Predicting Abdominal Aortic Aneurysm Target Genes by Level-2 Protein-Protein Interaction

<div><p>Abdominal aortic aneurysm (AAA) is frequently lethal and has no effective pharmaceutical treatment, posing a great threat to human health. Previous bioinformatics studies of the mechanisms underlying AAA relied largely on the detection of direct protein-protein interactions (level-1 PPI) between the products of reported AAA-related genes. Thus, some proteins not suspected to be directly linked to previously reported genes of pivotal importance to AAA might have been missed. In this study, we constructed an indirect protein-protein interaction (level-2 PPI) network based on common interacting proteins encoded by known AAA-related genes and successfully predicted previously unreported AAA-related genes using this network. We used four methods to test and verify the performance of this level-2 PPI network: cross validation, human AAA mRNA chip array comparison, literature mining, and verification in a mouse CaPO₄ AAA model. We confirmed that the new level-2 PPI network is superior to the original level-1 PPI network and proved that the top 100 candidate genes predicted by the level-2 PPI network shared similar GO functions and KEGG pathways compared with positive genes.</p></div

The mRNA expression levels of top-ranking candidate genes in a mouse AAA model.

<p>(A) Representative morphology of NaCl- and CaPO₄-treated mice aortas. (B) The maximum diameter of abdominal aortas. (C) Representative Gomori staining, arrows showing elastin degradation. (D) Representative immunofluorescence (green) staining of IL-6 (left) and MCP-1 (right) in infrarenal aortas of mice treated with NaCl or CaPO₄. Nuclei were counterstained with Hoechst (blue). (E) mRNA was extracted from the aortas of both NaCl- and CaPO₄-treated mice, and the gene expression levels of APP, TP53, SPARC, MMP-14 and ITGB1 were assayed by Real-Time PCR. 18S was used as reference gene. Scale bar = 20 μm.</p

An illustration of the workflow: construction of the PPI network (A) and validation using various methods (B).

<p>An illustration of the workflow: construction of the PPI network (A) and validation using various methods (B).</p

The ratio of positive genes to candidate genes.

<p>Five genes were randomly deleted from the set of 34 positive genes, and the PPI network was reconstructed 100 times.</p

The coincidence ratios of the candidate genes to RNA array (A) and literature mining (B) for different top-level groups.

<p>The coincidence ratios of the candidate genes to RNA array (A) and literature mining (B) for different top-level groups.</p

Probability distributions of level-1 and level-2 neighbors for positive and candidate genes.

<p>Probability distributions of level-1 and level-2 neighbors for positive and candidate genes.</p

An example of indirect interaction with AAA disease genes.

<p>The PRKCD gene (labeled in yellow) is present as a root of trees in which the level-1 (labeled in blue) and level-2 neighbors (labeled in red) correspond to the level-1 and level-2 child nodes.</p