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Unravelling the Pathogenetic Mechanisms in Congenital Aortopathies: Need for an Integrative Translational Approach.

ABSTRACT: Congenital heart disease (CHD)-associated aortopathy is a very heterogeneous entity with a wide spectrum of clinical presentations. The pathogenesis of aortopathy is still incompletely understood, and, therefore, the best prevention and management strategy is currently unknown. The most common entity of CHD-associated aortopathies is bicuspid aortic valve (BAV)-associated aortic disease (so called bicuspid aortopathy) that is found in 50%-60% of BAV individuals. BAV aortopathy has been reported in association with an increased risk of aortic events, especially aortic dissection and sudden cardiac death. Risk stratification of adverse aortic events is still very rudimentary and considers only the maximal aortic diameter, which makes it unsuitable for an individual risk prediction. This introductory Editorial highlights the unmet clinical need for more integrative and translational research to unravel pathogenetic pathways in the development of CHD-associated aortopathies, integrating recently identified genetic lesions and knowledge on circulating biomarkers and microstructural changes in the diseased aorta.

SUBMITTER: Girdauskas E

PROVIDER: S-EPMC7019613 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Unravelling the Pathogenetic Mechanisms in Congenital Aortopathies: Need for an Integrative Translational Approach.

Girdauskas Evaldas E Kaemmerer Harald H von Kodolitsch Yskert Y

Journal of clinical medicine 20200111 1

Congenital heart disease (CHD)-associated aortopathy is a very heterogeneous entity with a wide spectrum of clinical presentations. The pathogenesis of aortopathy is still incompletely understood, and, therefore, the best prevention and management strategy is currently unknown. The most common entity of CHD-associated aortopathies is bicuspid aortic valve (BAV)-associated aortic disease (so called bicuspid aortopathy) that is found in 50%-60% of BAV individuals. BAV aortopathy has been reported ...[more]

PMID: 31940858

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Project description:With mounting availability of genomic and phenotypic databases, data integration and mining become increasingly challenging. While efforts have been put forward to analyze prokaryotic phenotypes, current computational technologies either lack high throughput capacity for genomic scale analysis, or are limited in their capability to integrate and mine data across different scales of biology. Consequently, simultaneous analysis of associations among genomes, phenotypes, and gene functions is prohibited. Here, we developed a high throughput computational approach, and demonstrated for the first time the feasibility of integrating large quantities of prokaryotic phenotypes along with genomic datasets for mining across multiple scales of biology (protein domains, pathways, molecular functions, and cellular processes). Applying this method over 59 fully sequenced prokaryotic species, we identified genetic basis and molecular mechanisms underlying the phenotypes in bacteria. We identified 3,711 significant correlations between 1,499 distinct Pfam and 63 phenotypes, with 2,650 correlations and 1,061 anti-correlations. Manual evaluation of a random sample of these significant correlations showed a minimal precision of 30% (95% confidence interval: 20%-42%; n = 50). We stratified the most significant 478 predictions and subjected 100 to manual evaluation, of which 60 were corroborated in the literature. We furthermore unveiled 10 significant correlations between phenotypes and KEGG pathways, eight of which were corroborated in the evaluation, and 309 significant correlations between phenotypes and 166 GO concepts evaluated using a random sample (minimal precision = 72%; 95% confidence interval: 60%-80%; n = 50). Additionally, we conducted a novel large-scale phenomic visualization analysis to provide insight into the modular nature of common molecular mechanisms spanning multiple biological scales and reused by related phenotypes (metaphenotypes). We propose that this method elucidates which classes of molecular mechanisms are associated with phenotypes or metaphenotypes and holds promise in facilitating a computable systems biology approach to genomic and biomedical research.

Dataset Information

Unravelling the Pathogenetic Mechanisms in Congenital Aortopathies: Need for an Integrative Translational Approach.

Publications

Unravelling the Pathogenetic Mechanisms in Congenital Aortopathies: Need for an Integrative Translational Approach.

Similar Datasets

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