Abstract
Myasthenia gravis (MG) is an autoimmune disease. In recent years,
considerable evidence has indicated that Gene Ontology (GO) functions,
especially GO-biological processes, have important effects on the
mechanisms and treatments of different diseases. However, the roles of
GO functions in the pathogenesis and treatment of MG have not been well
studied. This study aimed to uncover the potential important roles of
risk-related GO functions and to screen significant candidate drugs
related to GO functions for MG. Based on MG risk genes, 238 risk GO
functions and 42 drugs were identified. Through constructing a GO
function network, we discovered that positive regulation of NF-kappaB
transcription factor activity (GO:0051092) may be one of the most
important GO functions in the mechanism of MG. Furthermore, we built a
drug-GO function network to help evaluate the latent relationship
between drugs and GO functions. According to the drug-GO function
network, 5 candidate drugs showing promise for treating MG were
identified. Indeed, 2 out of 5 candidate drugs have been investigated
to treat MG. Through functional enrichment analysis, we found that the
mechanisms between 5 candidate drugs and associated GO functions may
involve two vital pathways, specifically hsa05332 (graft-versus-host
disease) and hsa04940 (type I diabetes mellitus). More interestingly,
most of the processes in these two pathways were consistent. Our study
will not only reveal a new perspective on the mechanisms and novel
treatment strategies of MG, but also will provide strong support for
research on GO functions.
Introduction
Myasthenia gravis (MG) is an autoimmune disease of chronic
neuromuscular disorder mainly caused by the antibodies against
nicotinic acetylcholine receptor (AChR) in the postsynaptic membrane
[[50]1]. The primary clinical manifestations of MG include fluctuating
muscle weakness and fatigue, which can range from mild forms affecting
only the eye muscles to severe generalized forms. Many studies have
elucidated the pathogenesis of MG [[51]2,[52]3]. With improved
diagnosis and prolonged survival, the prevalence of MG is growing in
recent years [[53]2,[54]4]. However, the current treatment strategies
have different degrees of side effects and none of them can completely
cure MG.
In recent years, researching gene networks has become a focus. Vitali
et al. constructed a protein-protein interaction (PPI) network to
explore the genetic underpinnings of wound healing mechanisms [[55]5].
Many researchers have also developed various algorithms to analyze or
identify the network functions of genes or gene products, such as MTGO
[[56]6] and DCAFP [[57]7], which provided great insight into the
research of genes or gene products. Gene ontology (GO) project provides
a set of comprehensive available resources on genes and gene products
[[58]8], which include concepts/classes to describe gene function and
annotation. The project focuses on the following three aspects:
molecular function (MF), cellular component (CC) and biological process
(BP). In recent years, GO-biological process (GO-BP) has been the focus
of multiple research projects. For example, while exploring autophagy
with GO database, Paul et al. found that different types of autophagy
require specific BP terms [[59]9]. According to a novel form of
network-based gene enrichment, Lena et al. proposed a more effective
method for detecting BPs associated diseases [[60]10], which may help
us to better understand the mechanism of different diseases if we can
determine the BPs of diseases. Another study has found that altered
genes in bladder neoplasm patients were mainly enriched for two classes
of BP through GO analysis, which suggests that these BPs may
participate in the onset of this disease or worsen the observed
phenotype [[61]11]. In addition, Wirapati et al. discovered that the
GO-BPs with high ‘coexpression’ genes could help to reveal the common
thread connecting molecular subtyping and several prognostic signatures
of breast cancer [[62]12]. These studies indicated that GO-BP may have
an important role in the initiation and progression of diseases.
However, the potential role of GO-BP in MG is still unclear.
It has been reported that using old drugs for new indications has
become an attractive form of drug discovery [[63]13] that can save time
and money compared to developing new drugs. For example, based on
widely functions of miRNA, a miRNA-regulated drug-pathway network was
constructed to recognize new treatment drugs for MG in our previous
work [[64]14,[65]15]. However, a disease may be caused by many
abnormally expressed genes, which in turn disturb the BPs that the
genes participated. In addition, drugs can bind to target genes and
influence the BPs in which the target genes are located. For instance,
Lee et al. developed an efficient and useful way to investigate the
relationships between BPs and side effects by building a
process-drug-side effect network [[66]16]. It seems that if we can
determine abnormal BPs that are affected by differentially expressed
genes, then some drugs that target those genes can be applied to offset
the anomalies caused by the BPs. This approach will provide a new
dimension for the treatment of diseases. Researchers have proposed a
method for drug repositioning based on disease-associated GO-BP
[[67]17]. Meanwhile, Porrelloa and Piergentilib identified new
potential therapeutic targets for bladder neoplasm by analyzing BP in
which altered genes were enriched [[68]11], which provided strong
support for screening new drugs based on GO-BP. However, no studies
have focused on the association between drugs and GO-BPs in MG.
In this study, as shown in the flowchart ([69]Fig 1), we identified
risk-related GO functions and recognized drugs based on MG risk genes.
Then, we constructed GO function network (GOFN) and drug-GO function
network (DGOFN). We found an important immune-related GO function and
revealed several new drug candidates for MG by calculating Z-value
between drugs and MG. Finally, we identified two risk pathways
regulated by MG risk genes and drugs, which might interact with GO
functions. These results may provide potential guidance for identifying
the mechanisms and treatments for MG.
Fig 1. Flowchart of methods.
Fig 1
[70]Open in a new tab
Step 1: MG risk genes were collected from current public databases.
Step 2: According to the cumulative hypergeometric distribution, we
identified statistically significant GO functions for MG. Step 3: Based
on MG risk genes, we acquired drugs related to MG. Step 4: Based on the
cumulative hypergeometric formula, we obtained statistically
significant GO function pairs and constructed a GOFN network. Step 5:
According to the cumulative hypergeometric formula, we obtained
statistically significant drug-GO function pairs and constructed a
DGOFN network. Step 6: According to the DGOFN, we calculated the
association scores (AS) and the specificity of the association and
dissected the mechanism between candidate drugs and GO functions in
pathways.
Data sources
Data for human MG risk genes
MG risk gene data were collected in two ways. For one thing, we
manually browsed 12,454 items by searching literature published before
March 1st, 2018 in PubMed database using the terms [myasthenia gravis
(MeSH Terms) and English (Language)], and then we selected eligible
genes. The selection criteria for an MG risk gene was consistent with
our previous studies published online [[71]18]: the risk gene was
significantly differently expressed in at least 5 MG samples (including
blood samples or thymic samples) and was detected using dependable
experimental methods (such as ELISA, RT-PCR and Western Blot). For
another thing, we also obtained MG risk gene data from three current
public databases, including Online Mendelian Inheritance in Man (OMIM)
(March 1st, 2017, [72]https://omim.org) [[73]19], the Genetic
Association Database (GAD) (September 1st, 2014,
[74]http://geneticassociationdb.nih.gov) [[75]20] and DisGeNET (version
5.0, [76]http://www.disgenet.org) [[77]21]. Finally, we collected 258
MG risk genes, including 144 risk genes obtained through a literature
search (detailed information in [78]S1 Table) and 114 risk genes
compiled from public databases (shown in [79]S2 Table)
Human gene ontology data
The information for GO annotation was download from GO database
([80]http://www.geneontology.org), with the species restricted to “Homo
sapiens”. Based on the genes we collected, we identified MG related GO
functions in GO level 3 (we selected GO-BPs to represent GO functions)
[[81]10,[82]14]. The GO functions with less than 5 MG risk genes were
excluded from this study.
Drugs and drug targets data
The data for drugs and their target genes were downloaded from DrugBank
database (version 5.1.1) [[83]22]. The species was limited to “Homo
sapiens”. By intersecting MG risk genes we collected with drug targets,
we obtained 464 drugs that might target MG. Then, we excluded drugs
with less than 3 target genes from this study. Finally, we obtained 43
drugs that targeted MG risk genes.
Methods
Human MG risk gene ontology annotation
We performed an enrichment analysis to annotate human MG risk genes
using the following formula:
[MATH: P=F(x|M,K,N)=∑i=0x(Ki)(
M−K<
mtd>N−i)(MN) :MATH]
(1)
At first, the enrichment analysis was applied for selecting GO
functions significantly related to MG. Using this approach, M denoted
the total number for the human whole genome, K denoted the number of
genes in a given GO function, N denoted the total number of MG risk
genes, and x represented the number of overlapping genes between GO
function and MG risk genes. Statistical significance was achieved if
the P-value was less than 0.05.
Construction of networks
The enrichment analysis (the formula ([84]1)) was also performed in the
construction of networks, including GO function network (GOFN) and
drug-GO function network (DGOFN). First, when analyzing every two
random GO function pairs, M represented the total number of the human
whole genome, N represented the total number of genes in one GO
function, K denoted the number of genes in another GO function, and x
was the number of overlapping genes between two GO functions. The
association between each GO function and all other GO functions was
analyzed. Similarly, for each drug-GO function pair, M denoted the
total number of human MG risk genes, K denoted the number of genes in a
given GO function, N denoted the number of target genes of a given
drug, and x represented the number of overlapping genes between GO
functions and drugs. After calculating the p-value between every random
GO function pair or drug-GO function pair, we adjusted the p-value
using the Benjamini and Hochberg false discovery rate (FDR) to
determine statistical significance. We considered a GO function pair
and a drug-GO function pair to be notably overlapping if the FDR was
less than 0.05 and constructed separately GOFN and DGOFN networks.
Next, Cytoscape 3.6.0 was used to visualize the networks.
Functional enrichment analysis in pathways
We carried out KEGG pathway enrichment analysis to identify MG risk
pathways that included significant candidate drug targets (MG risk
genes) that were enriched using the functional annotation tools in
DAVID [[85]23]. We defined an FDR value less than 0.05 as the cutoff.
Calculation of degree and betweenness
In the GOFN, the degree of a GO function was the number of the other GO
functions which were connected to the GO function. Similarly, the
degree of a GO function (or a drug) was the number of drugs (or GO
functions), which were connected to the GO function (or the drug) in
the DGOFN. For a GO function ‘v’ in GOFN, the betweenness of ‘v’ was
the sum of the numbers of the shortest paths between all pairs of GO
functions through the node ‘v’. In this study, we calculated the node
of betweenness by using the package igraph for R and the Network
Analysis plugin [[86]24] of Cytoscape was used to analyze the network
properties.
Association scores and screening significant drugs for MG
Based on the DGOFN, we calculated the association scores (AS) and the
specificity of the association by using the formulas from our previous
study [[87]14]. The formulas were as follows:
[MATH:
Sdrugi,MG=−
lg∑P
drugi,k<
/mrow>×PMG,<
/mo>k
:MATH]
(2)
[MATH:
Zdrugi,MG=Sdrugi<
/mi>,k−average(Sran<
/mi>dom,drugi)std(Sran<
/mi>dom,drugi) :MATH]
(3)
In formula ([88]2), P[drugi,k] was the P-value of drug ‘i’ enriched in
GO function ‘k’; P[MG,k] was the P-value of MG enriched in GO function
‘k’; and ‘k’ was used to represent the most significant GO function
affected by drug ‘i’ targets and MG risk genes. We obtained the S value
of each candidate drug after applying formula ([89]2). Next, to assess
the specificity of the association between drugs and MG, we conducted a
permutation of the GO functions and computed the Z scores of the drugs
and MG by using formula ([90]3). We also obtained the random GO
function profiles of the drugs by randomly ranking the GO function
10,000 times. For each random profile, S[random,drugi] was calculated
according to formula ([91]2); the average(S[random,drugi]) represents
the average association score between random cases and drug ‘i’; and
std(S[random,drugi]) represents the standard variation of association
between random cases and drug ‘i’. Z[drugi,MG] was the significant
score between drug ‘i’ and MG. The higher the Z-value, the more
significant the association between drug and MG was. The drugs can be
regarded as candidates for MG treatment if the Z-value >1.96 (P<0.05).
Validation using GEO dataset
The human microarray dataset [92]GSE85452 [[93]25] was downloaded from
the NCBI Gene Expression Omnibus ([94]www.ncbi.nlm.nih.gov/geo/). The
[95]GSE85452 microarray dataset was generated with the [96]GPL10558
platform (Illumina HumanHT-12 V4.0 expression beadchip) and included 13
MG patients and 12 controls. T-test was applied to identify the
differential expressed genes (P < 0.05, |fold change (FC)| >2).
According to a hypergeometric test, the overlap of MG risk genes and
differential expressed genes in [97]GSE85452 data were statistically
significant if the P-value was less than 0.05.
Results
Identification of MG-related GO functions
A catalog of 258 risk genes was created. In terms of these MG risk
genes, 238 risk GO functions (P<0.05) were identified (the detailed
information was summarized in [98]S3 Table). In this study, we mainly
concentrated on immune-related GO functions due to the complex
immunological mechanism of MG [[99]26]. A total of 20 immune-related GO
functions (P<0.05) were observed manually and were shown in [100]Fig
2A. Other types of GO functions, such as cell development, defense
response and tissue homeostasis, were shown in [101]S3 Table. MG has
been reported as a humoral immunity-mediated autoimmune disease.
Epstein-Barr virus (EBV) infection was observed in B cells and plasma
cells (PCs) in the thymus of patients with MG, which provided a
possible theoretical basis for the mechanism through which humoral
innate immunity induces autoimmunity in MG [[102]26,[103]27]. In
addition, increasing evidence indicated the modulation of immune
response and the presence of inflammation could contribute to MG
mechanism [[104]28,[105]29] which have shown that these GO functions
might exert potential important effects in the pathogenesis of MG.
Fig 2. Twenty GO functions of myasthenia gravis.
[106]Fig 2
[107]Open in a new tab
Twenty immune-related GO functions enriched by MG risk genes (P<0.05).
(B) Functional classification of MG risk genes in 20 immune-related GO
functions. (C) The frequency of MG risk genes in 20 immune-related GO
functions.
Sixty-six MG risk genes were included in the 20 immune-related GO
functions. According to the functional description of MG risk genes
enriched in 20 immune-related GO functions, these genes could be
predominantly grouped into 9 categories ([108]Fig 2B). We found that
most of the MG risk genes belonged to cytokines. It has been reported
that cytokines were likely to have major importance in the pathogenesis
of MG [[109]30]. At the same time, we also analyzed MG risk genes
located in 20 immune-related GO functions in a PPI network and obtained
a subnetwork ([110]S1 Fig). The degree distribution of nodes in the
subnetwork was displayed in [111]S2 Fig. We found that TGFB1 with the
highest degree was part of cytokines in [112]Fig 2B, which suggested
that MG risk genes played an important role in the PPI network and GO
functions. The risk genes in the 20 immune-related GO functions which
appeared more than once were shown in [113]Fig 2C. The more frequently
a gene appeared, the more widely it participated in GO functions, such
as Toll-like receptor 9 (TLR9) and tumor necrosis factor (TNF). For
example, Cavalcante et al. discovered that TLR9 affected by EBV might
cause the onset or maintenance of the autoimmune response in the
intrathymic pathogenesis of MG [[114]31]. These results suggest that
the GO functions we identified may be crucial components of the
mechanism of MG and provide a new direction for studying the
pathogenesis of MG.
Construction of GO function network and analysis of the network properties
Based on the links of the 238 GO functions, we constructed a GOFN
([115]Fig 3A). The network contained 238 nodes and 5167 interactions.
Fig 3. The associations among GO functions.
[116]Fig 3
[117]Open in a new tab
GO function network (GOFN) in MG. Blue triangles represent GO
functions; the size of the node represents the magnitude of the degree;
the edge represents the connection between the two GO functions. (B)
Degree distribution of all the nodes in the GOFN. (C) Node betweenness
in the GOFN. (D) The dissection between the GO function for the
positive regulation of NF-kappaB transcription factor activity and its
connected GO functions. Blue triangles represent GO functions; purple
circles represent MG risk genes; the interaction of two GO functions
via MG risk genes is shown in the figure.
We analyzed the topological characteristics of the network. First, the
degrees of the nodes in the GOFN were shown in [118]Fig 3B. A small
number of GO functions had high connectivity, such as the GO term of
positive regulation of NF-kappaB transcription factor activity
(GO:0051092), which had the highest degree among the immune-related GO
functions and meant that it was highly relevant to the other GO
functions it was connected to. Meanwhile, we also calculated the
betweenness of the nodes in GOFN ([119]Fig 3C). The higher the
betweenness of the node, the more important this node was in
maintaining tight connectivity in the network. Similarly, the GO term
of GO:0051092 had the highest betweenness in all of the immune-related
GO functions, which illustrated this GO term could have a wide range of
functions. It has been found that GO:0051092 might play a core role in
the GOFN network based on the analysis of the network properties,
therefore, to figure out the association between the GO term of
GO:0051092 and the other GO functions, a subnetwork was determined
([120]Fig 3D) by dissecting the GO term of GO:0051092 in depth. As
shown, the term was connected to 117 GO functions, and 79 of these GO
functions were connected through MG risk genes, which contained 15 MG
risk genes, including TRL9, TNF, EIF2AK2, IL6, AGER, IL1B, CD40,
TNFRSF11A, TGFB1, TLR3, TLR4, KRAS, INS and NTRK1. It has been reported
that NF-kappaB transcription factor can mediate inducible expression of
several genes involved in inflammatory immune responses and many other
BPs, which makes it a critical regulator of the inflammatory immune
response [[121]32,[122]33]. Our results were consistent with those of
previous studies.
Construction of drug-GO function network and analysis of its topological
features
To understand how drugs (data sources) affect the GO functions, we
identified 461 drug-GO function pairs (FDR value<0.05) and established
the DGOFN ([123]Fig 4A). According to the statistical analysis, 37 MG
risk genes were enriched in all the drug-GO function pairs (not shown
in the DGOFN). Meanwhile, 461 significant drug-GO function pairs
between 42 drugs and 117 GO functions, which were from 238 risk GO
functions, were contained in the network.
Fig 4. The relationship between drugs and GO functions.
[124]Fig 4
[125]Open in a new tab
(A) Drug-GO function network (DGOFN) in MG. Network organization of
drug and GO function associations. Green ‘V’ formations represent
drugs; blue triangles represent GO functions. The size of the node
represents the size of the degree; edge represents the connection
between drugs and GO functions. (B) Degree distribution for all nodes
in the DGOFN. (C) Degree distribution for the GO function nodes. (D)
Degree distribution for the GO nodes. (E) The top 10 drugs ranked by
drug degree (Polaprezinc, Minocycline, Apremilast, Zinc, Glucosamine,
Carvedilol, Pseudoephedrine, Regorafenib, Clenbuterol and Epinephrine).
To comprehend the topological features of the DGOFN in detail, we
characterized its degree distribution. The degree distribution of all
the nodes followed a power law distribution f(x) = 51.28x^−1.16 in the
DGOFN ([126]Fig 4B). We also determined the degree distribution of GO
functions and drugs in the DGOFN. The degree distribution of GO
functions was displayed in [127]Fig 4C, the degree of regulation of
immune response (GO:0050776) was 20, which was the highest degree in
all GO functions, i.e., 20 drugs could act on this GO function. In
addition, the GO term of GO:0051092 had the second highest degree of
all the immune-related GO functions. These results indicated that
immune-related GO functions may be important potential therapeutic
targets for MG. The degree distribution of drugs was shown in [128]Fig
4D, which suggested that most of the drugs can influence more than one
GO function. Furthermore, we demonstrated the top 10 drugs and the GO
functions associated with these drugs in [129]Fig 4E, which might
provide more options for the target therapy of MG.
MG candidate drugs and associated GO functions
According to the DGOFN, we calculated Z scores of drugs and MG
(methods). Five candidate drugs with Z-value >1.96 (P<0.05) were
identified, including Glucosamine, Apremilast, Adalimumab, Etanercept
and Polaprezinc. To verify the reliability of our results, we
downloaded the gene expression profile of MG ([130]GSE85452) from Gene
Expression Omnibus (GEO) database
([131]http://www.ncbi.nlm.nih.gov/geo/). By analyzing high-throughput
expression profile, we found that 1647 MG genes were differentially
expressed ([132]Fig 5A). The overlap of MG risk genes and
differentially expressed MG genes was statistically significant
(p<0.05) based on a hypergeometric test ([133]Fig 5B). Importantly, MG
risk gene-FCGR1A, which is the target of Adalimumab and Etanercept, was
shown to be differentially expressed in the expression profile of
[134]GSE85452. In fact, Adalimumab and Etanercept have been
investigated to treat MG. These findings further enhanced that our
results were reliable.
Fig 5. Venn diagram, heatmap and Layered networks.
[135]Fig 5
[136]Open in a new tab
A. Venn diagram of MG risk genes and differentially expressed MG genes;
light blue ellipse indicates differentially expressed MG genes, pink
ellipse indicates MG risk genes; purple intersection indicates
overlapping genes. B. Heatmap of overlap between MG risk genes and
differentially expressed MG genes; blue rectangles represent control
samples, purple rectangles represent MG samples. C. Layered networks,
including Glucosamine-GO functions network, Apremilast-GO functions
network, Adalimumab-GO functions network, Etanercept-GO functions
network and Polaprezinc-GO functions network; green round rectangles
indicate GO functions; purple ellipses indicate MG risk genes; orange
hexagons indicate candidate drugs.
In principle, the same drug can often be used to treat other diseases
that share the affected BPs. Therefore, we also confirmed the
reliability of our candidate drugs by consulting the relevant
literature in PubMed. Glucosamine, which is a common dietary
supplement, has immunosuppressive effects on autoimmune diseases
[[137]34,[138]35]. Chien et al. confirmed Glucosamine could reduce IL2
downstream signaling through downregulating IL2RA [[139]36], while IL2
and IL2RA (CD25) were upregulated in patients with MG, and IL2RA might
affect the clinical symptoms of MG [[140]37,[141]38]. These results
show that Glucosamine has the potential to be a therapeutic target for
MG. Apremilast is a novel inhibitor of phosphodiesterase 4 that has led
to great interest in targeted treatments for autoimmune diseases
[[142]39] It was reported that Apremilast has been approved for
psoriasis and psoriatic arthritis [[143]40]. Furthermore, Apremilast
can inhibit the generation of cytokines such as TNF, IL-2, CXCL10 and
CCL4 [[144]41], which are all MG risk genes. These studies suggest that
Apremilast is promising for treating MG.
Next, to more intuitively illustrate the relationship among MG risk
genes, 5 candidate drugs and the associated GO functions, we built
drug-GO function layered networks. We divided the GO functions
associated with drugs into immune-related functions and
immune-unrelated functions. Then, we selected the immune-related GO
functions to build layered networks ([145]Fig 5C). Researchers have
concluded that targeting NF-kappaB as well as its related signaling
pathways could be a potential therapeutic target for cancer treatments
[[146]42]. However, MG is mainly caused by thymoma and may also be
related to other kind of cancers, which implies that GO functions may
be key factors for the treatment of MG. We can see that GO term of
GO:0051092 was associated with all of the candidate drugs through TNF.
A study showed that TNF was one of the most important cytokines in the
mechanism of MG, and inhibiting TNF may exert notable clinical efficacy
for MG [[147]43], which indicates that TNF may be a significant
therapeutic target in the future. We summarized the information on
target genes among the 5 candidate drugs and immune-unrelated GO
functions in [148]S4 Table.
Mechanism dissection of MG candidate drugs and associated GO functions in
pathways
Finally, to investigate the underlying mechanisms between the 5
candidate drugs and the GO functions affected by these candidate drugs
in pathways, we performed KEGG pathway enrichment analysis based on the
target genes between the 5 candidate drugs and GO functions. As a
result, we identified 37 MG risk pathways (FDR value<0.05). Among these
pathways, we discovered that hsa05332 (graft-versus-host disease) and
hsa04940 (type I diabetes mellitus) were the two most statistically
significant pathways ([149]Fig 6). Myasthenic symptoms are frequently
associated with other symptoms of chronic graft-versus-host disease
(GVHD) and MG has been reported as a rare complication of chronic GVHD
after allogeneic hematopoietic stem cell transplantation
[[150]44–[151]46]. Additionally, it has been evidenced that the
development of type 1 diabetes increases the risk of other autoimmune
diseases and is related to genetic susceptibility for development of
these diseases [[152]47]. Consequently, these two pathways are closely
related to the pathogenesis of MG. Forty-two GO functions were involved
in the hsa05332 pathway, whereas 31 GO functions were associated with
the hsa04940 pathway. Immune-related GO functions participating in the
two pathways were displayed in [153]Fig 6 (the remaining GO functions
were shown in [154]S4 Table). As we can see, TNF, IL2 and INFG are the
common genes in both pathways. Because the two pathways were regulated
by the most of the same risk genes, the candidate drugs and associated
GO functions were almost similar. These results demonstrate that the GO
functions we identified are useful for discovering candidate drugs and
are extremely crucial in the mechanism of MG. Meanwhile, our results
will provide a new direction for clarifying the pathogenesis and
treatments of MG.
Fig 6. Dissection of mechanism between candidate drugs and GO functions in
pathways.
[155]Fig 6
[156]Open in a new tab
The rectangle with the red lines indicates hsa05332 (graft-versus-host
disease); the rectangle with the blue lines indicates hsa04940 (type I
diabetes mellitus). Yellow rectangles indicate MG risk genes; orange
hexagons indicate drugs; circles indicate GO functions.
Discussion
In this study, we have identified the potential mechanism of risk GO
functions based on the current knowledge of MG and screened significant
candidate drugs for MG for the first time. Through compiling the MG
risk gene catalog, we enriched MG risk GO functions. Furthermore, we
constructed the GOFN and demonstrated the importance of GO functions;
we also built the DGOFN and revealed candidate drugs that may affect
risk-related GO functions. Finally, we performed an enrichment analysis
in pathways and proposed a potential mechanism between GO functions and
drugs.
The GO functions we identified reveal an overview of MG pathogenesis.
They also provide strong support for further investigation of GO
functions. MG is an autoimmune disease, immune-related GO functions may
be more closed to the pathogenesis of MG. In the study, we mainly
focused on 20 immune-related GO functions. It has been reported that
immune response played important roles in the mechanism of MG
[[157]48,[158]49], which provide the basis for our study. Several
significant GO functions associated with MG were discovered, which may
contribute to the onset of MG. NF-kappaB was induced during B cells
maturation, therefore, it may have a major role in the activation and
the development of B cells [[159]50] while B cells are critical
contributors to the humoral immune response and can produce various
antibodies. In addition, NF-kappaB can activate cells of the innate
immune system when inflammation occurs [[160]51]. Once NF-kappaB is
triggered, a series of inflammatory immune responses may occur that may
accompany the production of antibodies, including AChR antibody and
thus induce MG. In addition, the classification of MG risk genes in
immune-related GO functions further highlighted the fundamental
characteristics of autoimmune MG.
The GOFN network demonstrated that all of the GO functions were
significantly correlated with some of the remaining GO functions.
According to the topological properties of GOFN, we further implicated
the potential significant role of GO:0051092 in MG. NF-kappaB is part
of a complex family of proteins that not only can mediate many crucial
biological functions through innate and adaptive immunity
[[161]50,[162]52], but can also influence the expression of genes
participating in inflammatory immune responses. For example, miR-26 can
downregulate IL6 production through silencing the expression of MALT1
and HMGA1, while MALT1 and HMGA1 are two proteins with vital functions
in NF-kappaB [[163]53]. By dissecting GO:0051092 in depth, we
discovered that this GO term did interact with other GO functions
through some important MG risk genes. Therefore, we speculated that
GO:0051092 was one of the most important GO functions in MG.
While building the GOFN network, we also built the DGOFN network to
identify significant candidate drugs related to GO functions. The
significant candidate drugs we acquired were related to MG or other
autoimmune diseases, supported by related literature. The effects of
such potential discoveries are broad because they might lead to
accurate targeted therapeutics and individual treatments. Our previous
study considered the pathways that were enriched by MG risk genes and
miRNAs [[164]14], whereas we mainly focused on the GO functions related
MG to identify MG candidate drugs for this study. Starting from a
different perspective, we again recognized several significant
candidate drugs for MG. Our results further illuminate that GO
functions may have more prospects for research on MG pathogenesis and
for screening candidate drugs for diseases. However, well-designed
experiments are still essential to confirm whether these drugs can be
used to treat MG. Nonetheless, our research will serve as an important
complement to future experimental studies of GO functions and drugs in
MG, particularly because of the lack of exploration in this field to
date.
In conclusion, we compiled a catalog of MG risk genes and identified
risk GO functions, drugs and risk pathways. We constructed a GOFN to
help understand the association between GO functions. We also
investigated the complex connection among MG risk genes, drugs and GO
functions by constructing a DGOFN and we identified 5 candidate drugs.
Furthermore, we dissected the regulatory mechanism of candidate drugs
and associated GO functions in risk pathways. Our results may provide
strong support and new viewpoint for further research on the mechanisms
and treatments of MG.
Supporting information
S1 Fig. The PPI subnetwork of MG risk genes located in 20
immune-related GO functions; blue circles represents genes.
(TIF)
[165]Click here for additional data file.^ (1.7MB, tif)
S2 Fig. The degree distribution of nodes in the PPI subnetwork.
(TIF)
[166]Click here for additional data file.^ (247.8KB, tif)
S1 Table. MG risk genes obtained from a literature search.
(DOC)
[167]Click here for additional data file.^ (244.1KB, doc)
S2 Table. MG risk genes downloaded from three current databases.
(DOC)
[168]Click here for additional data file.^ (139.5KB, doc)
S3 Table. Detailed information on 238 GO functions.
(XLS)
[169]Click here for additional data file.^ (77.5KB, xls)
S4 Table. Target genes between 5 candidate drugs and GO functions.
(DOC)
[170]Click here for additional data file.^ (98.5KB, doc)
Acknowledgments