Network Biology Analysis of the Human Abdominal Aortic Aneurysm Plasma Proteome

Abstract

Objective: Abdominal aortic aneurysm (AAA) affects up to 9% of adults, and the incidence of asymptomatic and ruptured AAA currently accounts for 1-2% of male deaths. A number of studies have applied classical proteomics methodology to identify plasma biomarkers of AAA.

The use of a systems biology protein-protein interaction network analysis on proteomics results can reveal novel mechanisms.

Methods: We performed a bioinformatics analysis on a compiled set of proteins previously identified in multiple proteomics studies. Results from a total of nine papers were identified and included in the analysis. The 64 proteins related to AAA were first analyzed by over-representation analysis (ORA) using Webgestalt. Afterwards, the same input list was submitted to BioProfiling. This portal used statistical methodology for the network based interpretation of the protein list (Rspider, PPIspider). The networks obtained by both Reactome and PPI analysis were further analyzed by ORA using the JEPETTO application in the Cytoscape environment.

Results: This analysis revealed a strong over-representation for proteins related not only to blood clotting but also to cellular mediated response, such as cell adhesion and cytokine activation of platelets and white blood cells.

Conclusions: We used a network strategy to generate statistically valid novel hypotheses about biological mechanisms related to AAA, which may be useful in providing new insights into the understanding of the pathogenesis of AAA.

Keywords: Abdominal aortic aneurysm; Proteomics; Network analysis

Introduction

Abdominal aortic aneurysm (AAA) affects up to 9% of adults older than 65 years of age [1]. The mortality rate for patients with ruptured AAA approaches 90%, with 40-50% of deaths occurring before the patient reaches the hospital. Asymptomatic AAA is easily detectable with abdominal ultrasound, and screening programs are now considered a reasonable practice option since surgical repair is restricted to AAA >5.5 cm at centers with acceptable operative mortality [2-6]. Identifying markers other than AAA size that would predict a risk of rupture is the focus of current investigation. For AAA, one significant limitation relates to the fact that many biomarkers are not disease specific; indeed most of them are also markers for atherosclerosis [7]. Serum Elastin Peptide holds promise to be an improved biomarker to predict AAA expansion and rupture [8-10] because it positively correlates between its plasma level and the AAA growth rate [10]. The extent of aortic dilation and vessel tortuosity is associated with blood levels of several fibrinolytic factors, including D-dimer, fibrinogen/fibrin degradation products, and plasmin inhibitor-plasmin complexes [11]. Specifically, there is a positive correlation between AAA size and fibrinogen concentration in the plasma [12]. For AAA many of the identified differences in the serum proteome often represented proteins highly abundant in serum rather than true candidate biomarkers [13]. Using a proteomic approach, our team has confirmed several plasma biomarkers that others have previously identified as being associated with AAA [7-14]. New approaches to quantitatively identify low-abundance proteins (such as cytokines) and their functional analysis into specific protein networks are of crucial importance in the post-genomic era [15-17]. A classical proteomics approach has been used to identify in AAA subjects four serum proteins that show altered expression profiles specifically linked to AAA pathology [18,19]. Proteome analysis has been generally limited in complex samples such as serum and plasma that have a dramatically wide dynamic range of protein abundance. The use of pre-fractionated plasma samples revealed that patients with small AAA showed an increased level of the enzyme glycosyl phosphatidyl inositol-specific phospholipase D compared to controls without aneurysm [20]. However, smaller proteins may be part of larger protein complexes and hence the removal of proteins involved in complexes with high-abundance proteins such as albumin may inhibit the search for disease biomarkers [7]. In recent years, bioinformatics approaches have been applied to provide systems biology interpretations of proteomic data [21]. The focus of the present study was the application of bioinformatics to analyze all the proteins lists obtained by proteomics experiments performed on human AAA serum or plasma samples. A common approach to the visualization and examination of "omics" data involves the generation of a network of all the individual components of a given setof experiments [22,23]. We used a network strategy to generate statistically valid hypotheses about biological mechanisms related to AAA and to provide new insight into the understanding of pathogenesis of such a complex disease [24]. To proceed we carried out a detailed bioinformatics analysis (using open source software for analyses/interpretation of biological data and visualization of complex networks obtained) on proteins identified in previous proteomics. Such an approach is being used with increased frequency, and such applications increase our understanding of complex human diseases including cardiovascular diseases [25].

Methods

Literature search to obtain the protein input list

Publications with the medical subject heading "abdominal aortic aneurysms" and keywords "proteomics" and "plasma" and "progression" or "growth" or "expansion rate" or "rupture" were searched in the MEDLINE/PubMed and EMBASE databases. The search was restricted until May 2013. Only human studies that reported aneurysm size, expansion rates and plasma markers were selected.

Bioinformatics analysis

We performed over-representation analysis (ORA) of the protein list obtained by the literature search (the input list) using the Webgestalt online tool (https://bioinfo.vanderbilt.edu/webgestalt/) against Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), WikiPathway and Pathway Commons databases [26]. The analysis of functional genomics data often involves the assessment of potential functional associations between a protein set of interest. To identify these putative associations several enrichment analysis tools have been developed including over-representation analysis (ORA) techniques. This method assess the statistical over-representation of a user-defined, pre-selected gene/protein list of interest in a reference list of known gene/protein sets using a statistical test, e.g. the one-sided Fisher's exact test or the hypergeometric distribution.

Afterwards the web portal BioProfiling.de (https://www. bioprofiling.de/) was used to build enriched networks. This portal covered most of the available information regarding signaling and metabolic pathways (database: Reactome) and physical protein interactions (database: IntAct) [27]. Accordingly, the input list was analyzed by Rspider [28] and PPI spider [29,30]. Both tools employed advanced enrichment or network-based statistical frameworks. The p-value provided was computed by the Monte Carlo simulation (https://www.bioprofiling.de/statistical_frameworks.html) and referred to the probability of obtaining a model of the same quality for a random gene list of the same size [31]. The significant networks, using a cut-off of p < 0.01, were further considered for proteomics data interpretation and discussion. The enriched network was exported as an .xgmml file and visualized and modified by Cytoscape 3.1.0 (https://www.cytoscape.org/) [32]. Moreover, the Cytoscape Jepetto (Java Enrichment of Pathways Extended to Topology) application was employed to perform ORA of the enriched networks [33] (https://apps.cytoscape.org/apps/jepetto). For the enrichment analysis by JEPETTO, EnrichNet server is used. EnrichNet maps the input gene set onto a molecular interaction network and, using a random walk, scores distances between the genes and pathways/ processes in a reference database. This network-based association score (XD-score) is relative to the average distance to all pathways and represents a deviation (positive or negative) from the average distance. Significantly over-represented pathways were selected if theirXd score was greater than that calculated as the intercept of the linear regression of Xd scores vs the Fisher's exact test p-value after Benjamini-Hochberg correction (q-value; Figure 1) [34].

Figure 1 :Linear regression analysis and Pearson correlation of Xd scores as obtained with the JEPETTO application vs the corrected p-value of the Fisher exact test. Xd values greater than the intercept of the linear model are considered as significant.

Figure 1 : Linear regression analysis and Pearson correlation of Xd scores as obtained with the JEPETTO application vs the corrected p-value of the Fisher exact test. Xd values greater than the intercept of the linear model are considered as significant.

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Results and Discussion

Literature analysis of proteomics studies of plasma AAA associated markers. Nine published proteomics studies were used to generate an input protein set of 64 seed proteins (the input list). The proteins included in the input list were differentially expressed in plasma from AAA patients with respect to controls. The 64 seed proteins are reported in Table 1.

Table 1: The input list.

  

    
Uniprot    accession number 
    
Gene    Symbol 
    
Protein    name 
    
Reference 
  
  

    
O00175
    
CCL24
    
chemokine (C-C motif) ligand 24
    
[17]
  
  

    
O43927
    
CXCL13
    
chemokine (C-X-C motif) ligand 13
    
[17]
  
  

    
P00450
    
CP
    
ceruloplasmin (ferroxidase)
    
[14,18]
  
  

    
P00734
    
F2
    
coagulation factor II (thrombin)
    
[14,20]
  
  

    
P00738
    
HP
    
haptoglobin
    
[7,14,18]
  
  

    
P00748
    
F12
    
coagulation factor XII (Hageman factor)
    
[16]
  
  

    
P00751
    
CFB
    
complement factor B
    
[7,14]
  
  

    
P01009
    
SERPINA1
    
serpin peptidase inhibitor, clade A (alpha-1    antiproteinase, antitrypsin), member 1
    
[7,14,16,20]
  
  

    
P01011
    
SERPINA3
    
serpin peptidase inhibitor, clade A (alpha-1    antiproteinase, antitrypsin), member 3
    
[14]
  
  

    
P01023
    
A2M
    
alpha-2-macroglobulin
    
[7,14]
  
  

    
P01024
    
C3
    
complement component 3
    
[16]
  
  

    
P01042
    
KNG1
    
kininogen 1
    
[7]
  
  

    
P01579
    
IFNG
    
interferon, gamma
    
[20]
  
  

    
P01834
    
IGKC
    
immunoglobulin kappa constant
    
[16]
  
  

    
P01857
    
IGHG1
    
immunoglobulin heavy constant gamma 1 (G1m marker)
    
[19]
  
  

    
P01859
    
IGHG2
    
immunoglobulin heavy constant gamma 2 (G2m marker)
    
[19]
  
  

    
P01871
    
IGHM
    
immunoglobulin heavy constant mu
    
[19]
  
  

    
P02647
    
APOA1
    
apolipoprotein A-I
    
[7,14,18,20]
  
  

    
P02655
    
APOC2
    
apolipoprotein C-II
    
[13]
  
  

    
P02671
    
FGA
    
fibrinogen alpha chain
    
[7,14]
  
  

    
P02675
    
FGB
    
fibrinogen beta chain
    
[14]
  
  

    
P02679
    
FGG
    
fibrinogen gamma chain
    
[7,14,18]
  
  

    
P02741
    
CRP
    
C-reactive protein, pentraxin-related
    
[20]
  
  

    
P02743
    
APCS
    
amyloid P component, serum
    
[18]
  
  

    
P02750
    
LRG1
    
leucine-rich alpha-2-glycoprotein 1
    
[7,14]
  
  

    
P02753
    
RBP4
    
retinol binding protein 4, plasma
    
[14]
  
  

    
P02760
    
AMBP
    
alpha-1-microglobulin/bikunin precursor
    
[15]
  
  

    
P02765
    
AHSG
    
alpha-2-HS-glycoprotein
    
[7,14]
  
  

    
P02768
    
ALB
    
albumin
    
[13,14,16]
  
  

    
P02774
    
GC
    
group-specific component (vitamin D binding protein)
    
[7,18]
  
  

    
P02775
    
PPBP
    
pro-platelet basic protein (chemokine (C-X-C motif)    ligand 7)
    
[15]
  
  

    
P02787
    
TF
    
transferrin
    
[7,14,18]
  
  

    
P02790
    
HPX
    
hemopexin
    
[7,18]
  
  

    
P05546
    
SERPIND1
    
serpin peptidase inhibitor, clade D (heparin cofactor),    member 1
    
[15]
  
  

    
P06396
    
GSN
    
gelsolin
    
[15,19]
  
  

    
P06727
    
APOA4
    
apolipoprotein A-IV
    
[14]
  
  

    
P07737
    
PFN1
    
profilin 1
    
[15]
  
  

    
P08185
    
SERPINA6
    
serpin peptidase inhibitor, clade A (alpha-1    antiproteinase, antitrypsin), member 6
    
[15]
  
  

    
P08294
    
SOD3
    
superoxide dismutase 3, extracellular
    
[15]
  
  

    
P08567
    
PLEK
    
pleckstrin
    
[15]
  
  

    
P08697
    
SERPINF2
    
serpin peptidase inhibitor, clade F (alpha-2 antiplasmin,    pigment epithelium derived factor), member 2
    
[15,20]
  
  

    
P08833
    
IGFBP1
    
insulin-like growth factor binding protein 1
    
[17]
  
  

    
P09237
    
MMP7
    
matrix metallopeptidase 7 (matrilysin, uterine)
    
[20]
  
  

    
P0C0L5
    
LOC100293534
    
complement C4-B-like
    
[18]
  
  

    
P10909
    
CLU
    
clusterin
    
[7,14]
  
  

    
P12814
    
ACTN1
    
actinin, alpha 1
    
[15]
  
  

    
P14151
    
SELL
    
selectin L
    
[17]
  
  

    
P15169
    
CPN1
    
carboxypeptidase N, polypeptide 1
    
[15]
  
  

    
P15502
    
ELN
    
elastin
    
[20]
  
  

    
P18065
    
IGFBP2
    
insulin-like growth factor binding protein 2, 36kDa
    
[17]
  
  

    
P18206
    
VCL
    
vinculin
    
[15]
  
  

    
P19320
    
VCAM1
    
vascular cell adhesion molecule 1
    
[17]
  
  

    
P21333
    
FLNA
    
filamin A, alpha
    
[15]
  
  

    
P29622
    
SERPINA4
    
serpin peptidase inhibitor, clade A (alpha-1    antiproteinase, antitrypsin), member 4
    
[15]
  
  

    
P39905
    
GDNF
    
glial cell derived neurotrophic factor
    
[17]
  
  

    
P61626
    
LYZ
    
lysozyme
    
[15]
  
  

    
P68871
    
HBB
    
hemoglobin, beta
    
[7,13]
  
  

    
P78556
    
CCL20
    
chemokine (C-C motif) ligand 20
    
[17]
  
  

    
P80108
    
GPLD1
    
glycosylphosphatidylinositol specific phospholipase D1
    
[19]
  
  

    
Q01518
    
CAP1
    
CAP, adenylate cyclase-associated protein 1 (yeast)
    
[15]
  
  

    
Q14624
    
ITIH4
    
inter-alpha-trypsin inhibitor heavy chain family, member    4
    
[19]
  
  

    
Q15848
    
ADIPOQ
    
adiponectin, C1Q and collagen domain containing
    
[15,17]
  
  

    
Q96IY4
    
CPB2
    
carboxypeptidase B2 (plasma)
    
[15]
  
  

    
Q9Y490
    
TLN1
    
talin 1
    
[15]
  

Table 1:  The input list.

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Over-representation analysis of the input list

The input list was uploaded to the Webgestalt portal and ORA was performed. Figure 2 shows the significantly over-represented GO Biological Processes (BP, Panel A), Molecular Functions (MF, Panel B) and Cellular Components (CC, Panel C) in the input protein set (for details, see Supplementary Table 1). As expected for this pathology, we found enriched Gene Ontology Biological Process (GO BP) terms involved in response mechanisms to generic stress conditions, including response to stress (GO:0006950 - 47 genes); and inflammatory response (GO:0006954 - 20 genes). Other BP terms appear to be more closely related to the pathological condition being investigated, namely hemostasis (GO: 0007599 - 25 genes); blood coagulation (GO: 0007596 - 25 genes); and the related protein activation cascade (GO: 0072376 - 8 genes). Of note, some of the BP terms corresponded to processes involved in transport and secretion, such as secretion by cell (GO: 0032940 - 24 genes); exocytosis (GO: 0006887 - 19 genes); and platelet degranulation (GO: 0002576 - 19 genes). As expected, the molecular functions related to coagulation proteases, including regulatory factors, were significantly enriched in the input list. Molecular function terms associated with receptor binding were over-represented in the list. The enriched GO MF terms contain ontologies involved in binding, including cell surface binding (GO: 0043498 - 7 genes); protein binding (GO: 0005515 - 47 genes); and receptor binding (GO: 0005102 - 19 genes). There were also enriched GO MF terms involved in enzyme regulator activity, including enzyme inhibitor (GO: 0004857 - 13 genes); peptidase regulator (GO: 0061134 - 12 genes); peptidase inhibitor (GO:0030414- 13 genes); endopeptidase regulator (GO:0061135-12 genes) and endopeptidase inhibitor (GO:0004866 - 12 genes). The enriched GO cellular components terms pointed out two major over-represented classes. The first were associated with the secretory pathway, which would be expected for platelet activation and included platelet alpha granule lumen (GO: 0031093 - 11 genes); and platelet alpha granule (GO: 0031091 - 11 genes). The other class was those components on the cell surface or in the extracellular region and included extracellular space (GO: 0005615 - 41 genes); extracellular region (GO: 0005576 - 54 genes); extracellular region part (GO:0044421 - 42 genes); and extracellular matrix (GO:0031012 - 10 genes), suggesting a possible role of cell-celland cell-ECM adhesion in the composition of the investigated signature. The set was then analyzed with respect to: KEGG (Table 2), Pathway Commons (Table 3) and Wiki Pathways (Table 4) databases. A significant over-representation of pathways related to blood coagulation and thrombin/plasmin cascades was found (14 genes), as expected. Additionally, evidence for a remarkable involvement of cell-mediated responses came to the light. Indeed, GO ORA suggested the role of soluble platelet activation markers as detectable in plasma. KEGG pathways indicated that a few proteins in the list were linked to focal adhesion (4 genes), cytokine-cytokine receptor interaction (5 genes) and chemokine signaling (4 genes). Results from the PathwayCommons ORA also supported the link of the proteins set with either cell-cell or cell-ECM adhesion processes (integrin family cell surface interaction - 21 genes, alpha9 beta1 integrin signaling events - 19 gens, proteoglycan syndecan-mediated signaling events - 21 genes), in addition to pathways associated with blood coagulation and fibrinolysis (hemostasis - 18 genes; thrombin/ protease-activated receptor (PAR) pathway and PAR1-8 mediated thrombin signaling events - 19 genes for each pathway). In addition, WikiPathways ORA indicated a possible connection between the input list and reactive oxygen species scavenging (selenium pathway - 8 genes) and biosynthesis. As a whole, the ORA performed on the input list identified several elements of the protein signature not directly related to wound healing and/or coagulation. Among those, the presence of soluble platelet activation markers and of proteins involved in cellular processes at the cell surface could represent an interesting hint to look for novel prognostic AAA markers. To further reinforce this finding, we performed network enrichment analysis.

Figure 2 :Gene Ontology over-representation analysis of the input list performed by Webgestalt significantly over-represented categories are highlighted in red. Panel A: GO Biological Process (BP). Panel B: GO Molecular Function (MF). Panel C: Cellular Component (CC).

Figure 2 : Gene Ontology over-representation analysis of the input list performed by Webgestalt significantly over-represented categories are highlighted in red. Panel A: GO Biological Process (BP). Panel B: GO Molecular Function (MF). Panel C: Cellular Component (CC).

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Table 2: Over-representation of KEGG pathways in the input dataset. p < 0.001 after Benjamini-Hochberg correction.

  

    
PathwayName
    
#Gene
    
EntrezGene
    
Statistics
  
  

    
Complement    and coagulation cascades
    
14
    
P0C0L5 P01024 P01009 Q96IY4 P05546 P01023 P01042 P02675    P02679 P00751 P02671 Q8IZZ5 P00748
    
C=69;O=14;E=0.10;R=136.73;rawP=7.91e-27;adjP=1.66e-25
  
  

    
African    trypanosomiasis
    
4
    
P01579 P68871 P19320 P02647
    
C=35;O=4;E=0.05;R=77.01;rawP=2.23e-07;adjP=2.34e-06
  
  

    
Staphylococcus    aureus infection
    
4
    
P0C0L5 P01024 P02679 P00751
    
C=55;O=4;E=0.08;R=49.01;rawP=1.42e-06;adjP=9.94e-06
  
  

    
Regulation    of actin cytoskeleton
    
5
    
P06396 P12814 P00734 P18206 P07737
    
C=213;O=5;E=0.32;R=15.82;rawP=1.69e-05;adjP=8.87e-05
  
  

    
Systemic    lupus erythematosus
    
4
    
P0C0L4 P01579 P01024 P12814
    
C=136;O=4;E=0.20;R=19.82;rawP=5.19e-05;adjP=0.0002
  
  

    
Cytokine-cytokine    receptor interaction
    
5
    
P01579 O00175 O43927 P78556 P02775
    
C=265;O=5;E=0.39;R=12.71;rawP=4.78e-05;adjP=0.0002
  
  

    
Malaria
    
3
    
P01579 P68871 P19320
    
C=51;O=3;E=0.08;R=39.64;rawP=6.17e-05;adjP=0.0002
  
  

    
Focal    adhesion
    
4
    
P12814 P21333 P18206 Q9Y490
    
C=200;O=4;E=0.30;R=13.48;rawP=0.0002;adjP=0.0005
  
  

    
Chemokine    signaling pathway
    
4
    
O00175 O43927 P78556 P02775
    
C=189;O=4;E=0.28;R=14.26;rawP=0.0002;adjP=0.0005
  
  

    
Amoebiasis
    
3
    
P01579 P12814 P18206
    
C=106;O=3;E=0.16;R=19.07;rawP=0.0005;adjP=0.0010
  

Table 2:  Over-representation of KEGG pathways in the input dataset. p < 0.001 after Benjamini-Hochberg correction.

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Table 3: Over - Representation of pathway commons pathways in the input dataset. p < 0.001 after Benjamini-Hochberg correction.

  

    
PathwayName
    
#Gene
    
EntrezGene
    
Statistics
  
  

    
Hemostasis
    
18
    
P00734 Q9Y490 P01023 P14151 P01042 P02679 P00748 P21333    P02787 P68871 P08567 Q01518 P07737 P02675 P02671 P08697 P02647 P18206
    
C=376;O=18;E=0.56;R=32.26;rawP=1.41e-22;adjP=1.71e-20
  
  

    
Platelet    degranulation
    
8
    
Q9Y490 P21333 P02787 P08567 Q01518 P07737 P02647 P18206
    
C=29;O=8;E=0.04;R=185.89;rawP=6.25e-17;adjP=3.78e-15
  
  

    
Proteoglycan    syndecan-mediated signaling events
    
21
    
P01579 P00734 P00450 Q9Y490 P08833 P01023 P25090 P02679    P12814 P21333 P02787 P80108 P02765 P06396 P09237 P39905 P02675 P02671 P10909    P18206 P02741
    
C=1345;O=21;E=2.00;R=10.52;rawP=2.32e-16;adjP=9.36e-15
  
  

    
Integrin    family cell surface interactions
    
21
    
P01024 P01579 P00450 Q9Y490 P08833 P01023 P25090    P02679P12814 P02787 P80108 P02765 P06396 P09237 P39905 P19320 P02675 P02671    P10909 P18206 P02741
    
C=1378;O=21;E=2.04;R=10.27;rawP=3.76e-16;adjP=1.14e-14
  
  

    
Response    to elevated platelet cytosolic Ca2+
    
8
    
Q9Y490 P21333 P02787 P08567 Q01518 P07737P02647P18206
    
C=40;O=8;E=0.06;R=134.77;rawP=1.11e-15;adjP=2.69e-14
  
  

    
Alpha9    beta1 integrin signaling events
    
19
    
P01579 P00450 Q9Y490 P08833 P01023 P02679 P12814 P02787    P80108 P02765 P06396 P09237 P19320 P39905 P02675 P02671 P10909 P18206 P02741
    
C=1305;O=19;E=1.94;R=9.81;rawP=2.89e-14;adjP=4.37e-13
  
  

    
PAR1-mediated    thrombin signaling events
    
19
    
P01579 P00734 P00450 Q9Y490 P08833 P01023 P02679 P12814    P02787 P80108 P02765 P06396 P09237 P39905 P02675 P02671 P10909 P18206 P02741
    
C=1299;O=19;E=1.93;R=9.86;rawP=2.66e-14;adjP=4.37e-13
  
  

    
Thrombin/protease-activated    receptor (PAR) pathway
    
19
    
P01579 P00734 P00450 Q9Y490 P08833 P01023 P02679 P12814    P02787 P80108 P02765 P06396 P09237 P39905 P02675 P02671 P10909 P18206 P02741
    
C=1300;O=19;E=1.93;R=9.85;rawP=2.69e-14;adjP=4.37e-13
  
  

    
Formation    of Fibrin Clot (Clotting Cascade)
    
7
    
P00734 P01023 P01042 P02679 P00748 P02675 P02671
    
C=33;O=7;E=0.05;R=142.94;rawP=4.68e-14;adjP=5.66e-13
      
 
  

Table 3:  Over - Representation of pathway commons pathways in the input dataset. p < 0.001 after Benjamini-Hochberg correction.

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Table 4: Over-Representation of WikiPathways entries in the input dataset. p < 0.001 after Benjamini-Hochberg correction.

  

    
PathwayName
    
#Gene
    
EntrezGene
    
Statistics
  
  

    
Complement    and Coagulation Cascades
    
10
    
P01024 P00734 P01009 P05546 Q96IY4 P01023 P01042 P02675    P00748 P08697
    
C=51;O=10;E=0.08;R=132.13;rawP=3.01e-19;adjP=4.52e-18
  
  

    
Blood    Clotting Cascade
    
6
    
P00734 P02679 P02675 P00748 P02671 P08697
    
C=26;O=6;E=0.04;R=155.51;rawP=1.89e-12;adjP=1.42e-11
  
  

    
Selenium    Pathway
    
8
    
P68871 P01579 P00734 P02768 P01011 P02647 P02741 P08294
    
C=108;O=8;E=0.16;R=49.92;rawP=4.68e-12;adjP=2.34e-11
  
  

    
Regulation    of Actin Cytoskeleton
    
5
    
P00734 P06396 P07737 P12814 P18206
    
C=157;O=5;E=0.23;R=21.46;rawP=3.85e-06;adjP=1.44e-05
  
  

    
Vitamin    B12 Metabolism
    
3
    
P68871 P08294 P02647
    
C=24;O=3;E=0.04;R=84.23;rawP=6.17e-06;adjP=1.85e-05
  
  

    
Folate    Metabolism
    
3
    
P68871 P08294 P02647
    
C=29;O=3;E=0.04;R=69.71;rawP=1.11e-05;adjP=2.64e-05
  
  

    
Statin    Pathway
    
3
    
P02655 P06727 P02647
    
C=30;O=3;E=0.04;R=67.39;rawP=1.23e-05;adjP=2.64e-05
  

Table 4:  Over-Representation of WikiPathways entries in the input dataset. p < 0.001 after Benjamini-Hochberg correction.

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Network enrichment

In order to discriminate the number of proteins that could be associated to disease, the protein set was enriched in terms of a network-based statistical framework. As a first approach, a pathway-based network was built by allowing the insertion of no more than 1 or 2 intermediate nodes using the Reactome database as a reference (Figure 3). In both cases, highly interconnected networks were obtained, with 12 nodes (Figure 3, Panel A) and 18 nodes (Figure 3, Panel B) overlapping with the input list, respectively. Furthermore, a physical-interaction-based network was built by limiting the insertion of enriched nodes to one or two using the Int Act database as a reference (Figure 4). In the resulting networks, 18 nodes (Figure 4, Panel A) and 26 nodes (Figure 4, Panel B) overlapped with the input list, respectively. Interestingly, a subnetwork of 11 interacting proteins was detected without any added node. References for the protein-protein interaction pairs defining the edges of Figure 4 are reported in the Supplementary Table 2.

Figure 3 :Enriched networks obtained with Rspider. Panel A: A maximum of one intermediate node was allowed to be added. Panel B: A maximum of two intermediate nodes was allowed to be added. Network visualization was obtained using Cytoscape. Nodes arising from the "input list" are shown as white squares, whereas enriched nodes are represented by red triangles. p < 0.01.

Figure 3 : Enriched networks obtained with Rspider. Panel A: A maximum of one intermediate node was allowed to be added. Panel B: A maximum of two intermediate nodes was allowed to be added. Network visualization was obtained using Cytoscape. Nodes arising from the "input list" are shown as white squares, whereas enriched nodes are represented by red triangles. p < 0.01.

Close

Over-representation analysis of the enriched networks

Enriched networks were analyzed by ORA within the specific network-based environment provided by the Cytoscape platform. As far as pathway-based networks are concerned, the analysis was limited to GO terms. As a first approach, the protein set of Figure 3A was analyzed, and the results are listed in Table 5. In addition to GO categories intrinsically linked to blood coagulation, a BP term associated with T-cell involvement was significantly over-represented, suggesting a role of lymphocyte cell-mediated responses in the composition of the plasma proteome AAA fingerprint. The same is supported by MF terms being over-represented in the present analysis, such as signal transduction pathways associated to surface binding and growth factor receptors. However, the analysis of this enriched network did not add a significant amount of new information to the analysis of the input list. Therefore, a more extended enrichment was taken into account. The analysis of the extended network (Figure 3, Panel B) revealed the identification of other categories possibly involved in the AAA pathogenetic process (Table 6). By allowing a maximum of two intermediate nodes to be inserted in the pathway-based network, stronger roles for cell-cell/ECM interaction and leukocyte-mediated response became appreciable in the plasma proteome. The ORA procedure was also performed on the protein-protein interaction networks. A physical-interaction-based enrichment procedure may potentially explore distinct pathways and/or GO categories that would have been restricted by a pathway-based procedure. Accordingly, the highly interconnected subnetwork highlighted in Figure 4A was analyzed as a standalone network (Table 7), and the whole graph was later taken into account to find over-represented GO categories and reactome pathways (Table 8). The analysis of the interconnected subcluster highlighted in Figure 4A drew attention to lipid storage and transport and again an involvement for platelet degranulation. The analysis of the whole network indicated the potential molecular mediators of the cellular response evidenced above. In particular, the signal transduction pathways linked to integrin signaling such as the GRB2/ SOS/ERK cascade was highly prevalent. The same ORA procedure applied to the network reported in Figure 4B did not add any other significant category/pathway to the results (Supplementary Table 3).

Figure 4 :Enriched networks obtained with PPI spider. Panel A: A maximum of one intermediate node was allowed to be added. Panel B: A maximum of two intermediate nodes was allowed to be added. Network visualization was obtained using Cytoscape. p < 0.01. Nodes arising from the "input list" are shown as white/yellow squares, whereas enriched nodes are represented by red triangles. A highly-interconnected sub network without added nodes is highlighted in yellow.

Figure 4 : Enriched networks obtained with PPI spider. Panel A: A maximum of one intermediate node was allowed to be added. Panel B: A maximum of two intermediate nodes was allowed to be added. Network visualization was obtained using Cytoscape. p < 0.01. Nodes arising from the "input list" are shown as white/yellow squares, whereas enriched nodes are represented by red triangles. A highly-interconnected sub network without added nodes is highlighted in yellow.

Close

Table 5: Over-Representation analysis of the network displayed in Figure 3, Panel A.

  

    
GO    Category (Biological Process)
    
XD-score
    
q-value
    
Overlap/Size
  
  

    
blood    coagulation, intrinsic pathway
    
1,63249
    
0,00000
    
7/17
  
  

    
regulation    of blood coagulation
    
1,18543
    
0,00021
    
3/10
  
  

    
positive    regulation of blood coagulation
    
0,78543
    
0,02288
    
2/10
  
  

    
positive    regulation of activated T cell proliferation
    
0,78543
    
0,00065
    
3/15
  
  

    
positive    regulation of glycogen biosynthetic process
    
0,71270
    
0,02540
    
2/11
  
  

    
fibrinolysis
    
0,65210
    
0,00099
    
3/18
  
  

    
protein    localization at cell surface
    
0,55686
    
0,03839
    
2/14
  
  

    
GO    Category (Molecular Function)
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
insulin-like    growth factor receptor binding
    
0,65619
    
0,01662
    
2/12
  
  

    
eukaryotic    cell surface binding
    
0,51126
    
0,00296
    
3/23
  
  

    
insulin-like    growth factor binding
    
0,46011
    
0,02919
    
2/17
  
  

    
insulin    receptor binding
    
0,40332
    
0,00298
    
3/29
  
  

    
Rho    GTPase binding
    
0,33735
    
0,04721
    
2/23
  
  

    
GO    Category (Cellular Component)
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
platelet    alpha granule
    
1,33158
    
0,00000
    
4/12
  
  

    
platelet    alpha granule lumen
    
0,59768
    
0,00000
    
7/46
  

Table 5:  Over-Representation analysis of the network displayed in Figure 3, Panel A.

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Table 6: Over-representation analysis of the network displayed in Figure 3, Panel B.

  

    
GO    Category (Biological Process)
    
XD-score
    
q-value
    
Overlap/Size
  
  

    
blood    coagulation, intrinsic pathway
    
1,61553
    
0,00000
    
7/17
  
  

    
negative    regulation of fibrinolysis
    
1,16847
    
0,00107
    
3/10
  
  

    
regulation    of blood coagulation
    
1,16847
    
0,00107
    
3/10
  
  

    
fibrinolysis
    
1,07959
    
0,00000
    
5/18
  
  

    
positive    regulation of blood coagulation
    
0,76847
    
0,04487
    
2/10
  
  

    
positive    regulation of activated T cell proliferation
    
0,76847
    
0,00334
    
3/15
  
  

    
positive    regulation of insulin-like growth factor receptor signaling pathway
    
0,76847
    
0,04487
    
2/10
  
  

    
platelet    degranulation
    
0,75795
    
0,00000
    
15/76
  
  

    
positive    regulation of glycogen biosynthetic process
    
0,69575
    
0,05110
    
2/11
  
  

    
tissue    regeneration
    
0,67436
    
0,00459
    
3/17
  
  

    
protein    localization at cell surface
    
0,53990
    
0,06812
    
2/14
  
  

    
positive    regulation of collagen biosynthetic process
    
0,53990
    
0,06812
    
2/14
  
  

    
myoblast    differentiation
    
0,53990
    
0,06812
    
2/14
  
  

    
leukocyte    cell-cell adhesion
    
0,49021
    
0,01018
    
3/23
  
  

    
protein    polymerization
    
0,49021
    
0,01018
    
3/23
  
  

    
GO    Category (Molecular Function)
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
insulin-like    growth factor binding
    
0,68349
    
0,00260
    
3/17
  
  

    
eukaryotic    cell surface binding
    
0,67326
    
0,00040
    
4/23
  
  

    
fibronectin    binding
    
0,64427
    
0,00260
    
3/18
  
  

    
insulin-like    growth factor receptor binding
    
0,64427
    
0,03254
    
2/12
  
  

    
laminin    binding
    
0,51094
    
0,04795
    
2/15
  
  

    
protease    binding
    
0,40550
    
0,00040
    
5/47
  
  

    
insulin    receptor binding
    
0,39140
    
0,00902
    
3/29
  
  

    
GO    Category (Cellular Component)
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
platelet    alpha granule
    
1,31763
    
0,00001
    
4/12
  
  

    
platelet    alpha granule lumen
    
0,93156
    
0,00000
    
11/46
  
  

    
fascia    adherens
    
0,59042
    
0,03764
    
2/13
  
  

    
cortical    actin cytoskeleton
    
0,39609
    
0,07391
    
2/19
  
  

    
podosome
    
0,37504
    
0,53453
    
1/10
  

Table 6:  Over-representation analysis of the network displayed in Figure 3, Panel B.

Close

Table 7: Over-Representation analysis of the subnetwork highlighted in Figure 4, Panel A.

  

    
GO    Category (Biological Process)
    
XD-score
    
q-value
    
Overlap/Size
  
  

    
reverse    cholesterol transport
    
0,72179
    
0,01239
    
2/11
  
  

    
protein    polymerization
    
0,51626
    
0,00047
    
3/23
  
  

    
triglyceride    homeostasis
    
0,39452
    
1,00000
    
1/10
  
  

    
endothelial    cell proliferation
    
0,39452
    
1,00000
    
1/10
  
  

    
complement    activation
    
0,35815
    
0,04435
    
2/22
  
  

    
cholesterol    efflux
    
0,35815
    
1,00000
    
1/11
  
  

    
lipid    storage
    
0,32785
    
1,00000
    
1/12
  
  

    
iron ion    transport
    
0,32785
    
1,00000
    
1/12
  
  

    
platelet    degranulation
    
0,31031
    
0,00000
    
6/76
  
  

    
GO    Category (Molecular Function)
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
eukaryotic    cell surface binding
    
0,51797
    
0,00055
    
3/23
  
  

    
lipid    transporter activity
    
0,32956
    
0,91340
    
1/12
  
  

    
GO    Category (Cellular Component)
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
platelet    alpha granule
    
0,99286
    
0,00002
    
3/12
  
  

    
platelet    alpha granule lumen
    
0,34068
    
0,00001
    
4/46
  
  

    
very-low-density    lipoprotein particle
    
0,32619
    
0,43292
    
1/12
  

Table 7:  Over-Representation analysis of the subnetwork highlighted in Figure 4, Panel A.

Close

Table 8: Over-representation analysis of the network shown in Figure 4, Panel A.

  

    
GO    Category (Biological Process)
    
XD-score
    
q-value
    
Overlap/Size
  
  

    
reverse    cholesterol transport
    
0,71498
    
0,05011
    
2/11
  
  

    
platelet    degranulation
    
0,57981
    
0,00000
    
10/76
  
  

    
protein    localization at cell surface
    
0,55914
    
0,07227
    
2/14
  
  

    
protein    polymerization
    
0,50945
    
0,00533
    
3/23
  
  

    
GO    Category (Molecular Function)
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
eukaryotic    cell surface binding
    
0,51256
    
0,00523
    
3/23
  
  

    
GO    Category (Cellular Component)
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
platelet    alpha granule
    
0,98688
    
0,00013
    
3/12
  
  

    
platelet    alpha granule lumen
    
0,61731
    
0,00000
    
6/46
  
  

    
Wiki    Pathways
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
Blood    Clotting Cascade
    
0,55235
    
0,00472
    
3/21
  
  

    
Reactome
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
COMMON    PATHWAY
    
0,89808
    
0,00091
    
3/13
  
  

    
GRB2 SOS    PROVIDES LINKAGE TO MAPK SIGNALING FOR INTEGRINS
    
0,77500
    
0,00108
    
3/15
  
  

    
P130CAS    LINKAGE TO MAPK SIGNALING FOR INTEGRINS
    
0,77500
    
0,00108
    
3/15
  
  

    
FURTHER    PLATELET RELEASATE
    
0,73691
    
0,00007
    
4/21
  
  

    
PLATELET    DEGRANULATION
    
0,53750
    
0,00000
    
10/80
  
  

    
KEGG
    
XD-score 
    
q-value 
    
Overlap/Size 
  
  

    
Complement    and coagulation cascades
    
0,29122
    
0,00041
    
5/65
  

Table 8:  Over-representation analysis of the network shown in Figure 4, Panel A.

Close

Concluding remarks

A thorough systems biology analysis of the complex plasma protein panel associated with AAA has highlighted the relevant presence of proteins linked to blood coagulation and to the proteolytic enzyme cascade leading to blood clotting. Moreover, input list of proteins uncovered significant functional connections with biological processes and molecular functions mediated by blood cells. In particular, cell adhesion mechanisms mediated by integrins, signal transduction pathways elicited by cytokines and growth factors, and markers of the inflammatory response represent a valuable pool of candidate novel biomarkers of AAA outcomes. As reported by Albert-László Barabási [22] an integrated understanding of the interactions among the genome, the proteome, the environment and the pathophenome, mediated by the underlying cellular network, offers a basis for the cells' global organization - the 'think globally, act locally' paradigm of network medicine.

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Citation: Modesti A, Alberio T, Gamberi T, Fasano M, Magherini F, et al. Network Biology Analysis of the Human Abdominal Aortic Aneurysm Plasma Proteome. Austin J Proteomics Bioinform & Genomics. 2014;1(2): 10. ISSN : 2471-0423

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