In Silico Analysis of Differential Gene Expression in Three Common Rat Models of Diastolic Dysfunction

Altara, Raffaele; Zouein, Fouad A.; Brandão, Rita D.; Bajestani, Saeed; Cataliotti, Alessandro; Booz, George W.

doi:10.3389/fcvm.2018.00011

Cited by 8 publications

(5 citation statements)

References 96 publications

(90 reference statements)

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“…to attenuate pressure-overload cardiac fibrosis. 27,28 The role of these predicted upstream regulators should therefore be investigated in focused future studies.…”

Section: I S C L a I M E R : T H E M A N U S C R I P T A N D I T S C ...mentioning

confidence: 99%

Cardiac Microvascular Endothelial Cells in Pressure Overload–Induced Heart Disease

Trenson

Hermans

Craps

et al. 2021

Circ: Heart Failure

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Background: Chronic pressure overload predisposes to heart failure, but the pathogenic role of microvascular endothelial cells (MiVEC) remains unknown. We characterized transcriptional, metabolic, and functional adaptation of cardiac MiVEC to pressure overload in mice and patients with aortic stenosis (AS). Methods: In Tie2-Gfp mice subjected to transverse aortic constriction or sham surgery, we performed RNA sequencing of isolated cardiac Gfp + -MiVEC and validated the signature in freshly isolated MiVEC from left ventricle outflow tract and right atrium of patients with AS. We next compared their angiogenic and metabolic profiles and finally correlated molecular and pathological signatures with clinical phenotypes of 42 patients with AS (50% women). Results: In mice, transverse aortic constriction induced progressive systolic dysfunction, fibrosis, and reduced microvascular density. After 10 weeks, 25 genes predominantly involved in matrix-regulation were >2-fold upregulated in isolated MiVEC. Increased transcript levels of Cartilage Intermediate Layer Protein ( Cilp ), Thrombospondin-4 , Adamtsl-2 , and Collagen1a1 were confirmed by quantitative reverse transcription polymerase chain reaction and recapitulated in left ventricle outflow tract-derived MiVEC of AS ( P <0.05 versus right atrium-MiVEC). Fatty acid oxidation increased >2-fold in left ventricle outflow tract-MiVEC, proline content by 130% (median, IQR, 58%–474%; P =0.008) and procollagen secretion by 85% (mean [95% CI, 16%–154%]; P <0.05 versus right atrium-MiVEC for all). The altered transcriptome in left ventricle outflow tract-MiVEC was associated with impaired 2-dimensional-vascular network formation and 3-dimensional-spheroid sprouting ( P <0.05 versus right atrium-MiVEC), profibrotic ultrastructural changes, and impaired diastolic left ventricle function, capillary density and functional status, especially in female AS. Conclusions: Pressure overload induces major transcriptional and metabolic adaptations in cardiac MiVEC resulting in excess interstitial fibrosis and impaired angiogenesis. Molecular rewiring of MiVEC is worse in women, compromises functional status, and identifies novel targets for intervention.

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“…to attenuate pressure-overload cardiac fibrosis. 27,28 The role of these predicted upstream regulators should therefore be investigated in focused future studies.…”

Section: I S C L a I M E R : T H E M A N U S C R I P T A N D I T S C ...mentioning

confidence: 99%

Cardiac Microvascular Endothelial Cells in Pressure Overload–Induced Heart Disease

Trenson

Hermans

Craps

et al. 2021

Circ: Heart Failure

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“…These authors used the same in silico method of results confirmation as we did in this study PLOS ONE [22]. In silico confirmation of biological function has found its way in gene association studies, nucleotide polymorphism detection, differential gene expression analysis and novel gene prediction [23][24][25]. However, there are some discrepancies between the Shannon entropy results and current results, though, with both entropy-based and DFT-based methods, we got logical results only from gene sequence data clustering.…”

Section: Plos Onementioning

confidence: 83%

A new gene tree algorithm employing DNA sequences of bovine genome using discrete Fourier transformation

et al. 2023

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Within the realms of human thoughts on nature, Fourier analysis is considered as one of the greatest ideas currently put forwarded. The Fourier transform shows that any periodic function can be rewritten as the sum of sinusoidal functions. Having a Fourier transform view on real-world problems like the DNA sequence of genes, would make things intuitively simple to understand in comparison with their initial formal domain view. In this study we used discrete Fourier transform (DFT) on DNA sequences of a set of genes in the bovine genome known to govern milk production, in order to develop a new gene clustering algorithm. The implementation of this algorithm is very user-friendly and requires only simple routine mathematical operations. By transforming the configuration of gene sequences into frequency domain, we sought to elucidate important features and reveal hidden gene properties. This is biologically appealing since no information is lost via this transformation and we are therefore not reducing the number of degrees of freedom. The results from different clustering methods were integrated using evidence accumulation algorithms to provide in insilico validation of our results. We propose using candidate gene sequences accompanied by other genes of biologically unknown function. These will then be assigned some degree of relevant annotation by using our proposed algorithm. Current knowledge in biological gene clustering investigation is also lacking, and so DFT-based methods will help shine a light on use of these algorithms for biological insight.

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“…The aim of this study is to elucidate the underlying genes by the bioinformatics method to identify DEGs in left ventricle cardiac tissue of patients with heart failure. With the development of bioinformatics, differential gene expression pro le analysis and data mining based on bioinformatics have been increasingly used to analyze the potential mechanism of disease [25][26][27]. Our study provides the basis for further research on the interaction and underlying functions between genes involved in heart failure.…”

Section: Million Inmentioning

confidence: 93%

Analysis of differential gene expression of heart failure based on Gene Expression Omnibus database

Yan

Wang

Hou

et al. 2020

Preprint

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Background To screen and identify key genes involved in heart failure and explore underlying molecular mechanisms. Methods The expression profile of GSE26887 was downloaded from Gene Expression Omnibus (GEO), which contained 24 samples, including 19 left ventricle cardiac tissue of heart failure and 5 controls. The differentially expressed genes (DEGs) were obtained and got further analysis by bioinformatics methods. The DEGs and volcano plot were acquired with the use of ‘lima’ package in ‘R’ software and heat map was drawn through the ‘heatmap’ package. Gene ontology (GO) and pathway analysis of DEGs were performed by means of Database for Annotation, Visualization and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) online analyses, respectively. The DEGs interaction and network map were constructed through Search Tool for the Retrieval of Interacting Genes (STRING ) database and Cystoscope(v3.6.0)software. Results The transcriptome analysis of left ventricle cardiac tissue showed that 236 genes were differentially expressed between heart failure and control, of which 124 were significantly upregulated (P < 0.01) and 101 genes downregulated (P < 0.01). GO analysis uncovered that DEGs were enriched in extracellular space, extracellular matrix, extracellular matrix organization, cell adhesion, proteinaceous extracellular matrix and heparin binding. Thus, the function of extracellular matrix is mainly affected. The KEGG pathway enrichment indicated that the DEGs were involved in eight pathways, of these pathways, ECM-receptor interaction, Drug metabolism-cytochrome P450 and Pathogenic Escherichia coli infection are dominant. Protein-protein interaction (PPI) revealed the interactions of 30 protein products encoded of DEGs. Of the 30 protein products, the critical gene, called Interleukin-6 (IL6), was identified with the use of Cystoscope software. Conclusion Extracellular matrix and IL6 play an important role in the development of heart failure. Functional annotation and pathway analysis of these main genes were identified, which provide the basis for insight into the underlying pathogenetic mechanisms and predicting therapeutic targets of heart failure.

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In Silico Analysis of Differential Gene Expression in Three Common Rat Models of Diastolic Dysfunction

Cited by 8 publications

References 96 publications

Cardiac Microvascular Endothelial Cells in Pressure Overload–Induced Heart Disease

Cardiac Microvascular Endothelial Cells in Pressure Overload–Induced Heart Disease

A new gene tree algorithm employing DNA sequences of bovine genome using discrete Fourier transformation

Analysis of differential gene expression of heart failure based on Gene Expression Omnibus database

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