Abstract
Gestational diabetes mellitus (GDM) is a condition associated with the
onset of abnormal glucose tolerance during pregnancy. Long non-coding
RNAs (lncRNAs), microRNAs (miRNAs), and genes can form lncRNA-mediated
feedforward loops (lnc-FFLs), which are functional network motifs that
regulate a wide range of biological processes and diseases. However,
lnc-FFL network motifs have not been systematically investigated in
GDM, and their role in the disease remains largely unknown. In the
present study, a global lnc-FFL network was constructed and analyzed.
Glycometabolism- and hormone-related lnc-FFL networks were extracted
from the global network. An integrated algorithm was designed to
identify dysregulated glycometabolism- and hormone-related lnc-FFLs in
GDM. The patterns of dysregulated lnc-FFLs in GDM were complex.
Moreover, there were strong associations between dysregulated
glycometabolism- and hormone-related lnc-FFLs in GDM. Core modules were
extracted from the dysregulated lnc-FFL networks in GDM and showed
specific and essential functions. In addition, dysregulated lnc-FFLs
could combine with ceRNAs and form more complex modules, which could
play novel roles in GDM. Notably, we discovered that the dysregulated
lnc-FFLs were enriched in the thyroid hormone signaling pathway. Some
drug-repurposing candidates, such as hormonal drugs, could be
identified based on lnc-FFLs in GDM. In summary, the present study
highlighted the effect of dysregulated glycometabolism- and
hormone-related lnc-FFLs in GDM and revealed their potential for the
discovery of novel biomarkers and therapeutic targets for GDM.
Keywords: lncRNA-mediated feedforward loop, glycometabolism, hormone,
gestational diabetes, drug repurposing, thyroid hormone
Introduction
Gestational diabetes mellitus (GDM), or glucose intolerance with first
onset and recognition in pregnancy, is a common disease among pregnant
women ([35]1). Although the diagnostic criteria and the optimal
protocol for detection and treatment of GDM are under debate, there is
universal recognition that GDM can increase the risk for type 2
diabetes ([36]2). Patients with GDM usually develop type 2 diabetes at
a relatively younger age (<40 years) than women without GDM, have a
higher risk of cardiovascular diseases, nonalcoholic fatty liver
disease, as well as renal disease, and exhibit higher rates of early
mortality ([37]3, [38]4). Among the risk factors for GDM are
pre-pregnancy weight gain and obesity, a family history of diabetes, an
advanced maternal age, a poor diet, and low physical activity ([39]5).
While GDM is considered to stem from a diminished capacity of the
pancreas to produce sufficient insulin and an impaired insulin action
related to pregnancy, the detailed and global mechanism causing GDM
remain uncertain.
Marked changes occur during maternal metabolism, and many of these are
essential for healthy fetal growth and development. However, they may
also lead to GDM and other diseases if there is a substantial deviation
from the physiological gestational levels ([40]6, [41]7). Pregnant
women with GDM generally exhibit metabolic abnormalities, with
alterations in lipid, glucose, and carbohydrate metabolism ([42]8).
Fetal exposure to hyperglycemia during a critical developmental period
may have long-term effects on the fetus by creating a metabolic memory,
also known as fetal programming ([43]9). Certain hormones, such as
progestogens, estrogens, and androgens, are essential for the
successful establishment and maintenance of pregnancy and the proper
development of the fetus. Hormone synthesis is generally abnormal in
GDM patients ([44]10). The thyroid hormone is an essential hormone for
many biological processes and plays import roles in glucose metabolism
and homeostasis. It has been suggested that thyroid hormone
abnormalities play a role in the etiology of GDM ([45]11). Although
there are strong associations between glycometabolism, hormones, and
GDM, the details of the mechanism are unknown.
Recently, genetic studies have focused on long non-coding RNAs
(lncRNAs), which are defined as non-coding RNAs more than 200
nucleotides in length ([46]12). lncRNAs are known to play a vital role
in cellular development and many biological process and diseases
([47]13–[48]15). A previous study has suggested that circulating lncRNA
could serve as a fingerprint for GDM, and it is associated with a
macrosomia risk ([49]16). The expression of lncRNA MALAT1 could offer a
novel biomarker for predicting GDM ([50]17). Genes, microRNAs (miRNAs),
and their shared target lncRNAs can form lncRNA-associated feedforward
loops (lnc-FFLs), in which genes and miRNAs co-ordinate to regulate
lncRNA expression ([51]18). The regulatory units within an lnc-FFL
network are comprised of a gene, an miRNA, and their shared target
lncRNAs. FFLs participate in many biological processes, including cell
development and differentiation, and can cause disease ([52]19,
[53]20). However, the functions of lnc-FFL in GDM, and especially the
relationship between lnc-FFL, glycometabolism, and hormones, are
unclear.
In the present study, a global lnc-FFL network was constructed and
analyzed. Glycometabolism- and hormone-related lnc-FFL networks were
extracted, and it was shown that lncRNAs play essential roles in these
two networks. An integrated computational approach was designed to
identify dysregulated glycometabolism- and hormone-related lnc-FFLs in
GDM patients. The patterns of dysregulated glycometabolism- and
hormone-related lnc-FFLs in GDM are complex. There are strong
associations between dysregulated glycometabolism- and hormone-related
lnc-FFLs in GDM. Some core modules were discovered and extracted from
dysregulated lnc-FFLs in GDM. These dysregulated lnc-FFLs showed
specific and essential functions. Furthermore, the dysregulated
lnc-FFLs and ceRNAs could form more complex modules that play
additional roles in GDM. In particular, we discovered that these
dysregulated lnc-FFLs focused on the thyroid hormone signaling pathway.
Some drug repurposing candidates were also identified based on lnc-FFL
in GDM. Collectively, the present study highlighted the effect of
dysregulated glycometabolism- and hormone-related lnc-FFLs in GDM and
revealed their potential as novel biomarkers and treatment targets in
GDM.
Materials and Methods
Construction of a Global Experimentally Validated lnc-FFL Network
Data for experimentally validated gene–miRNA interactions were obtained
from miRTarBase 7.0, which is a public database that contains more than
360,000 miRNA–target interactions ([54]21). For the current study, we
only extracted gene–miRNA interactions supported by strong experimental
evidence. All experimentally validated gene–lncRNA and miRNA–lncRNA
interactions were obtained from the RAID 2.0 database ([55]22). RAID
contains experimentally verified and computationally predicted RNA–RNA
interactions. Only gene–lncRNA and miRNA–lncRNA interactions supported
by strong experimental evidence were extracted. Based on these
experimentally verified interactions, an lnc-FFL was identified whereby
a specific gene would regulate an lncRNA and an miRNA, while the miRNA
would also regulate the lncRNA. Then, based on all of the lnc-FFLs
identified above, the global lnc-FFL network was constructed using
cytoscape ([56]http://www.cytoscape.org/). The analysis of the
topological features of the network was also performed in cytoscape.
Characteristics of GDM and NGT Samples
All the GDM and normal glucose-tolerant (NGT) samples were obtained
from a previous study ([57]23). All the subjects received 5-point 75-g
oral glucose tolerance test after fasting for one night. Blood glucose
and insulin were measured at 0, 30, 60, 90, and 120 min. A Bedside
glucose analyzer (YSI, Yellow Springs, OH, USA) and ADVIA Centaur XP
(Siemens Healthcare GmbH, Erlangen, Germany) were used to measure blood
glucose and plasma insulin. NGT samples were defined as fasting glucose
≤ 5.11 mmol/L, 1-h glucose ≤ 10.00 mmol/L, and 2-h glucose ≤ 8.50
mmol/L. GDM patients were defined as the fasting glucose beyond the
above limits. GDM was diagnosed based on the International Association
of diabetes and pregnancy study groups in 2010 recommendations for the
diagnosis and classification of hyperglycemia during pregnancy. At
last, the present study contained eight GDM and NGT pregnant women who
were matched based on their body-mass-index and age. The anthropometric
and metabolic characteristics of GDM and NGT samples were summarized in
[58]Table S1.
Collection of High-Throughput Expression Profiles of lncRNAs, miRNAs, and
Genes for GDM
The high-throughput expression profiles of lncRNAs, miRNAs, and genes
for GDM and normal control samples were obtained from the Gene
Expression Omnibus database ([59]www.ncbi.nlm.nih.gov/geo). Whole blood
was collected from these 16 subjects in PAXgene Blood RNA Tubes
(PreanalytiX) after overnight fasting. Next, total RNA was isolated
using the PAXgene Blood miRNA kit (PreanalytiX) according to the
manufacturer's specifications. Paired data were adjusted for
mid-pregnancy weight gain and pregnancy week ([60]GSE92772). The
Illumina HiSeq 2500 sequencer was used to obtain the blood cell
RNA-sequencing data from the above 16 samples. Adapter trimming was
performed using trim_galore/cutadapt v 0.4.0 with standard settings for
Illumina reads. Read mapping against the hg38 genome was performed
using STAR aligner v 2.4.1d with standard settings. The expression and
detailed information about the dataset can be obtained from the
[61]GSE92772 database
([62]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92772).
Collection of Glycometabolism- and Hormone-Related Genes
All glycometabolism- and hormone-related genes were obtained from AmiGO
2 version: 2.4.26 in Homo sapiens species ([63]24). Finally, we
collected 1,845 glycometabolism-related genes and 1,552 hormone-related
genes.
Identification of Dysregulated Glycometabolism- and Hormone-Related lnc-FFLs
in GDM
We designed a comprehensive computational approach to identify
significantly dysregulated lnc-FFLs for GDM using global lnc-FFL
network and expression profile data. This integrated approach focused
on whole change of the L-FFL rather than a single molecule. Firstly, we
performed a Student t-test for each lnc-FFL to evaluate differences in
lncRNA, miRNA, and gene expression levels between GDM and NGT patients.
The resulting p-values from the t-tests were used in consequent
analyses. Secondly, we calculated Pearson Correlation Coefficients
(PCCs) for each gene–lncRNA, gene–miRNA, and miRNA–lncRNA interaction
in the GDM and NGT samples. For each interaction, the absolute
difference between PCC values from the GDM and NGT samples was defined
as the difference level for that specific interaction. Thirdly, for
each lnc-FFL, two comprehensive scores (CSs) containing differential
expression P-values (CS[dif]) and PCCs (CS[PCC]) were defined:
[MATH: CSdif=PlncRNA*PmiRNA*Pgene :MATH]
(1)
[MATH: CSPCC=|(GDMlnc−miR
− NGTlnc−miR)*(GDMlnc−gene− NGTlnc−gne)*(GDMmiR−gene− NGTmiR−gene)| (2) :MATH]
(2)
where P[lncRNA], P[miRNA], and P[gene] denote the p-values for the
differential lncRNA, miRNA, and gene expression, respectively, for each
lnc-FFL. CS[dif] represents the integrated differential lncRNA, miRNA,
and gene expression levels between the GDM and NGT samples.
GDM[lnc−miR], GDM[lnc−gene], and GDM[miR−gene] represent the PCC values
of lncRNA-miRNA, lncRNA-gene, and gene-miRNA interactions,
respectively, in GDM patients. Similarly, NGT[lnc−miR], NGT[lnc−gene],
and NGT[miR−gene], represent the PCC values of lncRNA–miRNA,
lncRNA–gene, and gene–miRNA interactions, respectively, in NGT samples.
CS[PCC] represents the difference in the co-expression levels of an
lnc-FFL between GDM and NGT. Fourthly, we used the values of CS[dif]
and CS[PCC] to rank all lnc-FFLs based on an equal weighted ranking
method, and each lnc-FFL was then given a final comprehensive score.
Finally, we performed 1,000 random permutations of the sample labels in
the lncRNA, miRNA, and gene expression profiles. Then, the significant
lnc-FFLs were obtained by comparing their final comprehensive score
with their permutation score (p < 0.05).
Identification of Key and Core Modules From the Dysregulated lnc-FFL Network
Associated With GDM
Key modules containing the node with the highest degree and its
neighbors were extracted. Core modules were extracted from the
dysregulated lnc-FFL network associated with GDM using the package
ClusterOne in cytoscape with default parameters
([64]http://apps.cytoscape.org/apps/ClusterONE). ClusterONE is a
package that clusters a given network based on its topology in order to
identify densely connected regions.
Functional Enrichment Analysis for Dysregulated lnc-FFLs in GDM Patients
In order to describe the functions of dysregulated lnc-FFLs in GDM, we
used the DIANA-miRPath v3.0 software to analyze miRNA functions, thus
inferring the functions of the corresponding lnc-FFLs and lncRNAs
([65]25). Several significant GO terms and KEGG pathways were
identified.
Analysis of Drug Repurposing Candidates Based on GDM lnc-FFLs
DrugBank was used to obtain information about the relation between
drugs and genes in dysregulated lnc-FFLs ([66]26). The SM2miR database
was used to obtain information about the relation between drugs and
miRNAs in dysregulated lnc-FFLs ([67]27).
Results
Construction and Analysis of Glycometabolism- and Hormone-Related lnc-FFL
Networks
lnc-FFL is defined as a regulatory motif whereby a gene regulates an
lncRNA and an miRNA while the miRNA also regulates the lncRNA. Multiple
lnc-FFLs could form an lnc-FFL network. In our study, a global lnc-FFL
network was constructed based on data describing experimentally
verified gene–lncRNA, gene–miRNA, and miRNA–lncRNA interactions
([68]Figure 1A). The global lnc-FFL network contained 1,347 lnc-FFLs,
114 coding genes, 164 lncRNAs, and 154 miRNAs ([69]Figure 1B). We
discovered that the global lnc-FFL network approximates a scale-free
network (R-square = 0.916), which is a major topological feature of
biology network ([70]Figure 1C). This indicates that the global lnc-FFL
network provides an effective context for identifying specific lnc-FFLs
of GDM. Previous study suggested that glycometabolism and hormones are
two highly influential factors in GDM ([71]8, [72]10). We obtained
1,845 glycometabolism- and 1,552 hormone-related genes. The
intersection between glycometabolism- and hormone-related genes
contained 435 members, suggesting that there is a link between these
two factors ([73]Figure 1D). In order to describe the associations
between glycometabolism- and hormone-related lnc-FFLs in GDM, we
extracted glycometabolism- and hormone-related lnc-FFL subnetworks from
the global lnc-FFL network ([74]Figures 1E,F). The
glycometabolism-related lnc-FFL network included 229 lnc-FFLs, 20
coding genes, 44 lncRNAs, and 58 miRNAs. The hormone-related lnc-FFL
network included 530 lnc-FFLs, 42 coding genes, 69 lncRNAs, and 111
miRNAs. These lncRNAs and miRNAs maybe play important roles in the
glycometabolism- and hormone-related lnc-FFL network. All these results
indicate that lnc-FFLs are vital motifs in the biology of GDM.
Figure 1.
[75]Figure 1
[76]Open in a new tab
Construction and analysis of glycometabolism- and hormone-related
lnc-FFL networks. (A) A global lnc-FFL network based on experimentally
verified interaction data. lncRNAs, miRNAs, and genes are colored in
green, yellow, and red, respectively. (B) Bar plot, showing the number
of lncRNAs, miRNAs, and genes in the global lnc-FFLs network. (C) The
degree distribution of the global lnc-FFL network. (D) Venn diagram,
showing the intersection between glycometabolism- and hormone-related
genes. (E) The glycometabolism-related lnc-FFL network. (F) The
hormone-related lnc-FFL network.
Some Glycometabolism- and Hormone-Related lnc-FFLs Are Dysregulated in GDM
Patients
A comprehensive algorithm was developed to identify significantly
dysregulated lnc-FFLs in the GDM-associated glycometabolism- and
hormone-related lnc-FFL using the global lnc-FFL network and lncRNA,
miRNA, and gene expression profiles. We identified 11 glycometabolism-
and 29 hormone-related lnc-FFLs that were dysregulated in GDM
([77]Figures 2A,B). Then, two comprehensive scores based on CS[dif] and
CS[PCC] as well as a final score were used to characterize the
networks. All three scores showed a similar distribution in both
glycometabolism- or hormone-related lnc-FFL networks ([78]Figures
2C–E). Moreover, CS[dif] and the final score approximated a unimodal
distribution, whereas CS[PCC] followed a bimodal-peaks distribution.
Notably, we discovered that the dysregulated patterns of lnc-FFLs are
multiple and complex. For example, in the dysregulated
glycometabolism-related lnc-FFL SP1/miR-200c-3p/CYP1B1-AS1, the
interaction between miR-200c-3p and CYP1B1 changed from a positive
correlation to a negative correlation ([79]Figure 2F), whereas the
interaction between SP1 and CYP1B1 changed from no correlation to a
positive correlation. The SP1 gene is downregulated in GDM patients,
yet no change in the interaction between SP1 and miR-200c-3p was
observed. In another dysregulated hormone-related lnc-FFL
SMAD4/miR-185-5p/ZFAS1, the interaction between SMAD4 and miR-182-5p
changed from a negative correlation to no correlation ([80]Figure 2G),
and the interaction between SMAD4 and ZFAS1 changed from a positive
correlation to no correlation. The respective coding gene is
downregulated in GDM patients. These results indicate that only local
changes in the lnc-FFL networks can contribute to dysregulation in GDM.
The resulting patterns in the dysregulated lnc-FFLs are multiple and
complex.
Figure 2.
[81]Figure 2
[82]Open in a new tab
The dysregulated glycometabolism- and hormone-related lnc-FFL networks.
(A) The dysregulated glycometabolism-related lnc-FFL network. (B) The
dysregulated hormone-related lnc-FFL network. (C–E) Density
distribution curves of CS[dif], CS[PCC], and final score, respectively,
in dysregulated glycometabolism- and hormone-related lnc-FFL networks.
(F,G) Two examples showing dysregulated patterns of lnc-FFLs in GDM.
The Dysregulated Glycometabolism- and Hormone-Related lnc-FFLs Show Strong
Associations in GDM Patients
In order to explore the associations between dysregulated
glycometabolism- and hormone-related lnc-FFLs, we further analyzed both
the common and unique dysregulated glycometabolism- and hormone-related
lnc-FFLs in GDM. We identified 10 common glycometabolism- and
hormone-related dysregulated lnc-FFLs in GDM ([83]Figure 3A). An
lnc-FFL dysregulated only in the glycometabolism-related networks is
the CEBPA/miR-125b-5p/CYB1B1-AS1 module ([84]Figure 3B). In this
dysregulated lnc-FFL, all interactions between gene, miRNA, and lncRNA
are perturbed. Similarly, we also constructed a specific
hormone-related dysregulated lnc-FFL network containing 19 lnc-FFLs,
six coding genes, six lncRNAs, and 16 lncRNAs ([85]Figure 3C). These
lnc-FFLs are all specific for the hormone-related dysregulated lnc-FFL
networks associated with GDM. In the specific hormone-related
dysregulated lnc-FFL EP300/miR-574-3p/LINC00324, only the interaction
of miR-574-3p and LINC00324 appeared to change from no correlation to a
negative correlation ([86]Figure 3D). The interaction between EP300 and
LINC00324 was stronger in GDM. We also discovered that the changes in
gene, miRNA, and lncRNA expression levels are weak ([87]Figure 3E).
However, the interactions between these three molecules show major
changes; therefore, they may be important for the development of GDM.
Figure 3.
[88]Figure 3
[89]Open in a new tab
Common and specific features of dysregulated glycometabolism- and
hormone-related lnc-FFLs. (A) Venn diagram showing the intersection
between dysregulated glycometabolism- and hormone-related lnc-FFLs. (B)
The glycometabolism-related lnc-FFL network in GDM. (C) The
hormone-related dysregulated lnc-FFL network in GDM. (D) An example
showing the common dysregulated lnc-FFLs in GDM. (E) lncRNA, miRNA, and
gene expression levels in a common dysregulated lnc-FFL.
Certain Core Modules Exhibit Specific Functions and lnc-FFLs Could Form More
Complex Modules With ceRNAs
In order to further characterize the roles of dysregulated lnc-FFLs in
GDM, we analyzed the lnc-FFL modules extracted from common
glycometabolism- and hormone-related dysregulated lnc-FFLs. First, we
extracted a key module, which contained the SP1 gene exhibiting the
highest degree and all its neighbors ([90]Figure 4A). This key module
contained three lncRNAs and seven miRNAs. In addition, we identified a
core module, which showed a closer network structure ([91]Figure 4B).
This core module consisted of one coding gene, two lncRNAs, two miRNAs,
and five molecules from two lnc-FFLs: EP300/LINC00324/miR-150-5p and
EP300/ZFAS1/miR-574-3p. We discovered that miRNAs play vital roles in
these two modules; therefore, we analyzed the functions of these miRNAs
to better understand the function of the whole module. We found that
these miRNAs are enriched in some key pathways, such as the thyroid
hormone signaling pathway, the fatty acid biosynthesis pathway, and the
lysine degradation pathway ([92]Figure 4C). They are also enriched in
some GO terms, such as the catabolic process, the small molecule
metabolic process, the insulin receptor signaling pathway, and the
androgen receptor signaling pathway ([93]Figure 4D). Almost all of the
enrichment pathways and GO terms are related with metabolic and hormone
regulation processes. Importantly, we discovered that some dysregulated
lnc-FFLs can combine with dysregulated ceRNAs to form more complex
modules that exhibit distinct functions. For example, lnc-FFL
EP300/ZFAS1/miR-150-5p and ceRNA EP300/HCG27/miR-150-5p form a complex
module by sharing EP300 and miR-150-5p ([94]Figure 4E). Our results
indicated that some modules extracted from dysregulated lnc-FFL
networks show specific functions and could combine with other motifs to
gain new functionalities.
Figure 4.
[95]Figure 4
[96]Open in a new tab
Core modules extracted from common dysregulated lnc-FFL networks,
showing specific functions. (A) A key module that contains the node
with the highest degree and its neighbors. (B) A core module extracted
from dysregulated lnc-FFL networks associated with GDM. (C) Heat map
showing the p-values of pathway enrichment analysis with red
representing more significant p-values. (D) Bar plot showing the
p-values of significantly enriched GO terms. (E) An example, showing
that ceRNAs and dysregulated lnc-FFLs combine to form more complex
modules.
Identification of Drug Repurposing Candidates for GDM Based on Dysregulated
lnc-FFLs
Based on functional analyses, we found a key pathway, namely, the
thyroid hormone signaling pathway, which is related with dysregulated
lnc-FFLs in GDM ([97]Figure 5A). The thyroid gland and its metabolism
have a considerable physiological impact during pregnancy ([98]28).
Thyroid dysfunction is considered to play a vital role in the etiology
of GDM because the thyroid hormone plays an important role in glucose
metabolism and homeostasis ([99]11). We discovered that some of the
target genes of miRNAs in dysregulated lnc-FFLs are enriched in the
thyroid hormone signaling pathway. For example, PFKFB2 is a key gene in
the thyroid hormone signaling pathway and directly regulates glucose
metabolism. Finally, we considered the possibility that dysregulated
lnc-FFLs could contribute to the identification of drug repurposing
candidates for GDM. For this purpose, we constructed a drug-related
dysregulated lnc-FFL network based on genes and miRNAs in dysregulated
lnc-FFLs networks associated with GDM ([100]Figure 5B). This
drug-related network contained 4 genes, 15 miRNAs, and 62 drugs, 72.41%
of which are anti-inflammatory and hormonal drugs, such as prednisone,
hydrocortisone, paramethasone, and ciclesonide ([101]Figure 5C). In
particular, we discovered a candidate drug named troglitazone, which
was introduced as an anti-diabetic drug but was subsequently withdrawn
from clinical use due to severe hepatotoxicity ([102]29). We found that
the ESRRA (steroid hormone receptor ERR1) gene is a target of
troglitazone and forms a dysregulated lnc-FFL with miR-125b-5p and
CYP1B1-AS1 ([103]Figure 5D). Another drug candidate was
1-CYCLOHEXYL-N-{[1-(4-METHYLPHENYL)-1H-INDOL-3-YL]METHYL}METHANAMINE,
whose function and mechanism of action is uncertain. These results
indicated that some anti-inflammatory and hormonal drugs can be
considered candidate drugs for GDM.
Figure 5.
[104]Figure 5
[105]Open in a new tab
Candidate drug repurposing based on dysregulated lnc-FFLs associated
with GDM. (A) The thyroid hormone pathway, and enriched target genes of
miRNAs in dysregulated lnc-FFLs associated with GDM. (B) Drug-related
dysregulated lnc-FFL networks. The square represents drugs and the
circle represents genes, lncRNAs, and miRNAs. (C) Pie chart showing the
percent of anti-inflammatory and hormonal drugs. (D) Candidate drug
repurposing of the ESPRA-related lnc-FFL.
Discussion
lncRNAs, miRNAs, and coding genes can form many types of motifs that
play important roles in many biological processes and a large number of
diseases. At present, there are many studies focusing on ceRNAs and
FFLs. Wu et al. constructed a comprehensive ceRNA network and
identified key molecules in diabetic retinopathy ([106]30). Ling et al.
also validated some ceRNA motifs in diabetic retinopathy ([107]31).
FFLs are network motifs, defined by a three-gene pattern, which are
composed of two input transcription factors, one of which regulates the
other, and both jointly regulate a target gene ([108]32). In our study,
we constructed an lncRNA-mediated FFL, composed of one gene, one
lncRNA, and one miRNA, whereby the gene regulates the miRNA, and both
the gene and the miRNA regulate the lncRNA. Such lnc-FFLs play
important roles in many diseases, including cancer ([109]18, [110]33,
[111]34). In the present study, we developed an integrated algorithm to
identify dysregulated lnc-FFLs associated with GDM. We attempted to
define the functions of such dysregulated lnc-FFLs in GDM based on
functional and drug repurposing analyses. Our results indicated that
dysregulated lnc-FFLs play important roles in GDM.
GDM is a disease characterized by metabolic dysfunction that manifests
during pregnancy. Typically, alterations in lipid, glucose, and
carbohydrate metabolism occur in pregnant women with GDM ([112]35). In
the present study, we focused on glycometabolism, which includes
fucosylation, lipid glycosylation, macromolecule glycosylation, and
mannosylation. A previous study suggested that, during normal
pregnancy, pathways involved in the biosynthesis of steroid hormones
are tightly regulated ([113]36). The roles and underlying mechanisms of
glycometabolism and hormones in GDM are unclear. In our study, we
identified glycometabolism- and hormone-related dysregulated lnc-FFLs
in GDM. We discovered a significant intersection between
glycometabolism- and hormone-related dysregulated lnc-FFLs in GDM,
suggesting that glycometabolism and hormone-related lnc-FFLs may act
jointly to contribute to the occurrence and development of GDM.
However, there were more hormone-related lnc-FFLs than
glycometabolism-related lnc-FFLs associated with GDM. Thus, hormonal
disorders may play more complex and important roles in GDM.
Pregnancy alters normal thyroid function, and severe maternal
hypothyroidism has been associated with an increased risk for GDM
([114]37). The levels of blood glucose and thyroid function of pregnant
women depend on many hormones, including estrogen, thyroid-binding
globulin, human chorionic gonadotropin, placental lactogen, cortisol,
and placental insulin enzyme ([115]38). An increasing number of studies
suggests that there is a strong association between thyroid diseases
and GDM ([116]39). In our analyses, we found that the thyroid hormone
signaling pathway is a key pathway in which the target genes of miRNAs
in dysregulated lnc-FFLs are enriched. The thyroid hormone signaling
pathway contains many important carbohydrate metabolism-related
subpathways or processes, such as glucose transport and glucose
metabolism. Our results, based on the analysis of dysregulated
lnc-FFLs, demonstrated that there is a strong association between
thyroid function and GDM.
Another important network motif that contributes to the development of
many diseases involves ceRNA ([117]40). In our analyses, we found 11
ceRNAs sharing common genes, miRNAs, or lncRNAs with dysregulated
lnc-FFLs in GDM. The dysregulated lnc-FFL and ceRNA motifs might
exhibit complex regulatory functions and crosstalk in GDM. Our results
suggested that many kinds of network motifs influence the development
of GDM. Moreover, these networks are not independent of each other, and
they generally form more complex motifs that may assume new roles in
GDM. Future studies will focus on investigating a higher number of GDM
samples in order to validate the accuracy and stability of the method
presented in the current study. The integrated method could provide
assistance for exploring the roles of lncRNAs in GDM by their
interacted genes and miRNAs. The comprehensive approach by integrating
expression profiles of gene, miRNA, and lncRNA could identify better
and more global biomarkers for GDM than a single type of molecule.
However, the size of the samples, which detected the expression of
lncRNAs, miRNAs, and genes at the same time, was not very large. In
future work, more GDM and NGT samples should be used for validating
this method. Although we were only able to validate the drug
repurposing candidates in a small sample size, these served as proof of
principles for using the lnc-FFLs in expression and interactions panels
to gain insight into precision medicine. With the emergence of the
pharmacogenomics data of standardly designed GDM precision medicine, we
should be able to determine the performance of lnc-FFLs in GDM patients
in short future.
In summary, in the present study, a global lnc-FFL network was
constructed. An integrated algorithm was designed and significantly
dysregulated glycometabolism- and hormone-related lnc-FFLs were
identified in connection with GDM. The patterns of the dysregulated
lnc-FFLs associated with GDM are multiple and complex. The identified
glycometabolism- and hormone-related lnc-FFLs showed strong association
with GDM. Core modules with specific functions in GDM could be
extracted from the dysregulated lnc-FFLs networks. In addition,
dysregulated lnc-FFLs could form more complex modules through
combination with ceRNAs. Drug repurposing candidates, such as
hormone-related drugs, were identified based on lnc-FFLs associated
with GDM. Our study provides insight for the identification of novel
biomarkers and drug candidates for GDM.
Data Availability Statement
All data generated or analyzed during this study are included in this
published article.
Author Contributions
NL and XF conceived and designed the experiments. HC, SZ, YL, and TL
analyzed the data. NL and YS wrote the manuscript.
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Footnotes
Funding. This work was supported by the Heilongjiang Traditional
Chinese Medicine Research Project.
Supplementary Material
The Supplementary Material for this article can be found online at:
[118]https://www.frontiersin.org/articles/10.3389/fendo.2020.00093/full
#supplementary-material
[119]Click here for additional data file.^ (28.5KB, DOC)
References