Abstract
Although more than 1 in 4 men develop symptomatic inguinal hernia
during their lifetime, the molecular mechanism behind inguinal hernia
remains unknown. Here, we explored the protein-protein interaction
network built on known inguinal hernia-causative genes to identify
essential and common downstream proteins for inguinal hernia formation.
We discovered that PIK3R1, PTPN11, TGFBR1, CDC42, SOS1, and KRAS were
the most essential inguinal hernia-causative proteins and UBC, GRB2,
CTNNB1, HSP90AA1, CBL, PLCG1, and CRK were listed as the most
commonly-involved downstream proteins. In addition, the transmembrane
receptor protein tyrosine kinase signaling pathway was the most
frequently found inguinal hernia-related pathway. Our in silico
approach was able to uncover a novel molecular mechanism underlying
inguinal hernia formation by identifying inguinal hernia-related
essential proteins and potential common downstream proteins of inguinal
hernia-causative proteins.
Introduction
In general surgery, inguinal hernia repair is one of the most routine
operations worldwide. More than one in four men can expect to undergo
inguinal hernia repair during their lifetime [[34]1, [35]2]. Annual
health care costs directly attributable to inguinal hernia exceed $2.5
billion in the US [[36]3]. Inguinal hernias can be classified as either
indirect, where the bowel herniates through a defective inguinal ring,
or direct, where the bowel protrudes through the weakened lower
abdominal muscle wall [[37]3–[38]6]. At times, inguinal hernias can
cause severe complications, such as incarceration and strangulation and
currently, surgery is the only treatment option for the management of
inguinal hernia. Unfortunately, complications such as postoperative
pain, nerve injury, infection, and recurrence continue to challenge
surgeons and patients [[39]5, [40]7–[41]9]. Despite its prevalence in
patients, the molecular mechanisms that predispose individuals to
develop inguinal hernias are still unknown.
There are several key risk factors for inguinal hernias. For instance,
men are more predisposed to developing inguinal hernias and have a
lifetime risk of 27% compared with a 3% lifetime risk in women [[42]1].
Old age is also a significant risk factor for hernia with incidence
peaking between 60 and 75 years of age, with approximately 50% of men
developing an inguinal hernia by the age of 75 [[43]10–[44]13]. The
risk of inguinal hernia also increases among first-degree relatives of
inguinal hernia patients, indicating a genetic risk factor for inguinal
hernia development [[45]14, [46]15]. Additionally, individuals with
connective tissue genetic diseases such as cutis laxa [[47]16], Marfan
syndrome [[48]17], and Ehlers-Danlos syndrome [[49]18] have a greater
risk of developing inguinal hernias. To date, only a small number of
those candidate genes have been investigated [[50]19–[51]24]. Among
those findings, a large genome-wide association study recently
identified four novel inguinal hernia susceptibility loci in the
regions of EFEMP1, WT1, EBF2, and ADAMTS6. Moreover, mouse connective
tissue and network analyses showed that two of these genes (EFEMP1 and
WT1) are critical for connective tissue maintenance/homoeostasis given
their expression [[52]21]. However, inguinal hernia-causative genes and
their corresponding proteins in the pathophysiology of inguinal hernia
are still unknown.
Recently, big data analysis has enabled the discovery of crucial
disease-causative genes and pathogenic mechanisms by exploring publicly
available Online Mendelian Inheritance in Man (OMIM) databases.
Furthermore, the protein-protein interactions (PPIs) between
corresponding proteins of disease-causative genes were studied by
construction of the PPI network [[53]25]. The PPI network for studying
human diseases has achieved noteworthy results [[54]26–[55]30]. Several
previous studies showed the feasibility of computational approaches to
predict gene essentiality and morbidity [[56]31–[57]34]. For example,
the topological properties of PPIs have been employed to identify
essential proteins in various organisms [[58]35, [59]36]. The main
concept is the “centrality-lethality rule”, in which highly connected
hub proteins with a central role are more essential to survival in the
PPI network [[60]34]. Although there is still significant debate
regarding this rule, several studies suggest a correlation between
topological centrality and protein essentiality [[61]27, [62]37,
[63]38]. Additionally, there may be common downstream proteins, which
maximally connect with those inguinal hernia-causative proteins through
either direct or indirect interaction. We applied these concepts to
calculate the essentiality of each protein in the inguinal hernia-PPI
network to define crucial inguinal hernia-causative proteins and their
downstream proteins.
In the present study, we constructed a PPI network based on inguinal
hernia-causative genes imported from the OMIM database. We then
identified key protein nodes of significant influence using topological
network indices, namely, degree, betweenness, closeness, and
eigenvector centrality. Our integration of network topological
properties and protein cluster information revealed several highly
ranked essential proteins related to inguinal hernia formation. We also
performed the functional enrichment analysis of those essential
proteins and identified several common downstream key proteins. Our
results revealed the novel molecular mechanisms associated with human
inguinal hernias which may serve as the potential drug targets to
combat this prevalent disease.
Materials and methods
The analytical framework
To investigate the essential and common proteins related to inguinal
hernia, the analytical framework is schematically illustrated in
[64]Fig 1. The whole process in this study consists of three main
steps–construction, processing, and detecting: 1) Construction involves
obtaining the inguinal hernia causal genes from the OMIM database and
creating the inguinal hernia PPI networks by inputting the inguinal
hernia-causative genes into Interologous Interaction Database (I2D); 2)
Processing involves measuring topology-based features in the protein
interaction, identifying clusters, and analyzing the functional
enrichment and pathways; and 3) Detecting involves defining essential
and common downstream proteins.
Fig 1. The overall framework to detect essential and common downstream
proteins.
[65]Fig 1
[66]Open in a new tab
Database
Two main databases, OMIM and I2D, were used in this study. The OMIM
database is a comprehensive research resource of curated descriptions
of human genes and genetic disorders [[67]39]. I2D is a comprehensive
database integrating experimental and predicted PPIs which possesses 38
well-known human protein interaction databases (e.g., Inact, BINT,
HRPD, and MINT) containing over 230,000 experimental and approximately
70,000 predicted PPIs from human sources
([68]http://ophid.utoronto.ca/i2d). Identified proteins were unified
using the protein IDs defined in the Uniprot database [[69]25]. The
database versions of OMIM and I2D were updated in December 2017.
Inguinal hernia-related genes and construction of PPI networks
Our input of the term “inguinal hernia” into the OMIM database yielded
a list of hereditary genes of inguinal hernia from the OMIM morbid map
([70]http://www.omim.org). Then, these gene names were submitted to the
I2D database with human as the chosen target organism and the resultant
PPIs were generated including predicted and experimental protein
interactions. To increase the data reliability of protein interactions,
all predicted homologous protein interactions were excluded. The
remaining protein interactions were employed to construct the inguinal
hernia PPI network in which proteins serve as nodes and protein-protein
interactions serve as edges. Protein identifiers were unified using the
protein IDs defined in the UniProt database [[71]24]. Because some
proteins were given multiple names, the results in tables and figures
were presented in the format of gene names and UniProt IDs to avoid the
ambiguous referring.
Identifying clusters
To further understand the biological function of the PPI network
generated by using inguinal hernia-causative genes, a clique
percolation clustering method (CPM) [[72]30], which is a partition
algorithm, was first used to identify dense subgraphs with various
k-cliques (k is the size of a clique, a k-clique at k = 3 is equivalent
to a triangle) and then explore overlapping clusters. A cluster is
composed of a series of adjacent k-cliques, which can be reached from
one another through the overlapping protein nodes. In the present
study, the inguinal hernia PPIs were further analyzed using
CFinder-2.0.6, an open source CPM software platform, for detecting the
densely connected regions in the PPI network that have possible
overlapping clustering between various k-cliques, leading to
speculating their specific biological functions [[73]29].
Detecting essential proteins
Essential proteins exert vital roles in cellular processes and are
indispensable for survival or reproduction [[74]29, [75]40]. Essential
proteins are also critical for the development of human diseases
[[76]41]. Here, the topological features of the PPI network were used
to detect the role of essential proteins in inguinal hernia disease.
These methods are based on the centrality-lethality rule, which means
essential proteins tend to form hubs (highly connected protein nodes)
in the PPI network. The removal of essential proteins causes the PPI
network to break down [[77]28]. Genome-wide studies show that deletion
of a hub protein is more likely to be lethal than deletion of a non-hub
protein [[78]27]. A number of categories for defining centralities,
such as degree centrality (DC) [[79]27], closeness centrality
(CC)[[80]26], betweenness centrality (BC) [[81]42, [82]43], and
eigenvector centrality (EC) [[83]44], have been proposed to
characterize the inguinal hernia PPI network and the participating
proteins for predicting essential proteins.
Suppose a PPI network is regarded as an undirected graph G (V, E) with
proteins as nodes (V) and interactions as edges (E), where u represents
a protein node in the PPI network and v is any protein nodes other than
u in the network, four features of the inguinal hernia PPI network were
characterized as follows:
(1) Degree centrality (DC) measures the number of interactions that a
protein has. It can be defined as the following equation [[84]27].
[MATH: DC(u)=∑uedg(u,v), :MATH]
(Eq 1)
where edg(u,v) is the interaction between u and v. if such interaction
does exist, the edg(u,v) is one. If not, it is zero.
(2) Closeness centrality (CC) is a measurement of how close a protein
is to others. The CC of a protein node in the PPI network is considered
as the reciprocal sum of its distance to all other nodes. It can be
defined as the following equation [[85]26].
[MATH: CC(u)=N−1
∑v∈Vdis(u,v),
:MATH]
(Eq 2)
where N is the number of the protein node in V and dis(u,v) is the
distance between u and v.
(3) Betweenness centrality (BC) measures the positional influence of a
protein in the networks. The BC of a node k in the PPI network is
defined as the relative stress centrality that can quantify the extent
to which node k monitors the communication between other nodes. It can
be defined as the following equation [[86]42, [87]43].
[MATH: δuv(k)=p(u,k,v)p(u,v),u≠k≠v,BC(k)=∑u∈V∑v∈Vδuv(k), :MATH]
(Eq 3)
where δ[uv](k) is the fraction of the shortest paths that pass through
the node k in the PPI network.
(4) Eigenvector centrality (EC) measures the relative number of
interaction connecting one protein to its surrounding proteins. The EC
of a protein node in the PPI network assumes that the centrality value
of a protein node depends on the values of each adjacent node, which is
defined as the following equation [[88]44].
[MATH: EC(u)=emax(u), :MATH]
(Eq 4)
where e[max] denotes the principal eigenvector of the adjacency matrix
(PPI network considered as a matrix) and e[max](u) denotes the u-th
component of the principal eigenvector.
Although the computation of centrality based on the network topology
has become an important method for identifying essential proteins, it
is difficult to identify many essential proteins that have low
connectivity in the PPI network [[89]45]. Recently, the majority of
studies have shown that the essentiality of proteins has a strong
correlation with clusters [[90]46, [91]47], which indicates that
essential proteins tend to gather in clusters. To further analyze the
PPI network employing both topology features and the cluster
characteristics, a novel edge clustering coefficient (ECC) algorithm
was designed to better detect essential proteins [[92]46].
First, the cluster centrality of a protein i, which means the
overlapping cluster number of a protein, is defined as follows:
[MATH: fun_c(i)=∑k=1m(|e(i,j)|),i,j<
mo>∈V(Ck) :MATH]
(Eq 5)
where fun_c(i) is the cluster centrality of a protein i, m is the
number of clusters containing i, j is any proteins other than i in the
PPI network, e(i, j) is an edge between i and j, C[k] is the k^th
cluster (1≤k≤m), and V(C[k]) is the node set of C[k].
Next, together with cluster centrality, the PPI network topology
features are added to measure the essential protein via the shape of
the network [[93]46]. Suppose the most appropriate topology feature was
defined as TopoCentrality, to integrate TopoCentrality(i) and cluster
centrality fun_c(i), the harmonic centrality (HC) of a protein i was
defined as follows:
[MATH: HC(i)=δ*Top<
mi>oCentrality(i)Topo
Central<
/mi>itymax+(1−δ)*fun<
mo>_c(i)fun_
cmax, :MATH]
(Eq 6)
where TopoCentrality(i) is the most appropriate topology centrality of
i, TopoCentrality[max] is the maximum value of TopoCentrality(i),
fun_c[max] is the maximum value of fun_c(i), δ is a tunable factor in
the range [0,1] which is used to adjust the weights of
TopoCentrality(i) and fun_c(i). Generally, δ is set to 0.5.
Gene ontology and pathway enrichment analyses
To further explore the biological roles of the genes in clusters, Gene
Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathway enrichment analyses were conducted using the tools from the
Database for Annotation, Visualization and Integrated Discovery (DAVID,
Version 6.8), which is a web-based bioinformatics resource, an
integrated analysis tool, and a biological knowledge base [[94]48]. GO
term enrichment and KEGG pathway analyses were performed using the GO
knowledgebase ([95]http://www.geneontology.org) and KEGG
([96]http://www.genome.jp/kegg/) database, respectively.
Finding common downstream proteins
To determine the common downstream proteins related to inguinal hernia,
a novel deformation breadth-first search (DBFS) algorithm was designed.
All causative proteins related to inguinal hernia in the PPI network
were assumed as the destination set D. Proteins closely linked to
destination set D were considered as the common downstream proteins
because these proteins in the same clusters closely interact with each
other to play a critical role in inguinal hernia development. The brief
procedure for finding common downstream proteins are summarized here.
Firstly, the DBFS algorithm found all adjacent proteins (i.e., one hop
proteins) for every destination protein in set D. These one hop
proteins, if they were not being visited before and were not included
in the destination set D, were able to be visited. Then, all adjacent
proteins (i.e., two hops proteins) of every one hop protein were
searched and operated using the same rule. This step was repeated until
the path length reached the constrain threshold. The detailed procedure
of DBFS is shown as below:
Input: G = ,G∈ inguinal hernia PPI networks; L = the constrain
threshold of path length;
D = (d[1],d[2],…,d[n]),d[i]∈destination protein set.
InitQueue(Q); InitQueue(T); //set Q and T as queue
For (i = 0; i = 0;w = NextAdj(G,u,w))
//obtain all adjacent nodes of u
If ((!visited[w]) && (w not in D)) //the adjacent point
w is not visited and not belongs to D
Visited[w] = TRUE; Visited(w,distance);
EnQueue(T,w);
Endfor
DeQueue(T, u);
EndWhile
EnQueue (T, -1); distance = distance+1; DeQueue(T,u);
EndWhile
EndWhile
Statistical analysis
To determine whether the value significantly deviates from the mean
value, the modified Thompson tau technique was employed. Its basic
concepts are as follows:
[MATH:
τ=tα
/2⋅(n−1)n⋅
n−2+tα/2
2 :MATH]
(Eq 7)
where n is the number of data points, t[α/2] is the critical student’s
t value, SD is sample standard deviation, α = 0.05, and df = n-2. * >
mean ± (τ·SD)/2, ** > mean ± τ·SD.
Results
Inguinal hernia-causative genes and the inguinal hernia PPI network
construction
A total of 83 inguinal hernia-causative genes were obtained from the
OMIM database as shown in [97]Table 1. Based on known PPIs, the
interactions of those gene products (inguinal hernia-causative
proteins) were investigated by exploring the I2D database to build the
inguinal hernia PPI network. To achieve this, all genes had to contain
the loci with known encoding protein profiles in Uniprot. After removal
of four genes without corresponding known coding proteins (i.e., SRS,
DIH2, ICR1, and H19) from the inguinal hernia-causative gene list, 79
genes were used to explore the I2D database. Our input of inguinal
hernia-related proteins into I2D yielded 8,215 interactions in inguinal
hernia PPI networks. After removal of homologous predicted
interactions, 4,201 interaction edges accompanied by 2,666 protein
nodes were eventually utilized to construct the inguinal hernia PPI
networks ([98]Fig 2).
Table 1. Inguinal hernia-causative genes from OMIM.
Uniprot ID Gene Uniprot ID Gene Uniprot ID Gene Uniprot ID Gene
[99]P42345 MTOR [100]O75369 FLNB [101]P53667 LIMK1 [102]P49918 CDKN1C
[103]Q02809 PLOD1 [104]Q7Z494 NPHP3 [105]P08123 COL1A2 [106]P19544 WT1
[107]P98160 HSPG2 [108]P58012 FOXL2 [109]P13569 CFTR [110]O95967 EFEMP2
[111]P60953 CDC42 [112]O00469 PLOD2 [113]P43694 GATA4 [114]P50454
SERPINH1
[115]Q8TAD8 SNIP1 [116]Q8NEZ3 WDR19 [117]P13497 BMP1 [118]O60706 ABCC9
[119]Q04721 NOTCH2 [120]P16234 PDGFRA [121]P11362 FGFR1 [122]P01116
KRAS
[123]P35354 PTGS2 [124]Q86XX4 FRAS1 [125]Q8WW38 ZFPM2 [126]P02458
COL2A1
[127]P00797 REN [128]Q99697 PITX2 [129]P07951 TPM2 [130]P13647 KRT5
[131]O95259 KCNH1 [132]Q9NQX1 PRDM5 [133]Q01974 ROR2 [134]Q16671 AMHR2
[135]P61812 TGFB2 [136]P27986 PIK3R1 [137]P37058 HSD17B3 [138]Q06124
PTPN11
[139]Q07889 SOS1 [140]Q16637 SMN1 [141]P36897 TGFBR1 [142]Q5SZK8 FREM2
[143]P22888 LHCGR [144]P50443 SLC26A2 [145]Q13285 NR5A1 [146]Q9Y625
GPC6
[147]Q12805 EFEMP1 [148]A1X283 SH3PXD2B [149]P20908 COL5A1 [150]O95455
TGDS
[151]P02461 COL3A1 [152]O95450 ADAMTS2 [153]P52895 AKR1C2 [154]P12644
BMP4
[155]P05997 COL5A2 [156]P35916 FLT4 [157]P17516 AKR1C4 [158]P10600
TGFB3
[159]O14793 MSTN [160]Q99519 NEU1 [161]P07949 RET [162]Q9UBX5 FBLN5
[163]O00755 WNT7A [164]P22105 TNXB [165]P36894 BMPR1A -- SRS
[166]P37173 TGFBR2 [167]Q9ULC3 RAB23 [168]P54886 ALDH18A1 -- DIH2
[169]P16278 GLB1 [170]Q9NWM8 FKBP14 [171]Q02962 PAX2 -- ICR1
[172]Q9BWF2 TRAIP [173]Q8N8U9 BMPER [174]P05093 CYP17A1 -- H19
[175]P41221 WNT5A [176]P15502 ELN [177]P01112 HRAS
[178]Open in a new tab
-- means no-Uniprot-ID
Fig 2. PPI networks related to inguinal hernia with 4,201 interactions and
2,666 protein nodes.
[179]Fig 2
[180]Open in a new tab
Red circles represent protein nodes and blue lines indicate the
protein-protein interactions.
Cluster analysis
To further determine inguinal hernia-causative proteins based on the
overlapping clusters (cliques) in the inguinal hernia PPI networks,
cluster analysis was conducted using the ClusterOne plug-in of the
CFinder 2.0.6 software. The clusters with densely connected nodes in
the inguinal hernia PPI network were detected. The numbers of clusters
were 784, 245, and 62, which corresponded to the clique percolation
parameter k = 3, 4, and 5, respectively. A network diagram of clusters
at k = 4 was shown in [181]Fig 3. The overlapping cluster numbers for a
protein that participated in clusters are shown in [182]Table 2.
PIK3R1, PTPN11, SOS1, TGFBR1, TGFBR2, CDC42, KRAS, HRAS, RET, and
PDGFRA were listed as the top ten proteins based on the overlapping
cluster number of hernia-causative genes, in which PIK3R1 and PTPN11
were significantly involved in the inguinal hernia PPI network.
Fig 3. The clusters of inguinal hernia-causative genes in the PPI network.
[183]Fig 3
[184]Open in a new tab
245 clusters at k = 4. The yellow core clusters are defined by
significant involvement ranking calculated in [185]Table 2 using the
Thompson Tau test.
Table 2. Top 20 inguinal hernia-causative proteins based on the number of
overlapping clusters.
Rank Uniprot Protein Cluster # Rank Uniprot Protein Cluster #
1 [186]P27986 PIK3R1 207[187]^** 11 [188]P11362 FGFR1 23
2 [189]Q06124 PTPN11 127[190]^* 12 [191]P0CG48 UBC 19
3 [192]Q07889 SOS1 101 13 [193]O75369 FLNB 16
4 [194]P36897 TGFBR1 68 14 [195]Q13285 NR5A1 14
5 [196]P37173 TGFBR2 56 15 [197]P43694 GATA4 11
6 [198]P60953 CDC42 50 16 [199]P61812 TGFB2 11
7 [200]P01116 KRAS 40 17 [201]P62993 GRB2 11
8 [202]P11112 HRAS 37 18 [203]P36894 BMPR1A 9
9 [204]P07949 RET 26 19 [205]P10600 TGFB3 8
10 [206]P16234 PDGFRA 23 20 [207]P35916 FLT4 8
[208]Open in a new tab
* > mean ± (τ·SD)/2
** > mean ± τ·SD
Detecting essential proteins
Four topological features (i.e., DC, CC, BC, and EC) were calculated
for identifying the essential proteins in the inguinal hernia PPI
network. DC, CC, BC, and EC were weighted equally when calculating the
essential proteins. The top 20 of those essential proteins were ranked
and shown in [209]Table 3. PIK3R1 ([210]P27986), CDC42 ([211]P60953),
CTFR ([212]P13569), TGFBR1 ([213]P36897), and PTPN11 ([214]Q06124) were
the top five proteins with visibly higher DC. These results suggest the
importance and extensive involvement of these proteins in inguinal
hernia pathogenesis. In addition, the top five proteins with higher CC
were UBC ([215]P0CG48), PIK3R1 ([216]P27986), CDC42 ([217]P60953),
TGFBR1 ([218]P36897), and PTPN11 ([219]Q06124); with higher BC were
PIK3R1 ([220]P27986), UBC ([221]P0CG48), CDC42 ([222]P60953), CTFR
([223]P13569), and TGFBR1 ([224]P36897); and with higher EC were PIK3R1
([225]P27986), PTPN11 ([226]Q06124), CDC42 ([227]P60953), TGFBR1
([228]P36897), and SOS1 ([229]Q07889). Consequently, PIK3R1, CDC42, and
TGFBR1 were always listed in the top five proteins for all topological
categories, and PTPN11 was listed in the top five proteins under three
topological categories (DC, CC, and EC). UBC was listed as the first
and second protein in CC and BC, respectively. CTFR was listed as the
third and the fourth protein under two topological categories (DC and
BC), and SOS1 was listed as the fifth protein in the topological
category EC. Thus, proteins PIK3R1, CDC42, TGFBR1, PTPN11, UBC, CTFR,
and SOS1 may play an important role in the inguinal hernia PPI network.
Table 3. Top 20 inguinal hernia-causative proteins ranked by DC, CC, BC, and
EC.
Rank Uniprot DC Uniprot CC Uniprot BC Uniprot EC
1 [230]P27986 345 [231]P0CG48 0.4685 [232]P27986 1419876.10 [233]P27986
0.4584
2 [234]P60953 294 [235]P27986 0.4210 [236]P0CG48 1182660.50 [237]Q06124
0.2933
3 [238]P13569 264 [239]P60953 0.3880 [240]P60953 1163367.80 [241]P60953
0.2296
4 [242]P36897 264 [243]P36897 0.3867 [244]P13569 1051267.10 [245]P36897
0.1914
5 [246]Q06124 241 [247]Q06124 0.3778 [248]P36897 1025756.25 [249]Q07889
0.1813
6 [250]Q16637 226 [251]P37173 0.3737 [252]Q16637 906896.44 [253]P01116
0.1387
7 [254]P42345 191 [255]Q07889 0.3732 [256]Q06124 789004.06 [257]P01112
0.1352
8 [258]P01116 173 [259]Q13501 0.3689 [260]P42345 636229.44 [261]P0CG48
0.1323
9 [262]P01112 138 [263]P01112 0.3632 [264]P01116 589670.75 [265]P37173
0.1093
10 [266]P13647 126 [267]P01116 0.3617 [268]P01112 426118.50 [269]P07949
0.0993
11 [270]Q07889 126 [271]P62993 0.3617 [272]P13647 395877.22 [273]P13569
0.0989
12 [274]P50454 96 [275]P16234 0.3560 [276]Q07889 322209.06 [277]P42345
0.0963
13 [278]O75369 88 [279]P07949 0.3552 [280]P50454 301589.00 [281]P62993
0.0943
14 [282]P37173 87 [283]P22681 0.3549 [284]O95967 278135.72 [285]P11362
0.0859
15 [286]Q13285 69 [287]P11362 0.3541 [288]O75369 269789.28 [289]O75369
0.0835
16 [290]O95967 68 [291]P13569 0.3538 [292]Q13285 231566.27 [293]P16234
0.0804
17 [294]P11362 64 [295]O75369 0.3524 [296]P37173 212951.39 [297]P22681
0.0756
18 [298]P36894 62 [299]Q16637 0.3490 [300]Q8TAD8 178868.39 [301]P35222
0.0693
19 [302]P54886 58 [303]Q13285 0.3485 [304]P11362 172819.90 [305]Q16637
0.0691
20 [306]Q8TAD8 56 [307]P49841 0.3482 [308]P36894 156695.28 [309]P12931
0.0666
[310]Open in a new tab
As expected, we observed different proteins present under various
topological features, because each topological feature depicts only
certain features of the PPI network and cannot include the entire
topological information of the PPI network. Thus, we further identified
the essential proteins in the PPI network using a comprehensive edge
clustering coefficient (ECC) algorithm. The ECC method considers both
the clustering characteristics and the topological features of a
protein. Because the shape of the inguinal hernia PPI network is
similar to a star topology as shown in [311]Fig 1, DC is more suitable
to find essential proteins. As a result, the topological feature of
harmonic centrality (HC) calculating from the ECC algorithm was used to
further define DC. The essential proteins ranked by HC are shown in
[312]Fig 4, in which PIK3R1, PTPN11, TGFBR1, CDC42, SOS1, and KRAS are
shown as the significantly enriched top-ranking hub proteins in the
inguinal hernia PPI network using the Thompson Tau test. Together, our
results show that PIK3R1, PTPN11, TGFBR1, CDC42, and SOS1 are probably
the most essential proteins involved in human hernia formation.
Fig 4. Inguinal hernia-related essential proteins ranked by HC.
[313]Fig 4
[314]Open in a new tab
* > mean ± (τ·SD)/2, ** > mean ± τ·SD.
Gene ontology and pathway enrichment analysis
We performed functional enrichment analysis including GO term
enrichment and KEGG pathway analysis on 784 clusters, k = 3 of the
inguinal hernia PPI network. In GO term analysis, three categories were
used including biological processes, cellular components, and molecular
function. The top ten GO terms in three categories are described in
[315]Table 4 with p values < 0.01. The top seven significant terms in
the biological processes category were peptidyl-tyrosine
phosphorylation, transmembrane receptor protein tyrosine kinase (RTK)
signaling pathway, vascular endothelial growth factor (VEGF) receptor
signaling pathway, transforming growth factor beta (TGFβ) receptor
signaling pathway, signal transduction, MAPK cascade, and regulation of
phosphatidylinositol 3-kinase (PI3K) signaling. The Jak-STAT signaling
pathway, insulin signaling pathway, fibroblast growth factor (FGF)
receptor signaling pathway, and estrogen signaling pathway were also
significantly enriched in this category of the GO term analysis
([316]S1 Table). The most significant term was cytosol (p = 4.96E-37)
in the cellular component category. The top five significant terms in
the molecular function category were protein binding, protein tyrosine
kinase activity, transmembrane RTK activity,
phosphatidylinositol-4,5-bisphosphate 3-kinase activity, and protein
kinase binding. These results show that the signaling pathways of
growth factors, insulin, estrogen, transmembrane RTK, MAPK, and PI3K
may play a vital role in the pathogenesis of inguinal hernia.
Table 4. The most significantly enriched GO terms.
Category Terms P-value
Biological Process peptidyl-tyrosine phosphorylation 2.89E-41
transmembrane receptor protein tyrosine kinase signaling pathway
1.64E-32
vascular endothelial growth factor receptor signaling pathway 8.75E-31
transforming growth factor beta receptor signaling pathway 3.33E-30
signal transduction 2.35E-29
MAPK cascade 2.77E-29
regulation of phosphatidylinositol 3-kinase signaling 4.29E-28
phosphatidylinositol-mediated signaling 9.76E-27
epidermal growth factor receptor signaling pathway 3.87E-26
positive regulation of GTPase activity 1.39E-25
Cellular Component cytosol 4.96E-37
plasma membrane 5.80E-32
focal adhesion 3.01E-29
cell-cell junction 3.82E-20
receptor complex 5.36E-19
membrane raft 5.48E-17
extrinsic component of cytoplasmic side of plasma membrane 5.49E-16
cytoplasm 1.07E-14
cell-cell adherens junction 1.49E-14
cell surface 1.81E-13
Molecular Function protein binding 6.13E-56
protein tyrosine kinase activity 6.68E-40
transmembrane receptor protein tyrosine kinase activity 2.11E-29
phosphatidylinositol-4,5-bisphosphate 3-kinase activity 4.31E-28
protein kinase binding 1.07E-24
Ras guanyl-nucleotide exchange factor activity 8.92E-22
protein phosphatase binding 1.03E-21
ATP binding 8.75E-21
phosphatidylinositol-3-kinase activity 3.34E-16
phosphatidylinositol 3-kinase binding 2.85E-15
[317]Open in a new tab
The KEGG pathway enrichment analysis revealed the significantly
enriched genes in the enriched pathways ([318]S1 Table). The top 15
enriched pathways are shown in [319]Table 5. The most significantly
changed pathway was proteoglycans in cancer. The next two pathways were
pathways in cancer and the ErbB signaling pathway. In addition, the
PI3K-Akt signaling pathway, MAPK signaling pathway, insulin signaling
pathway, VEGF signaling pathway, TGFβ signaling pathway, Jak-STAT
signaling pathway, and estrogen signaling pathway were listed as the
15^th, 26^th, 34^th, 36^th, 40^th, 48^th, and 50^th significantly
enriched pathways, respectively ([320]S1 Table). All of these pathways
are related to cell growth and proliferation. Among the top 15 enriched
pathways, essential proteins PIK3R1, PTPN11, TGFBR1, CDC42, and SOS1
were involved in 14, 5, 5, 9, and 11 pathways, respectively ([321]Table
5).
Table 5. Most significantly enriched pathways determined by the KEGG pathway
enrichment analysis and the involvement of top five essential proteins in the
top 15 enriched pathways.
Terms P-value Top five essential proteins
PIK3R1 PTPN11 TGFBR1 CDC42 SOS1
Proteoglycans in cancer 1.12E-41 + + - - +
Pathways in cancer 5.98E-41 + - + + +
ErbB signaling pathway 9.34E-33 + - - - -
Focal adhesion 9.72E-32 + - - + +
Ras signaling pathway 1.00E-31 + + - + +
Rap1 signaling pathway 2.68E-31 + - - + -
Chronic myeloid leukemia 1.37E-30 + + + - +
Pancreatic cancer 1.56E-26 + - + + -
Neurotrophin signaling pathway 1.35E-25 + + - + +
Adherens junction 7.97E-24 - - + + -
Renal cell carcinoma 9.88E-24 + + - + +
Fc epsilon RI signaling pathway 4.25E-23 + - - - +
Regulation of actin cytoskeleton 4.57E-23 + - - + +
FoxO signaling pathway 1.18E-22 + - + - +
PI3K-Akt signaling pathway 2.54E-22 + - - - +
[322]Open in a new tab
Common downstream proteins
To investigate how these functionally diverse pathogenic proteins led
to inguinal hernia formation, we further analyzed inguinal hernia PPI
profiles using the DBFS algorithm to identify common downstream
proteins of 79 inguinal hernia-causative proteins ([323]Table 1). The
top 21 common downstream proteins are shown in [324]Table 6, in which
UBC, GRB2, CTNNB1, HSP90AA1, PLCG1, CBL, and CRK were listed as the top
7 common downstream proteins ([325]Fig 5). Most importantly, UBC, GRB2,
and CTNNB1 were significantly enriched in the inguinal hernia PPI
network. ([326]Table 6).
Table 6. Top 21 common downstream proteins defined by the number of direct
interactions with inguinal hernia-causative proteins.
Rank Uniprot Protein Interacting # Rank Uniprot Protein Interacting #
1 [327]P0CG48 UBC 50[328]^** 11 [329]O00459 PIK3R2 8
2 [330]P62993 GRB2 19[331]^* 12 [332]P00533 EGFR 8
3 [333]P35222 CTNNB1 12[334]^* 13 [335]P05067 APP 8
4 [336]P07900 HSP90AA1 10 14 [337]P45983 MAPK8 8
5 [338]P19174 PLCG1 10 15 [339]P84022 SMAD3 8
6 [340]P22681 CBL 10 16 [341]Q13547 HDAC1 8
7 [342]P46108 CRK 10 17 [343]P01137 TGFB1 7
8 [344]P12931 SRC 9 18 [345]P12830 CDH1 7
9 [346]P29353 SHC1 9 19 [347]P40763 STAT3 7
10 [348]P49841 GSK3B 9 20 [349]Q03135 CAV1 7
21 [350]Q15796 SMAD2 7
[351]Open in a new tab
* > mean ± (τ·SD)/2
** > mean ± τ·SD
Fig 5. The detailed interactive profiles of top 7 common downstream protein
of inguinal hernia-causative proteins.
[352]Fig 5
[353]Open in a new tab
These common downstream proteins, UBC (A), GRB2 (B), CTNNB1 (C),
HSP90AA1 (D), PLCG1 (E), CBL (F), and CRK (G), are highlighted in
purple. The green proteins directly interacted with purple common
downstream proteins. Blue proteins are the secondary contact proteins
to purple common downstream proteins. The shortest distance between
blue proteins and purple protein is 2.
Discussion
Inguinal hernia is a multifactorial disease caused by endogenous
factors including age, gender, anatomic variations, and inheritance as
well as exogenous factors such as smoking, comorbidity, and outcomes
from surgery [[354]14]. Recently, we found that the conversion of
testosterone to estradiol by the aromatase enzyme in lower abdominal
muscle tissue in a humanized aromatase transgenic mouse model activates
pathways for fibroblast proliferation and fibrosis, leading to intense
lower abdominal muscle fibrosis, muscle atrophy, and inguinal hernia;
fortunately, an aromatase inhibitor entirely prevents this phenotype
[[355]49]. In the present study, we explored the inherited aspects of
inguinal hernia formation via data mining of inguinal hernia-causative
genes exported from the OMIM database. Five essential proteins (PIK3R1,
PTPN11, TGFBR1, CDC42, and SOS1) and three downstream common proteins
(UBC, GRB2, and CTNNB1) were found to be related to inguinal hernia
development. We also found that the signaling pathways of growth
factors, transmembrane RTK, MAPK, and PI3K are highly associated with
inguinal hernia disease. Furthermore, this data mining technique can be
utilized for the analysis of the PPI networks of various human diseases
to identify critical essential proteins contributing to human diseases.
The RTK pathways were shown in the biological process category of
enriched GO terms including signaling pathways for VEGF receptor, TGFβ
receptor, epidermal growth factor receptor, and insulin. The common
downstream signaling of the RTK pathways such as the MAPK cascade and
PI3K signaling was also found in the GO biological process analysis
[[356]50]. Furthermore, these growth factor-mediated RTK pathways were
listed in the top 40 significantly enriched pathway via the KEGG
pathway enrichment analysis. The GO term analysis also indicated that
protein tyrosine kinase activity, transmembrane RTK activity,
phosphatidylinositol-4,5-bisphosphate 3-kinase activity, as well as
binding and activity of PI3K were related to human inguinal hernia
diseases. Additionally, others also show that inguinal hernia-related
essential proteins, PTPN11, CDC42, and SOS1 regulate the RAS/MAPK
signaling pathway, which is another downstream effector of RTKs
[[357]51–[358]54]. Furthermore, essential proteins, PIK3R1 and TGFBR1
are involved in the regulation of both the PI3K/AKT and RAS/MAPK
signaling pathways [[359]55–[360]57]. Together, all of these analyses
suggest that the RTK pathways such as the PI3K and MAPK pathways may
play a critical role in the development of inguinal hernias.
To further reveal the genetic mechanism behind inguinal hernia, the
DBFS algorithm was used to detect a series of common downstream
proteins that directly interacted with inguinal hernia-causative
proteins, in which UBC, GRB2, CTNNB1, HSP90AA1, PLCG1, CBL, and CRK are
listed as the top seven downstream common proteins. Ubiquitin C,
encoded by the UBC gene, maintains the cellular ubiquitin levels during
stress. Protein ubiquitylation plays a key role in the regulation of
multiple cellular events including the recognition of interacting
proteins [[361]58]. It is not surprising that UBC was highly positioned
on the list of the downstream proteins related to inguinal
hernia-causative proteins. It was also the highest ranked protein
measured by CC and BC and may not have many direct neighbors as
measured by DC. According to the previous study [[362]28], if a node
had low DC and high BC and EC, it would locate “centrality”, where the
exchange and transition of multitudes of data and resources allow the
centrality node to arrive prior to the other nodes in the PPI network
because of its short-distance path. Thus, UBC is likely to be adjacent
to numerous inguinal hernia causative proteins. Furthermore, Akt
ubiquitination plays an important role in the activation of Akt
signaling [[363]59]. GRB2 (Growth factor receptor-bound protein 2)
connects RTKs to RAS, leading to the activation of the RAS/MAPK pathway
[[364]60]. Another common downstream protein, CTNNB1 (Beta-catenin) is
an essential part of the Wnt signaling pathway and is regulated by the
PI3K/Akt pathways [[365]61]. HSP90AA1 (heat shock protein 90 alpha
family class A member 1) promotes autophagy and inhibits apoptosis
through the PI3K/Akt/mTOR pathway [[366]62]. PLCG1 (Phospholipase C,
gamma 1) can be activated by RTKs [[367]63]. CBL is an E3
ubiquitin-protein ligase involved in the formation of a covalent bond
between ubiquitin and RTKs, leading to RTK protein ubiquitination and
downregulation [[368]64]. CRK is an adapter protein that binds to
several tyrosine-phosphorylated proteins [[369]65]. As discussed above,
all of the top seven common downstream proteins are linked to the RTK
pathways.
A major downstream mediator of RTKs, PI3K is a family of related
intracellular signal transducer enzymes involved in cell growth,
proliferation, differentiation, motility, survival, and intracellular
trafficking [[370]66]. It can be stimulated by diverse oncogenes and
growth factor receptors such as receptors for insulin, insulin-like
growth factor 1, TGFβ, VEGF, and platelet-derived growth factor.
Estrogen can also up-regulate PI3K signaling [[371]67]. The PI3K family
is divided into three different classes (I, II, and III)[[372]68].
Class IA PI3K is a heterodimeric enzyme containing a p85 regulatory and
a p110 catalytic subunit. In the basal state, the interaction between
p85 and p110 stabilizes and inhibits p110 catalytic activity. Upon
activation by growth factors or other signals, ligand binding to RTKs
promotes receptor activation. The p85 subunit binds to activated RTKs,
recruiting p110 to the plasma membrane, where a conformational change
induced by binding relieves inhibition of p110 catalytic activity.
Three genes, PIK3R1, PIK3R2, and PIK3R3, encode the p85α, p85β, and
p55γ isoforms of the p85 regulatory subunit of PI3K, respectively. The
PIK3R1 gene also gives rise to two shorter isoforms, p55α and p50α,
through alternative transcription-initiation sites [[373]50, [374]66].
Interestingly, we found that PIK3R1 was listed as the first essential
protein in relation to inguinal hernia disease. A previous study
demonstrated that heterozygous mutations of PIK3R1(R649W) and the
resultant impairment of the PI3K activation have been identified in
patients with SHORT syndrome—a disorder characterized by Short stature,
Hyperextensibility of joints and/or inguinal hernia, Ocular depression,
Reiger anomaly and Teething delay [[375]69]. This finding further
emphasizes that PIK3R1 in the PI3K pathway is closely associated with
the development of inguinal hernia.
Inguinal hernia formation is associated with increased lower abdominal
muscle tissue fibrosis and muscle atrophy [[376]49]. The exact role of
these essential and common downstream proteins and the related
signaling pathway in fibroblasts and myocytes of lower abdominal muscle
tissue is unknown. Future studies will reveal the underlying molecular
mechanisms for these proteins and pathways in fibroblast proliferation
and fibrosis, myocyte function, and hernia formation and further target
these essential and common downstream proteins for developing novel
pharmacological approaches for preventing and treating recurrent
inguinal hernia in high risk individuals. Our data along with previous
findings regarding the effect of inhibitory mutation of PIK3R1 on SHORT
syndrome-associated inguinal hernia indicate that the PI3K pathway,
especially the essential protein, PIK3R1 is necessary for inguinal
hernia development. A previous study showed that direct targeting of
PIK3R1 in hepatic stellate cells inhibits liver fibrosis, indicating
that PIK3R1 is probably participating in hernia-associated fibrosis
[[377]70]. In addition, overexpression of pik3r1 in rat myotubes
reduced insulin-stimulated PI3K/AKT activation [[378]71], and pik3r1
overexpression in mice decreased skeletal muscle insulin signaling
[[379]72]. Mice lacking both pik3r1 and pik3r2 in skeletal muscles
exhibited severely impaired PI3K signaling in those muscles [[380]73].
These animals showed reduced myocyte size and insulin-resistance in
their skeletal muscles, demonstrating that in vivo class IA PI3K is
both a vital regulator of muscle growth and a critical mediator of
insulin signaling in the muscle. With these findings, we are planning
to selectively delete PIK3R1 in fibroblasts and/or myocytes to define
the relative roles of PIK3R1 in fibroblasts and myocytes for
maintaining abdominal muscle function and in pathologic processes such
as fibrosis, atrophy, and hernia formation.
In summary, the present study deeply analyzed the protein-protein
interaction on known inguinal hernia-causative genes from the OMIM
database. Several essential proteins and common downstream proteins
related to inguinal hernia diseases have been identified. The
downstream signaling pathways of activated RTKs have been found to be
highly associated with inguinal hernias. In the future, we will further
determine how these essential proteins and the RTK signaling pathways
such as the PI3K/Atk pathway contribute to the pathogenesis of the
inguinal hernia formation.
Supporting information
S1 Table. The GO term and KEGG pathway enrichment analysis on the 784
clusters, k = 3 of the inguinal hernia PPI network.
(XLSX)
[381]Click here for additional data file.^ (199.2KB, xlsx)
Data Availability
All relevant data are within the manuscript and its Supporting
Information files.
Funding Statement
The authors received no specific funding for this work.
References