

Project description:BackgroundHeart failure (HF) with preserved ejection fraction (HFpEF) constitutes half of all HF but lacks effective therapy. Understanding of its myocardial biology remains limited because of a paucity of heart tissue molecular analysis.MethodsWe performed RNA sequencing on right ventricular septal endomyocardial biopsies prospectively obtained from patients meeting consensus criteria for HFpEF (n=41) contrasted with right ventricular septal tissue from patients with HF with reduced ejection fraction (HFrEF, n=30) and donor controls (n=24). Principal component analysis and hierarchical clustering tested for transcriptomic distinctiveness between groups, effect of comorbidities, and differential gene expression with pathway enrichment contrasted HF groups and donor controls. Within HFpEF, non-negative matrix factorization and weighted gene coexpression analysis identified molecular subgroups, and the resulting clusters were correlated with hemodynamic and clinical data.ResultsPatients with HFpEF were more often women (59%), African American (68%), obese (median body mass index 41), and hypertensive (98%), with clinical HF characterized by 65% New York Heart Association Class III or IV, nearly all on a loop diuretic, and 70% with a HF hospitalization in the previous year. Principal component analysis separated HFpEF from HFrEF and donor controls with minimal overlap, and this persisted after adjusting for primary comorbidities: body mass index, sex, age, diabetes, and renal function. Hierarchical clustering confirmed group separation. Nearly half the significantly altered genes in HFpEF versus donor controls (1882 up, 2593 down) changed in the same direction in HFrEF; however, 5745 genes were uniquely altered between HF groups. Compared with controls, uniquely upregulated genes in HFpEF were enriched in mitochondrial adenosine triphosphate synthesis/electron transport, pathways downregulated in HFrEF. HFpEF-specific downregulated genes engaged endoplasmic reticulum stress, autophagy, and angiogenesis. Body mass index differences largely accounted for HFpEF upregulated genes, whereas neither this nor broader comorbidity adjustment altered pathways enriched in downregulated genes. Non-negative matrix factorization identified 3 HFpEF transcriptomic subgroups with distinctive pathways and clinical correlates, including a group closest to HFrEF with higher mortality, and a mostly female group with smaller hearts and proinflammatory signaling. These groupings remained after sex adjustment. Weighted gene coexpression analysis yielded analogous gene clusters and clinical groupings.ConclusionsHFpEF exhibits distinctive broad transcriptomic signatures and molecular subgroupings with particular clinical features and outcomes. The data reveal new signaling targets to consider for precision therapeutics.

Project description:Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide. As a multifactorial syndrome with unpredictable clinical outcomes, identifying the common molecular underpinnings that drive HF pathogenesis remains a major focus of investigation. Disruption of cardiac gene expression has been shown to mediate a common final cascade of pathological hallmarks wherein the heart reactivates numerous developmental pathways. Although the central regulatory mechanisms that drive this cardiac transcriptional reprogramming remain unknown, epigenetic contributions are likely. In the current study, we examined whether the epigenome, specifically DNA methylation, is reprogrammed in HF to potentiate a pathological shift in cardiac gene expression. To accomplish this, we used paired-end whole genome bisulfite sequencing and next-generation RNA sequencing of left ventricle tissue obtained from seven patients with end-stage HF and three nonfailing donor hearts. We found that differential methylation was localized to promoter-associated cytosine-phosphate-guanine islands, which are established regulatory regions of downstream genes. Hypermethylated promoters were associated with genes involved in oxidative metabolism, whereas promoter hypomethylation enriched glycolytic pathways. Overexpression of plasmid-derived DNA methyltransferase 3A in vitro was sufficient to lower the expression of numerous oxidative metabolic genes in H9c2 rat cardiomyoblasts, further supporting the importance of epigenetic factors in the regulation of cardiac metabolism. Last, we identified binding-site competition via hypermethylation of the nuclear respiratory factor 1 (NRF1) motif, an established upstream regulator of mitochondrial biogenesis. These preliminary observations are the first to uncover an etiology-independent shift in cardiac DNA methylation that corresponds with altered metabolic gene expression in HF.NEW & NOTEWORTHY The failing heart undergoes profound metabolic changes because of alterations in cardiac gene expression, reactivating glycolytic genes and suppressing oxidative metabolic genes. In the current study, we discover that alterations to cardiac DNA methylation encode this fetal-like metabolic gene reprogramming. We also identify novel epigenetic interference of nuclear respiratory factor 1 via hypermethylation of its downstream promoter targets, further supporting a novel contribution of DNA methylation in the metabolic remodeling of heart failure.

Dataset Information

Linking gene expression and functional network data in human heart failure.

Publications

Linking gene expression and functional network data in human heart failure.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets