Abstract

   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 CaPO[4] 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.

Introduction

   Abdominal aortic aneurysm (AAA) is characterized by permanent abdominal
   aortic dilation, with the maximum diameter of diseased aorta reaching
   1.5 times that of the adjacent aorta. Due to a lack of apparent signs
   and symptoms, most AAAs, when first diagnosed, are at risk of rupture
   and hemorrhage with very high fatality rate. This complex
   cardiovascular disease is modulated by multiple genes related to
   extracellular matrix degradation, oxidative stress, inflammation, and
   apoptosis [[31]1]. To date, the only clinical treatment of AAA is
   invasive surgical repair, and no proven drug therapy is available.
   Exploring novel anti-aneurismal candidate genes could shed light on
   promising strategies for AAA prevention and therapy.

   In general, functionally related genes are likely to cluster in the
   same networks [[32]2]. Analysis revealed that genes related to a
   particular disease tend to have higher and more synchronized expression
   and tend to interact among each other [[33]4] [[34]5, [35]6];
   approximately 70–80% of proteins share at least one function with an
   interacting partner [[36]7]. Furthermore, proteins of similar function
   and cellular location tend to cluster together. Approximately 63% of
   the interactions occur between proteins with common functions, and 76%
   occur between proteins in the same subcellular compartment [[37]8].

   Most protein-protein interaction (PPI)-based bioinformatics studies for
   predicting disease related genes are based on direct PPIs, although the
   systems and dimensions considered vary. A machine-learning approach
   [[38]9] to analyzing protein-protein interaction data has become
   popular and has been applied to diverse biological problems, including
   gene classification [[39]10], prediction of function, and cancer tissue
   classification. Some approaches used to predict disease genes are based
   on using combined PPI network topological features [[40]11, [41]12] to
   construct a combined classifier, or on analysis of protein sequences
   [[42]5]. The candidate genes predicted by these methods must have at
   least one direct interaction with a known disease gene. Thus, the scope
   of annotation for a candidate gene is limited by the annotation of its
   interacting partners [[43]13]. Therefore, previous research using
   level-1 PPI networks to predict candidate disease genes may omit genes
   without direct interactions with known disease genes but that share
   substantial functional similarities with level-2 neighbors [[44]13].

   Recently, it was shown that if two proteins do not interact directly
   but share more common interacting partners than two proteins chosen at
   random, these two proteins are likely to have close functional
   associations [[45]14]. This is referred to as indirect functional
   association. Through the use of indirect interaction data and
   topological weight, researchers are able to augment the protein-protein
   interaction network, thereby improving the precision of clusters
   predicted by existing clustering algorithms [[46]15]. Chua and
   colleagues reported the use of indirect protein-protein interactions
   between level-2 neighbors for the prediction of protein complexes
   [[47]15]. However, whether this method can be extended to the
   prediction of human disease gene candidates remains elusive.

   We propose a novel method to predict candidate AAA disease genes by
   assessing indirect protein interactions. By comparison with the
   original PPI network, we found that using a novel level-2 PPI network
   is superior to the original method in terms of both quantity and
   quality of the predicted candidate genes. Moreover, we verified our
   results in vivo in a CaPO[4]-induced aneurysm mouse model.

System and Methods

2.1 Data Sources and Preprocessing

   Genes defined as Abdominal Aortic Aneurysm (AAA) disease genes were
   compiled from the Online Mendelian Inheritance in Man database [[48]16]
   (OMIM, [49]http://www.ncbi.nlm.nih.gov/omim/) and Genetic Association
   Database [[50]17] (GAD, [51]http://geneticassociationdb.nih.gov/). PPI
   data from the Human Protein Reference Database [[52]18] (HPRD,
   [53]http://www.hprd.org/) were manually extracted from the literature
   by expert biologists who read, interpreted and analyzed the published
   data. To avoid any bias toward well studied genes [[54]19], we examined
   the PPI networks for all interactions detailed by the HPRD annotation
   ([55]Fig 1).

Fig 1. An illustration of the workflow: construction of the PPI network (A)
and validation using various methods (B).

   [56]Fig 1
   [57]Open in a new tab

2.2 Candidate Gene Score Counting Method

   The interaction number between each candidate gene (both level-1 and
   level-2) and a positive gene is designated as follows: node “A”
   indicates a positive gene, while nodes “B,” “C,” and “D” indicate
   level-1 candidate genes and nodes “E” and “F” indicate level-2
   candidate genes. The interaction number between node B, C or D and
   positive gene node A is 1; the interaction number between node E and
   positive gene node A is 1; and the interaction number between node F
   and positive gene node A is 3 ([58]S1 Fig).

2.3 Prediction of Candidate Genes

   We constructed both level-1 and level-2 PPI networks to predict AAA
   disease genes (candidate genes). In the level-1 PPI network, the direct
   neighbors of all positive disease genes were named level-1 candidate
   genes. In the level-2 PPI network, the indirect neighbors (proteins
   with common interaction partners) of all positive disease genes were
   named level-2 candidate genes.

2.4 Animal Preparation

   The animal experimental protocol was approved by the Institutional
   Animal Care and Use Committee (IACUC) of the Peking University Health
   Science Center (LA2015–142). All studies followed the guidelines of the
   Animal Care and Use Committee of Peking University. Appropriate
   analgesics were used on all animals to reduce surgical pain. Mice
   undergoing surgery to induce abdominal aortic aneurysm were
   anesthetized by intraperitoneal injection of pentobarbital (30–40
   mg/kg) according to IACUC recommendations. After surgery, the animals
   were moved to a dry area with warming pad and were monitored during
   recovery.

   Mice AAA was induced by CaPO[4] as previously described [[59]20]. For
   the experimental group (n = 7), the infrarenal aortas of C57BL/6J mice
   (male, 12 weeks, purchased from Vital River, Beijing, China) were
   isolated and wrapped with gauze presoaked in 0.5 mol/L CaCl[2] for 10
   minutes. Then, the CaCl[2]-treated gauze was substituted with gauze
   presoaked in PBS for another 5 minutes. The abdominal cavities were
   washed with 0.9% NaCl before suturing. For the sham operation group (n
   = 7), both CaCl[2] and PBS solution were substituted with 0.9% NaCl
   solution, and all the other operations were same as the experimental
   group. Aortas were collected 7 days later. A picture of the aortas
   together with a dividing ruler was taken, and the maximum diameter of
   the aortas was measured using Image Pro Plus 6.0 according to the
   ruler.

2.5 Morphology of Aortas

   Mice were killed, infrarenal aortas were perfused with PBS and 4%
   paraformaldehyde. Serial cryosections (7-μm thick, 300 μm apart) were
   analyzed by Gomori staining for elastin assessment.

2.6 Immunofluorescence Staining

   Frozen aortic sections were incubated with antibodies against IL-6
   (1:200, Abcam) and MCP-1 (1:50, Abcam), followed by secondary
   FITC-conjugated goat anti-mouse IgG (1:300) (Rockland Inc.,
   Gilbertsville, Pennsylvania). Nuclei were counterstained with Hoechst
   33342. The negative control was the primary antibody replaced with IgG.
   The fluorescence signal was monitored by confocal laser scanning
   microscopy (Leica, Germany).

2.7 Real-Time PCR

   Real-time PCR amplification involved the use of an Mx3000 Multiplex
   Quantitative PCR System (Stratagene Corp, La Jolla, California) and
   SYBR Green I reagent. Products were normalized to an internal β-actin
   control. The primer sequences used for real-time PCR are provided in
   [60]S1 Table.

2.8 Analysis of Functional Coherence

   Cytoscape software [[61]21] was used to visualize complex networks and
   to integrate attribute data. BINGO [[62]22] was used to evaluate which
   Gene Ontology (GO, [63]http://www.geneontology.org/) terms were
   enriched. We also tested whether candidate genes shared functions with
   positive disease genes to validate the associations between Top 100
   level-2 candidate genes and the incidence of disease.

   Web-based Gene Set Analysis Toolkit [[64]2] (WebGestalt,
   [65]http://bioinfo.vanderbilt.edu/webgestalt/) is an extensively used
   tool for functional enrichment analysis. We used it to compare level-2
   candidate genes and positive disease genes with genes in KEGG pathways
   to identify significant pathways in which level-2 candidate genes and
   positive disease genes took part. A significance level of 0.01 was
   selected as the cutoff for selecting enriched pathway categories
   [[66]11].

2.9 Statistical Analysis

   All data are presented as the mean±standard error of the mean (SEM).
   Statistical analysis was performed with Student’s t-test for the
   CaPO[4]AAA model. Cross validation was tested using the paired samples
   t-test. P<0.05 was considered statistically significant.

Results

   In this article, we established a novel method for generating level-2
   PPI networks to identify candidate disease genes by measuring the
   number of shared interaction partners of each gene in a PPI network.
   This method is detailed in [67]Fig 1.

3.1 Level-1 and Level-2 PPI Network Construction

   AAA-related genes were compiled from the OMIM and GAD databases.
   Together, 56 positive genes were identified (33 genes from OMIM and 37
   genes from GAD; 14 genes were found in both databases). These genes
   were entered into the HPRD database, and level-1 neighbors known to
   directly interact with the genes were identified. These level-1
   neighbors were in turn entered into the HPRD database, and their
   level-1 neighbors—that is, the level-2 neighbors of disease genes—were
   found ([68]Fig 1). In the HPRD database, 959 level-1 neighbors with
   1,312 direct interactions and 5,730 level-2 neighbors with 42,615
   indirect interactions were identified ([69]Table 1). Level-1 neighbors,
   together with the disease genes and direct interactions, comprise the
   level-1 PPI network; level-2 neighbors, together with the disease genes
   and indirect interactions, comprise the level-2 PPI network. Of the 56
   positive genes in the level-1 PPI network, only 34 had a direct
   interaction with another positive gene, accounting for 60.71% of all
   positive genes. Of the 56 positive genes in the level-2 PPI network, 52
   had an indirect interaction with another positive gene, accounting for
   92.86% of all positive genes.

Table 1. Main Features of Level-1 and Level-2 PPI Network.

   Level-1 Level-2
   Positive Gene Interactions 34 52
   Positive Gene Interaction Percentage (Positive Genes with Interactions/
   Total Positive Genes) 60.71% 92.86%
   Candidate Genes 959 5730
   Protein-Protein Interactions 1312 42615
   [70]Open in a new tab

3.2 AAA-Related Gene Prediction and Topological Features of the PPI Network

   Genes that encode proteins that interact directly or indirectly with
   the proteins encoded by known AAA-related genes were referred to as
   candidate genes. The number of interactions between every candidate
   gene and all known AAA related genes was calculated, and the
   interaction score of every candidate gene was then obtained. Based on
   the probability distribution, we found that in both level-1 and level-2
   PPI networks, positive genes possessed much higher interaction scores
   with known AAA-related genes than did candidate genes ([71]Fig 2). More
   precisely, most candidate genes had more than 3 direct interactions or
   less than 10 indirect interactions with known AAA-related genes;
   positive genes and candidate genes showed significant differences in
   distribution when the direct interaction number was greater than 4, or
   when the indirect interaction number was greater than 10. Thus, level-1
   candidate genes with greater than 4 interactions are very likely to be
   hub nodes. The level-2 candidate genes and AAA disease genes may share
   physical or biochemical characteristics that allow them to bind to the
   hub nodes [[72]3]. The more interaction partners they share, the higher
   the probability that they share a common function. In the level-1 PPI
   network, 14.7% of the positive genes shared at least 4 direct
   interactions with an AAA-related gene, an 8-fold increase compared with
   the candidate genes (1.9%) ([73]Fig 2A). In the level-2 PPI network,
   17.3% of the positive genes shared at least 50 indirect interactions
   with an AAA-related gene, an 11-fold increase compared with the
   candidate genes (1.6%). As seen in [74]Fig 2B, most positive genes
   shared over 50 indirect interactions with AAA disease genes. Therefore,
   level-2 candidate genes with more than 50 indirect interactions were
   defined as prior candidate genes.

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

   [75]Fig 2
   [76]Open in a new tab

   One of the prior candidate genes with over 50 indirect interactions and
   0 direct interactions (i.e., not predicted by the level-1 PPI network)
   with AAA disease genes was chosen for further study. As shown in
   [77]Fig 3, protein kinase C delta (PRKCD) was chosen as the center of a
   protein network. In this network, PRKCD had no direct link (i.e.,
   interaction) with AAA disease genes (shown as red child nodes), but had
   common interacting partners (level-1 neighbors, shown as blue child
   nodes) with AAA-positive genes. Specifically, level-1 child nodes, such
   as the MAPK1, MAPK3, PAK1 and SHC1 genes, were likely to be hub nodes
   through which PRKCD and known AAA-related genes indirectly interact. In
   a published human AAA RNA chip array dataset (Reference Series:
   [78]GSE7084, [79]GSE47472) [[80]23], PRKCD expression was significantly
   increased in AAA cases compared with controls. In addition, PRKCD
   expression was markedly upregulated in human AAA vessel wall and was
   shown to mediate VSMC MCP-1 expression [[81]24], which could contribute
   to the vascular inflammatory process. Furthermore, recent studies in a
   mouse model of AAA have shown that PRKCD is an important signaling
   molecule in VSMC apoptosis and inflammation [[82]25].

Fig 3. An example of indirect interaction with AAA disease genes.

   [83]Fig 3
   [84]Open in a new tab

   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.

3.3 Cross-Validation

   A holdout validation test was used to evaluate the performance of both
   level-1 and level-2 PPI networks and to select the optimal PPI network.
   Five genes were randomly deleted from the list of 34 positive genes
   (i.e., positive genes shared by level-1 and level-2 networks), and both
   PPI networks were reconstructed. This process was repeated 100 times.
   The average scores for the positive and candidate gene groups were
   obtained each time and the ratios of the two average scores were
   calculated. In turn, the ratio after 100 randomizations was determined
   ([85]Fig 4). According to this analysis, the ratio for the level-2
   group was significantly higher than that for the level-1 group, making
   it more suitable for screening candidate genes related to AAA.

Fig 4. The ratio of positive genes to candidate genes.

   [86]Fig 4
   [87]Open in a new tab

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

3.4 Comparison with Existing AAA Gene Data

   Human AAA RNA array data(Reference Series: [88]GSE7084, [89]GSE47472)
   obtained via Illumina and Affymetrix microarray platforms was used to
   generate global gene expression profiles for both aneurismal (AAA) and
   non-aneurismal abdominal aortas. The genes that were significantly
   differentially expressed between cases and controls were used in our
   analysis. Identified candidate genes from both level-1and level-2 PPI
   networks were matched with the RNA array data, and the coincidence
   ratios of the candidate genes to the RNA array data in different
   top-level groups were calculated. There were 3,274 differentially
   expressed genes, including 235 level-1 candidate genes (6.3%) and 1,240
   level-2 candidate genes (33.1%). The top 14 genes corresponded to
   level-1 genes scoring greater than or equal to 5 and level-2 genes
   scoring greater than or equal to 100, with overlaps of 28.6% and 42.9%
   with the AAA chip array data, respectively. The top 15–76 genes
   corresponded to level-1 genes scoring 3–4 and level-2 genes scoring
   82–98, with overlaps of 30.8% and 26.9% with the AAA chip array data,
   respectively. The top 77–965 genes corresponded to level-1 genes
   scoring 1–2 and level-2 genes scoring 12–31, with respective overlaps
   of 24.2% and 26.5% with the AAA chip array data. The remaining level-2
   candidate genes scoring below 12 accounted for 26% of the genes. The
   upper range was segmented according to integer score. It was found that
   the coincidence ratio of level-2 candidate genes to the microarray data
   was significantly higher than that of level-1 candidate genes to the
   microarray data, especially among the top 14 ([90]Fig 5A).

Fig 5. The coincidence ratios of the candidate genes to RNA array (A) and
literature mining (B) for different top-level groups.

   [91]Fig 5
   [92]Open in a new tab

   In addition, literature available in PubMed was reviewed to determine
   whether candidate genes predicted by the two PPI networks were related
   to AAA. The candidate genes in the level-1 group scoring greater than 4
   and those in the level-2 group scoring greater than 50 were selected.
   According to the literature, predicted candidate genes in the level-2
   group had a higher coincidence ratio with published studies though the
   top 10 to top 75 groups, as shown in [93]Fig 5B. The top 14 genes
   corresponded to level-1 genes scoring greater than or equal to 5 and
   level-2 genes scoring greater than or equal to 100, with respective
   overlaps 28.6% and 42.9% compared to the AAA chip array data. The top
   15–32 genes corresponded to level-1 genes scoring 4 and level-2 genes
   scoring 82–98, with respective overlaps of 28.6% and 42.9% compared to
   the AAA chip array data. The top 33–76 genes corresponded to level-1
   genes scoring 3 and level-2 genes scoring 54–81, with respective
   overlaps of 28.6% and 42.9% compared to the AAA chip array data.

3.5 Analysis of Functional Coherence

   Functional coherence between candidate genes and known disease genes
   was examined to verify associations between candidate genes and AAA.
   The KEGG database (WebGestalt,
   [94]http://bioinfo.vanderbilt.edu/webgestalt/), which contains all
   signaling pathways in a Web-based Gene Set Analysis Toolkit platform,
   was utilized. The level-2 candidate genes and known AAA-related genes
   were entered, and a pathway enrichment analysis for both candidate
   genes and positive genes was performed. Eighty-five signaling pathways
   with enriched candidate AAA genes were obtained, with some—such as the
   ErbB signaling pathway, Focal Adhesion, Neurotrophin signaling pathway,
   TGF-β signaling pathway, MAPK signaling pathway, and Adherens Junction,
   B Cell Receptor signaling pathway ([95]S3 Table)–showing marked
   enrichment.

   Literature reports indicate that the TGF-β signaling pathway is
   involved in AAA. In animal models of AAA, TGF-β decreased aortic
   inflammatory cell infiltration, extracellular matrix degradation, and
   vascular smooth muscle cell apoptosis, thereby inhibiting AAA
   formation, progression and rupture. Active TGF-β binds to TGF-β
   Receptor II [[96]25], which recruits and phosphorylates TGF-β Receptor
   I [[97]26, [98]27]. The phosphorylated TGF-β Receptor I then activates
   receptor-regulated Smads [[99]28], including Smad2 (a level-2 candidate
   gene) and Smad3 (a positive disease gene). In addition, inhibitory
   Smads (I-Smads) were identified [[100]3]. One of these inhibitory
   Smads, Smad 7, negatively regulated TGF-β/ Smad signaling by preventing
   the activation of Smad2 and Smad3 [[101]28, [102]29]. In our pathway
   enrichment analysis, Smad7 was identified as a level-2 candidate gene
   ([103]S2 Fig).

3.6 Candidate Gene Expression in a CaPO[4]-Induced AAA Model

   Five of the candidate genes at the top of the level-2 PPI network list
   that were unreported in the literature were tested and validated in a
   CaPO[4]-induced mouse abdominal aortic aneurysm model [[104]30]. In the
   CaPO[4]-treated group, the infrarenal aorta was obviously expanded,
   while the aortas of NaCl-treated mice were morphologically normal
   ([105]Fig 6A). The maximum diameter of the control (0.9% NaCl) group
   was 0.885± 0.077 mm, and that of the CaPO[4]-treated group was 1.391±
   0.151 mm (p< 0.05) ([106]Fig 6B). The elastic lamina disruption of the
   CaPO[4]-treated group was severer than that of the NaCl group as shown
   by Gomori staining ([107]Fig 6C). Immunofluorescence staining showed
   that IL-6 and MCP-1 production in the aorta were also significantly
   increased in the CaPO[4]-treated group ([108]Fig 6D). These data
   indicated the successful induction of infrarenal abdominal aortic
   aneurysm by CaPO[4] treatment.

Fig 6. The mRNA expression levels of top-ranking candidate genes in a mouse
AAA model.

   [109]Fig 6
   [110]Open in a new tab

   (A) Representative morphology of NaCl- and CaPO[4]-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 CaPO[4]. Nuclei were
   counterstained with Hoechst (blue). (E) mRNA was extracted from the
   aortas of both NaCl- and CaPO[4]-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.

   RNA was extracted from the abdominal aortic tissue of both groups and
   was reverse transcribed into cDNA. Five of the candidate genes
   predicted in the level-2 PPI network were verified using Real-Time PCR.
   Compared with the NaCl-treated group, CaPO[4]-treated aortas contained
   reduced levels of Osteonectin (SPARC), Matrix Metalloproteinase-14
   (MMP-14) and Integrin beta 1 (ITGB1) mRNA expression, while Amyloid
   Precursor Protein (APP) and tumor protein p53 (TP53) showed no
   significant differences ([111]Fig 6E).

Discussion

   Abdominal aortic aneurysm is a complex cardiovascular disease in which
   multiple genes are involved [[112]31, [113]32]. In this study, we
   created a protein interaction database to predict AAA-related genes. We
   constructed a level-2 PPI network according to the interaction partners
   shared by positive genes and candidate genes, and successfully
   predicted a large number of undiscovered AA- related genes.

   In recent years, the amount of available protein interaction data has
   markedly increased, and many protein interaction databases have
   emerged. Using various disease gene prediction methods, it has been
   found that even when fixed position candidate strategies or gene chip
   arrays are utilized, protein interaction networks yield additional
   candidate genes in a convenient and accurate manner [[114]33]. Genes
   and their interaction partners are likely to share similar functions,
   and their reciprocal links are often closely related to certain
   phenotypes or diseases [[115]34, [116]35]. Goh [[117]3] and colleagues
   reported that proteins responsible for the same disease are inclined to
   cluster in a PPI network and that changes in neighbors of the disease
   genes are likely to contribute to the same or a similar disease.
   According to Josson [[118]36] et al., the interaction probability
   between disease genes is much higher than that between non-disease
   genes, and disease genes are more prone to gather in clusters.

   Direct protein-protein interaction (level-1 PPI) networks were widely
   used to predict the biological functions of proteins and disease
   related genes, especially in cancer and diabetes. However, at least one
   direct interaction with a known disease gene is required in a level-1
   PPI network. Thus, genes that do not interact directly with known
   disease genes but share common interacting partners with those of a
   positive gene could be neglected.

   In this study, we constructed a level-2 PPI network based on the number
   of shared interacting partners of known disease genes, then used it to
   predict candidate genes for abdominal aortic aneurysm (AAA). We found
   that most AAA candidate genes are likely to have the following
   characteristics: i) they tend to be hub nodes or to be bound to hub
   nodes in the network, indicating a greater link to disease genes; ii)
   they have a great number of indirect interactions with disease genes;
   iii) they have similar biological functions with known AAA genes
   according to GO functional categorization ([119]S3 Fig); and iv) they
   often take part in common signaling pathways with known AAA related
   genes. According to our analysis, our network of 56 positive genes
   contains 959 level-1 neighbors and 5,730 level-2 neighbors, and
   previously identified candidate genes tended to be hub nodes. Our
   functional enrichment analysis and experimental validation confirmed
   this inference. In our CaPO[4] induced mouse AAA model, 3 (SPARC,
   MMP-14 and ITGB1) of the 5 chosen prior candidate genes were
   significantly down-regulated in the AAA model compared with controls,
   and the other 2 genes (APP and TP53) tended to decrease.

   Recently, some research on SPARC in intracranial aneurysm showed
   controversial results of SPARC expression in intracranial aneurysm
   [[120]37, [121]38, [122]39]. However, we found for the first time that
   SPARC decreased in CaPO[4] treated mice abdominal aorta.

   For MMP-14, the reports are also contentious. Stéphanie Michineau
   et.al. conducted a CaCl[2] model (14 days) to induce mice AAA, and
   reported that MMP-14 increased [[123]40]. But in Hazem Abdul-Hussien's
   study, using tissue from patients, they found that a modest augment of
   MMP-14 was observed only in ruptured aneurysm. While in growing
   (unruptured) aneurysm, MMP-14 mRNA level was the same as that of normal
   controls [[124]41]. In our study, the mice were sacrificed 7 days after
   CaPO[4] treatment, and the aortas remained unruptured. We found that
   MMP-14 was down regulated in CaPO[4] treated group 7 days after
   surgery. These discrepancies could probably due to experimental
   conditions like various animal model, CaCl[2] incubation time, the
   induction of PBS, days after surgery, animal age etc.

   Some previously reported genes that influence AAA, such as Epidermal
   Growth Factor Receptor (EGFR) [[125]42], Smad family member 1 (Smad1)
   [[126]43], Estrogen Receptor (ESR2) [[127]44], and STAT family member 3
   (STAT3) [[128]45], were successfully predicted by our level-2 PPI
   network, but not by the level-1 PPI network. These reported genes were
   not positive in our network because they were identified in animals,
   whereas only human genome-wide association study (GWAS) reports were
   used in the construction of our networks.

   Two difficulties remain in the prediction of disease genes based on PPI
   networks. One is the current inadequacy of PPI databases; approximately
   one-third are based on computer prediction, creating concerns regarding
   the reliability of the data. The other difficulty is that network
   topological features are difficult to quantitate. For example, a
   protein with a high degree might be a hub node, but it might also be a
   well-studied protein of lesser importance.

   Further investigation of prior candidate genes and their interactions
   with positive genes may shed light on AAA nosogenesis and its
   underlying mechanisms. Future work with multiple AAA models and
   experimental methods is necessary to ascertain whether candidate genes
   are truly AAA-related and how they play a role in the disease.

Conclusions

   We constructed a level-2 PPI network based on indirect (level-2) PPI
   neighbors with common interaction partners of known disease genes. The
   more common interaction partners a gene possessed, the higher the
   chance that the two proteins would share certain common functions.
   Select level-2 candidate genes were screened, and those with more than
   50 indirect interactions with positive disease genes were referred to
   as prior candidate genes. These prior candidate genes were found to
   share similar GO functions and KEGG pathways with positive disease
   genes. Thus, predicting disease-related genes using a level-2 PPI
   network based on indirect protein-protein interactions can serve as a
   guide for researchers to discover additional novel disease-related
   genes.

Supporting Information

   S1 Fig. Candidate gene score counting method.

   (EPS)
   [129]Click here for additional data file.^ (615.1KB, eps)
   S2 Fig. The detailed interaction of candidate disease genes in the KEGG
   pathway and the TGF-beta signaling pathway.

   Blue nodes represent candidate genes and red nodes represent AAA
   disease genes.

   (EPS)
   [130]Click here for additional data file.^ (1.1MB, eps)
   S3 Fig. GO functional annotation of candidate disease genes.

   The red nodes are significantly overrepresented by candidate disease
   genes.

   (EPS)
   [131]Click here for additional data file.^ (1.8MB, eps)
   S1 Table. Primer sequences used for Real-Time PCR.

   (XLS)
   [132]Click here for additional data file.^ (22.5KB, xls)
   S2 Table. List of candidate disease genes.

   (XLS)
   [133]Click here for additional data file.^ (399.5KB, xls)
   S3 Table. List of pathways enriched for level-2 candidate disease
   genes.

   (XLS)
   [134]Click here for additional data file.^ (41.5KB, xls)

Data Availability

   All relevant data are within the paper and its Supporting Information
   files.

Funding Statement

   This work was supported by funding from the National Program on Key
   Basic Research Projects (973 Program) (2010CB912504, 2012CB518002); the
   National Natural Science Foundation of the P. R. China (81121061,
   91339000); National Science Fund for distinguished Young Scholars
   (81225002); International Cooperation and Exchanges NSFC (81220108004);
   and the “111” Project of Chinese Ministry of Education (No. B07001).
   All the fundings were received by WK. [135]http://www.most.gov.cn/,
   [136]http://www.moe.gov.cn/, [137]http://www.nsfc.gov.cn/. The funders
   had no role in study design, data collection and analysis, decision to
   publish, or preparation of the manuscript.

References