Next Article in Journal
Frequency of Gene Polymorphisms in Admixed Venezuelan Women with Recurrent Pregnancy Loss: Microsomal Epoxy Hydroxylase (rs1051740) and Enos (rs1799983)
Previous Article in Journal
Surface Engineering of Escherichia coli to Display Its Phytase (AppA) and Functional Analysis of Enzyme Activities
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
CIMB
Volume 46
Issue 4
10.3390/cimb46040216
cimb-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

MicroRNAs in the Pathogenesis of Preeclampsia—A Case-Control In Silico Analysis

by
Ramanathan Kasimanickam
1,* and
Vanmathy Kasimanickam
2
1
Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Washington State University, Pullman, WA 99164, USA
2
Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, WA 99164, USA
*
Author to whom correspondence should be addressed.
Curr. Issues Mol. Biol. 2024, 46(4), 3438-3459; https://doi.org/10.3390/cimb46040216
Submission received: 29 February 2024 / Revised: 3 April 2024 / Accepted: 4 April 2024 / Published: 17 April 2024
(This article belongs to the Section Bioinformatics and Systems Biology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Preeclampsia (PE) occurs in 5% to 7% of all pregnancies, and the PE that results from abnormal placentation acts as a primary cause of maternal and neonatal morbidity and mortality. The objective of this secondary analysis was to elucidate the pathogenesis of PE by probing protein–protein interactions from in silico analysis of transcriptomes between PE and normal placenta from Gene Expression Omnibus (GSE149812). The pathogenesis of PE is apparently determined by associations of miRNA molecules and their target genes and the degree of changes in their expressions with irregularities in the functions of hemostasis, vascular systems, and inflammatory processes at the fetal–maternal interface. These irregularities ultimately lead to impaired placental growth and hypoxic injuries, generally manifesting as placental insufficiency. These differentially expressed miRNAs or genes in placental tissue and/or in blood can serve as novel diagnostic and therapeutic biomarkers.

1. Introduction

Preeclampsia (PE) is a complication of pregnancy with symptoms of high blood pressure, proteinuria, or other signs of organ damage, and occurs in 5% to 7% of pregnancies. It is one of the leading causes of maternal morbidity. Annually, PE causes over 70,000 maternal deaths and 500,000 fetal deaths worldwide [1]. Risk factors for PE include first pregnancy; previous occurrence of PE; history of hypertension; chronic kidney disease; history of thrombophilia; pregnancy from in vitro fertilization; family history of PE; type 1 or type 2 diabetes; a body mass index (BMI) of ≥35 kg/m2; advanced maternal age (≥40 years); and prolonged interval since last pregnancy [2].
Genetic factors were associated with the occurrence of PE [3]. In a previous study by Moufarrej et al. (2022), marked cell-free RNA (cfRNA) transcriptomic changes were observed between normotensive and preeclamptic mothers early in gestation, well before the onset of PE symptoms [4]. Furthermore, their study validated a panel of 18 genes using cfRNA expression to identify the mothers at risk of preeclampsia at 5 to 16 weeks of gestation, long before the manifestation of clinical symptoms [4].
Preeclampsia that originates from abnormal placentation primarily causes maternal and neonatal morbidity and mortality [5,6]. However, the cause of the abnormal development of the placenta remains poorly understood [7,8]. Genes were found to be differentially expressed between PE and normal placenta tissues and were associated with PE pathogenesis [5]. Hence, studies have been focused on the genetic signature of the placenta from preeclampsia.
Recent advances in high-throughput in silico techniques portray experimental data into exemplified biological networks. Exploring these biological networks can disclose the role of individual proteins, protein–protein interactions (PPIs), and corresponding biological functions. This study intended to use the transcriptomic profiling of mRNA in preeclamptic (PE) and normal placentae from Gene Expression Omnibus (GSE149812) for further in silico analysis to elucidate the involvement of placenta-specific miRNA in the pathogenesis of PE.

2. Materials and Methods

In this study, differentially expressed PE-associated genes were identified from transcriptome data of PE and normal placenta samples. The gene expression data (profiled by microarray) and clinical characteristics were downloaded from the Gene Expression Omnibus (GSE149812; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149812; Accessed 7 July 2023). In the primary data, the description included patients’ clinical characteristics, tissue collection, RNA extraction, and microarray analysis methods. An excerpt of the description is provided below in Section 2.1 and Section 2.2.

2.1. Patients and Tissue Collection

Placental biopsies were obtained during cesarean section from both normotensive patients (n = 3) and those with preeclampsia (n = 3) (early onset type of PE; <31 weeks of gestation). All patients involved in this study were recruited from the Department of Obstetrics and Gynecology, the Third Xiangya Hospital, Central South University, Hunan, China. Pieces of villous tissue (0.5 × 0.5 × 0.5 cm3), approximately 2 cm beside the umbilical cord insertion, from the middle layer of the placenta midway between the maternal and fetal surfaces from different areas, were excised, excluding sites of hemorrhage, infarction, and fibrin deposition. Tissues were immediately placed in 1.0 mL RNAstore Reagent (CWbiotech Company, Taizhou, China), and then stored at −80 °C until use.

2.2. RNA Extraction and Microarray Analysis

Total RNA was extracted using TRIzol following the manufacturer’s instructions. Cyanine-3 (Cy3) labeled complementary RNA (cRNA) was prepared from 0.5 µg RNA using the One-Color Low RNA Input Linear Amplification PLUS kit (Agilent Tech. Inc., Santa Clara, CA, USA), followed by RNAeasy column purification (Qiagen Inc., Valencia, CA, USA). The cRNA yield was checked by an ND-1000 Spectrophotometer. Then, 1.5 µg of Cy3-labeled cRNA (specific activity > 10.0 pmol Cy3/µg cRNA) was fragmented at 60 °C for 30 min in a reaction volume of 250 mL containing 1× Agilent fragmentation buffer and 2× Agilent blocking buffer. On completion of the fragmentation reaction, 250 mL of 2× Agilent hybridization buffer was added to the fragmentation mixture and hybridized to Phalanx Human OneArray ver. 6 Release 1 for 17 h at 65 °C in a rotating Agilent hybridization oven. After hybridization, microarray slides were washed for 1 min at room temperature with GE wash buffer 1 (Agilent) and 1 min with 37 °C GE wash buffer 2 (Agilent) and then dried immediately by brief centrifugation. Slides were scanned immediately after washing on an Agilent DNA Microarray Scanner (G2505B) using one color scan setting for 1 × 44k array slides (scan area of 61 × 21.6 mm2; scan resolution of 10 µm; dye channel set to Green, and Green PMT was set to 100%). The scanned images were analyzed using Feature Extraction Software 9.1 (Agilent).

2.3. Data Processing

The data were analyzed with GEO2R to identify genes that are differentially expressed between the two groups. GEO2R uses DESeq2, which is an R package for identifying differentially expressed genes from RNA-seq data [9,10] using negative binomial generalized linear models, which are suitable for studies with few replicates [10]. A 5-fold relative difference (p ≤ 0.05) was used as a cut-off for the selection of differentially expressed (upregulated and downregulated) genes for further in silico analysis.

2.4. In Silico Analysis

2.4.1. Prediction and Analysis of Differentially Expressed Genes

The updated miRNet (http://www.mirnet.ca/, accessed on 1 July 2023) platform was used [11] to perform interaction analysis, separately, for upregulated and downregulated genes. The degree (defined by the number of connections a node has to other nodes) and betweenness (defined by the number of connections occurring upon a node) of miRNAs and genes in the network were determined.

2.4.2. Gene Ontology and Functional Annotation Analysis of Genes with the Highest Degree and Betweenness Centrality

The top 20 up- and downregulated genes with the highest degree and betweenness centrality were selected, and their tissue expression, associated interacting genes (up to 6 genes; http://stringdb.org/; accessed on 6 July 2023), and single-cell normalized expression (https://www.proteinatlas.org/; accessed on 6 July 2023) were investigated.

2.4.3. Gene Ontology Enrichment and KEGG Pathway Analysis

All differentially expressed genes from the network were retrieved to recognize PPIs. The PPI network was created using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) online database (http://stringdb.org/; accessed on 1 July 2023) separately for upregulated and downregulated genes [12]. Gene Ontology (GO) functional annotation for biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also performed. A p-value of <0.05 was regarded as statistically significant.

2.4.4. Identification and Analysis of Hub Gene

The PPI networks for upregulated and downregulated genes from the STRING database were exported to Cytoscape software (version 3.10) [13]. The hub genes were selected as the top 20 nodes of the PPI network using the Maximal Clique Centrality (MCC) method [14], which has a better performance on the precision of predicting top essential proteins. Further analysis was performed using ClueGO [15] to integrate GO terms as well as KEGG pathways and create a functionally nested or organized GO/pathway term (k-score = 3). This task compares one set of genes or two lists of genes and comprehensively visualizes functionally grouped terms [15].

2.4.5. Gene Ontology and Functional Annotation Analysis of Hub Genes

The hub genes and their roles, tissue expression, and protein–protein interactions (up to 6 closely related genes) for differentially expressed genes in women with PE from STRING (http://stringdb.org/; accessed on 4 July 2023) and human protein atlas (https://www.proteinatlas.org; accessed on 4 July 2023) were investigated. To substantiate their presence, tissue expression and organelle localization were presented.

2.4.6. Comparison of miRNAs of Different Types of Preeclampsia

For comparison of different types (early- vs. late-onset; mild vs. severe) of preeclampsia, we selected DE genes in early-onset severe preeclampsia, late-onset severe preeclampsia, and late-onset mild preeclampsia from RNA-seq on 65 high-quality placenta samples that included 33 from 30 PE patients and 32 from 30 control subjects reported by Ren et al., 2021 [16]. These DE gene sets representing different types of PE were subjected to gene-miRNA interaction analysis.

3. Results

The transcriptomic (mRNA) profiling between PE and normal placenta tissues from Gene Expression Omnibus (GSE149812) recognized 28,254 genes (Supplementary File S1). There were 79 and 60 up- and downregulated genes, respectively (Supplementary File S1). Of those differentially expressed genes, 52 and 42 up- and downregulated genes, respectively, were at a 5-fold difference (p ≤ 0.05; Supplementary File S2). The gene–miRNA interaction network analysis revealed the involvement of 45 upregulated and 32 downregulated genes.
From the gene–miRNA interaction network analysis, the degree and betweenness for the 45 upregulated genes were calculated. The 45 upregulated genes interacted with 829 miRNAs and 33 transcription factors (Figure 1). The degree and betweenness ranged from 1 to 19 and 0 to 16,641.0, respectively, for the 829 interacting miRNAs. The degree and betweenness ranged from 1 to 169 and 0 to 62,836.6, respectively, for the 45 upregulated genes. The degree and betweenness of the gene–miRNA interaction network for upregulated miRNAs is shown in Supplementary File S2.
Similarly, from the gene–miRNA interaction network analysis, the degree and betweenness for 36 downregulated genes were calculated. The 36 downregulated genes interacted with 1057 miRNAs and 39 transcription factors (Figure 2). The degree and betweenness ranged from 1 to 19 and 0 to 24,476.6 for the 1057 interacting miRNAs. The degree and betweenness ranged from 1 to 223 and 0 to 161,133.4 for the 36 downregulated genes. The degree and betweenness of the gene–miRNA interaction network for the downregulated miRNAs is shown in Supplementary File S3.
The interaction network for the top 20 upregulated genes is presented in Figure 3. The degree and betweenness ranged from 28 to 129 and 12,741.0 to 62,386.6 for the top 20 upregulated genes (Table 1). The interaction network for the top 20 downregulated genes is presented in Figure 4. The degree and betweenness ranged from 44 to 223 and 22,680.9 to 161,133.4 for the top 20 downregulated genes (Table 2). In addition, the top up- and downregulated genes’ tissue expressions, single-cell normalized expressions (https://www.proteinatlas.org/; accessed on 7 July 2023), and functions are given in Table 3 and Table 4, respectively.
After determining the degree and betweenness, the up- and downregulated genes that were 5-fold different (p < 0.05) were submitted (http://stringdb.org/; accessed on 7 July 2023) to elucidate enrichment networks. Figure 5 shows the PPIs for the upregulated genes (78 nodes; 193 edges; PPI enrichment with p < 1.0 × 10−16), revealing 225 significantly enriched biological process GO terms (False Recovery Rate, p ≤ 0.05) and 54 significant (False Recovery Rate, p ≤ 0.05) KEGG enrichment pathways (Supplementary File S2). Figure 6 shows the PPIs for the downregulated genes (73 nodes and 293 edges, PPI enrichment p-value of <1.0 × 10−16), revealing 268 significantly enriched biological process GO terms (False Recovery Rate, p ≤ 0.05) and 87 significant (False Recovery Rate, p ≤ 0.05) KEGG enrichment pathways (Supplementary File S3). The PPI networks for the up- and downregulated genes were separately constructed using the STRING database and Cytoscape software (Version 3.9). The top-ranked 20 hub genes using the Maximal Clique Centrality (MCC) method for up- and downregulated genes were screened and are presented in Figure 7 and Figure 8, respectively. To interpret functionally nested gene ontology and pathway annotation networks for up- and downregulated genes in the PE placenta, ClueGo nested network analysis was performed, and the results are presented in Figure 9A–C and Figure 10A–C, respectively. The enrichment path from the ClueGo nested network analysis is presented in Supplementary File S4 (False Recovery Rate, p < 0.05). Table 5 and Table 6 show the hub genes and their roles, tissue expressions, and protein–protein interactions (up to six closely related genes) for up-and downregulated genes in the PE placenta.
For the comparison of miRNAs of different types (early-onset severe preeclampsia, late-onset severe preeclampsia, and late-onset mild) of preeclampsia, the top 20 molecular markers (genes and miRNAs with high betweenness) were selected and compared. Six miRNAs (hsa-mir-124-3p, hsa-mir-1-3p, hsa-mir-146a-5p, hsa-mir-16-5p, hsa-mir-27a-3p, and hsa-mir-34a-5p) signifying all three PE types were recognized. Upon further comparison, it was realized that five (hsa-mir-1-3p, hsa-mir-146a-5p, hsa-mir-16-5p, hsa-mir-27a-3p, and hsa-mir-34a-5p) of these six miRNAs were the top miRNAs identified from the current analysis.

4. Discussion

Recent advances in high-throughput techniques transform experimental data into biological connotations. In illustrated networks, the nodes representing proteins, transcripts, or metabolites are linked by edges to show the interactions among nodes. Protein network exploration depicts the role of an individual protein and its communication with other proteins, representing the protein–protein interaction.
Centrality (network-based ranking of biological components) has been largely used to find important nodes in larger networks [17,18]. These nodes with higher degrees are more likely to be essential proteins influencing biological processes. These molecular markers and their properties are helpful when prioritizing them for disease associations. Using these methods, key biological mechanisms involved in the pathogenesis of PE were identified in the current study.
In this study, the gene–miRNA interaction networks of differentially expressed genes between PE and normal placentae revealed interactions with up to 28,000 genes and miRNAs. This shows the importance and depth of their involvement in the regulatory and interactive functions. Betweenness centrality measures the extent to which a miRNA/gene lies on paths between other miRNAs/genes. MicroRNAs/genes with high betweenness may have substantial influence within a regulatory network by virtue of their control over passing information between others [19]. It should be noted that genes with a high degree centrality are of important for the diagnosis of disease, and the proteins with a high degree of betweenness are important for drug discovery [20].
In this study, significantly upregulated (TGFBR1, DUSP4, TMCC1, EMP1, and BHLHE40) and downregulated (KPNA6, ATP6V0E1, KLF6, PLEKHG2, SIKE1, and ZNF85) genes with high degree and betweenness centrality showed key roles associated to the development of PE, including cell metabolic, developmental, proliferative, differentiative and apoptotic processes; cell macromolecule biosynthesis; DNA templated transcription; and responses to enzyme binding, stress, growth factor stimulation, lipid metabolism, and hypoxia.

4.1. Upregulated Genes with High Betweenness

Transforming growth factor beta 1 is a polypeptide member of the transforming growth factor beta superfamily of cytokines. It is a secreted protein that performs many cellular functions, including the control of cell growth, cell proliferation, cell differentiation, and apoptosis [21]. TGF-β1 signaling occurs by its binding with its receptor type 2 (TGFBR2), which in turn recruits and phosphorylates TGFBR1, forming a heterodimeric complex [22]. Once TGFBR1 is phosphorylated, it can downstream phosphorylate proteins SMAD2 and SMAD3, which then recruit SMAD4, translocate to the nucleus, and regulate the transcription of TGFβ1 target genes [23,24]. TGFβ1 levels were elevated in women with severe and mild preeclampsia late in gestation (mean gestational age, 40 weeks) compared with normotensive pregnant women [25,26,27]. TGFβ1 plays a decisive role in altering dNK (decidual natural killer) phenotype and function, which may have an obvious effect on the pathogenesis of preeclampsia [20]. In the decidual zone of normal pregnancy, the dNK cell-mediated immune response and angiogenesis were subtly regulated by Treg cells via soluble TGFb1. However, in PE decidua, excessive amounts of TGFb produced by Treg cells could significantly impair the phenotype and function of dNK subpopulations. This distorted immune response may further damage decidual angiogenesis and cause pathological pregnancy [28]. In this investigation, TGFBR1 illustrated degree and betweenness scores of 129 and 62,386.6. The higher a gene’s/protein’s betweenness, the more important they are for the efficient flow of gains in a network, and downregulation of TGFBR1 would have had a significant impact on the biological functions and on the pathogenesis of PE.
The Dual-specificity phosphatase (DUSP) gene family is characterized by highly conserved amino acid sequences, implicated in a variety of biological functions [29]. Taurine upregulated 1 (TUG1) was downregulated in the placental tissues of PE patients compared with a control group [30]. TUG1 affected trophoblasts’ biological function, including cell growth, migration, and crosstalk in vitro, and promoted the progression of preeclampsia. TET3 (tet methylcytosine dioxygenase 3, a DNA-binding protein) and DUSP were negatively regulated by TUG1. Molecular and functional interaction between TET3 and DUSPs impaired spiral artery remodeling in PE [30]. Downregulated TUG1 increased the expression of DUSP4 at both mRNA and protein levels. Notably, silencing of suppressor of variegation 39 homolog 1 (SUV39H1) by siRNAs significantly upregulated DUSP4, signifying the biding of TUG1 and SUV39H1 in the nucleus [30]. TET3 activated gene transcription by promoting DNA demethylation [31]. TET3 knockdown markedly decreased the cellular expression of DUSP4. In uterine cells, TET3 deficiency increased methylation of DUSP4 promoters. Further, the methylation level of DUSP4 promoters in the preeclamptic placenta was significantly increased compared with controls [30]. Overexpression of miR-218 (upregulated in this study; degree, 5 and betweenness, 609.1) significantly upregulated FOXP1 and TUG1 and downregulated DUSP4, at both mRNA and protein levels [30]. The regulatory network mediated by TUG1 and DUSP4 seems to be an essential determinant of the pathogenesis of PE, which regulates cell growth. In mice, the DUSP9 gene located on the X chromosome performs an essential function during placental development [31]. Mouse embryo lethality between 8 and 10.5 days postcoitum was due to a failure of labyrinth development. This correlates with the normal expression pattern of DUSP9 in the trophoblast giant cells and the labyrinth of the placenta.
Furthermore, TMCC1 was significantly downregulated in PE placentae compared with normal placentae [32]. EMP1 is a protein-coding gene involved in apoptosis, which negatively regulates cell growth [33]. Circulating EMP1 was positively associated with severe placental insufficiency, placental dysfunction, and fetal growth restriction [34]. BHLHE40 is a transcriptional repressor that responds to hypoxia and negatively regulates miR-196a-5p expression. BHLHE40/miR-196a-5p is involved in PE pathogenesis [35]. Knockdown of BHLHE40 or upregulation of miR-196a-5p restored cell viability, migration, invasion, and matrix metalloprotein (MMP)-2 and MMP-9 expression under hypoxia. BHLHE40 knockdown alleviated PE symptoms in pregnant C57/BL6N mice.

4.2. Downregulated Genes with High Betweenness Centrality

Karyopherin α6 (KPNA6, importin α7), directly interacts with the Kelch-like ECH Associated Protein 1 (KEAP1) [36]. Overexpression of KPNA6 facilitates KEAP1 nuclear import and attenuates the Nuclear Factor Erythroid 2-related Factor 2 (NRF2/NFE2L2) signaling, whereas knockdown of KPNA6 slows down KEAP1 nuclear import and enhances the NRF2-mediated adaptive response induced by oxidative stress [37]. Thus, KPNA6-mediated KEAP1 nuclear import plays an essential role in modulating the NRF2-dependent antioxidant response and maintaining cellular redox homeostasis [38]. In preeclampsia, there was increased decidual oxidative stress, NRF2-regulated gene expression was reduced, and KEAP1 protein expression was increased in areas of high trophoblast density [39]. This signifies the role of KPNA6. The degree and betweenness centrality scores for KPNA6 were 223 and 161,133.4. Regulatory networks mediated by KPNA6, KEAP1, and NRF2 are essential determinants of the pathogenesis of PE, which regulates oxidative stress. ATPase H+ Transporting V0 Subunit E1 (ATP6V0E1/ATP6H) gene-regulated macro-autophagy was implicated in the pathogenesis of PE. ATP6H knockdown resulted in antiproliferative and apoptosis effects on BxPC-3 cells (pancreatic ductal adenocarcinoma cell line).
In normal pregnancies, placental autophagy is critical for the maintenance of cellular homeostasis that is needed for embryo and placental development [40]. Autophagy is activated in response to environmental stress, and dysregulation of autophagy is associated with various diseases [41]. Oxidative stress and hypoxia in preeclampsia are associated with an increase in the autophagic process, particularly in nutrient-deprived conditions [42]. Mitochondria are involved not only in ATP production but also in calcium homeostasis, free radical generation, cell survival, apoptosis, and necrosis [43,44,45,46]. Changes in mitochondrial dynamics, and apoptosis, are observed in preeclampsia [47]. Modification in mitochondrial gene expression influences mitochondrial homeostasis, ensuing mitochondrial dysfunction. This dysfunction leads to excessive ROS and inadequate ATP production [47,48]. Mitochondrial DNA (mtDNA) is speculated to be the marker of this dysfunction because of its inflammatory response. Oxidative stress causes membrane potential changes, inducing mitochondrial membrane depolarization and increased permeability. These disruptions will release damaged mitochondrial components, such as ROS and mtDNA, in the cytosol. As a result, there will be alteration in inflammatory and apoptotic pathways [49].
In PE, the mitochondrial apoptosis process seems to be highly altered [50]. There was a decrease in proapoptotic proteins such as p53 and BCL2-associated X and an increase in antiapoptotic proteins such as B-cell lymphoma 2 (BCL2) in term preeclamptic syncytiotrophoblast mitochondria compared with the increase in the BAX/BLC2 ratio in preterm preeclampsia [39]. In addition, soluble fms-like tyrosine kinase 1 (sFlt-1), which has antiangiogenic activity, exerted roles in oxidative stress and apoptotic pathways [51,52,53]. Differential apoptosis signaling in preterm and term placentae suggests that mitochondria promote cell survival in the placenta by suppressing the apoptosis mechanism. The regulation of programmed cell death and adequate antioxidant activity is important to improve mitochondrial adaptation and function [54]. Mitochondrial dysfunction due to excessive ROS production and reduced antioxidant capacity may result in an exaggerated apoptotic rate, placentation defect, and, therefore, preeclampsia.
The transcription factor Krüppel-Like Factor 6 (KLF6) has important roles in cell differentiation, angiogenesis, apoptosis, and proliferation. Furthermore, KLF6 is required for proper placental development [55]. KLF6 is present in both the early and late onset of severe-type PE [56]. KLF6 may mediate some of the effects of hypoxia in placental development and so has relevance in the development of PE.
PLEKHG2 is involved in cellular development, cellular assembly, and organization activity in early pregnancy and PE [57]. Decreased gene and protein expression of PLEKHG2 is involved in the breakdown of extracellular matrix proteins and tissue re-modeling activity in the human placenta [58]. Differentially expressed ZNF85 is involved in the top 10 GO terms, including DNA and ion bindings, between preeclampsia cases and controls [59]. In placental tissue, there was a correlation between ZNF85 expression and CpG methylation variation [60].

4.3. Comparison of miRNAs of Different Types of Preeclampsia

For comparison of different types (early- vs. late-onset; mild vs. severe) of preeclampsia, we selected DE genes in early-onset severe preeclampsia, late-onset severe preeclampsia, and late-onset mild preeclampsia from RNA-seq on 65 high-quality placenta samples that included 33 from 30 PE patients and 32 from 30 control subjects reported by Ren et al., 2021 [16]. These DE gene sets representing different types of PE were subjected to gene–miRNA interaction analysis. The top 20 molecular markers (genes and miRNAs with high betweenness) were compared, and the common six miRNAs (hsa-mir-124-3p, hsa-mir-1-3p, hsa-mir-146a-5p, hsa-mir-16-5p, hsa-mir-27a-3p, and hsa-mir-34a-5p) signifying all three types of PE types were identified. It is interesting to note that five (hsa-mir-1-3p, hsa-mir-146a-5p, hsa-mir-16-5p, hsa-mir-27a-3p, and hsa-mir-34a-5p) of these six miRNAs were the top miRNAs (with high betweenness) exemplified from the current analysis. Their roles governing placenta development and PE are discussed below.
The significant alterations in the expression level of miRNA and the gene pairs hsa-miR-1-3p/ANXA2 and hsa-miR-1-3p/YWHAZ were associated with extracellular matrix organization, blood vessel development, smooth muscle contraction, angiogenesis, endothelial damage, and thrombi formation that caused a pulse increase in the right uterine and the umbilical arteries, hypoxia and oxidative stress, decreased placenta mass, and poor fetal development and weight (<10 percentile) [61].
Upregulated miR-146a-5p in the preeclamptic placentae provoked impaired trophoblast cell proliferation, poor invasiveness, and migratory capacity by inhibiting Wnt2 signaling [62]. miR-16-5p was upregulated in the placental tissue of a PE rat model [63]. miR-16-5p targeted the IGF-2 gene and downregulated its expression; consequently, it increased cell autophagy and cell death in the PE placenta [64].
In contrast, the downregulation of miR-27a-3p induced the migration and invasion of trophoblast cells into the uterine endometrium. Interestingly, the expression of miR-27a-3p was negatively related to ubiquitin-specific protease 25 (USP25) in recurrent miscarriage patients [65]. USP25 can regulate the processes of invasion and migration of different types of cells. It is reasonable that miR-27a-3p-mediated downregulation of USP25 contributes to the epithelial-to-mesenchymal transition, thereby inhibiting the migration and invasion of trophoblast cells via facilitating the Wnt pathway and regulating the miR-27a-3p/ATF3 axis [65,66].
miR-34a, a downstream gene of p53, regulates the cell cycle, apoptosis, and differentiation by targeting various target genes [67]. Elevated miR-34a has been reported to aggravate DNA damage and promote cell apoptosis [68]. Placental Growth Factor (PLGF) was a target gene of miR-34a [69]. PLGF regulates vascular endothelial growth and vascular remodeling via autocrine or paracrine mechanisms. miR-34a stimulates the proliferation of vascular endothelial cells and regulates DNA repair and apoptosis of these cells via PLGF [69]. It should be noted that hsa-miR-34a-5p was upregulated in the plasma during the first trimester in pregnant women with a high risk of preterm birth compared with normal controls. miR-34 was associated with pregnancy complications, including preeclampsia and intrauterine growth restriction [70,71].

4.4. Involvement of Hub Genes in Preeclampsia Development

ARNTL, CLOCK, NR3C1, ETS1, EGR1, NFKB1, CREBBP, SMARCA4, ESR1, RELA, CREB1, VDR, TP53, EPAS1, ARNT, VHL, SP1, E2F1, TFDP1, and RB1 proteins corresponding to the upregulated hub genes are involved in cellular proliferation, growth, and differentiation, cell metabolism, inflammation, and immune modulation in ovarian, uterine, and placental tissues (Table 5). In addition, biological rhythms and preeclampsia are linked [72], and ARNTL and CLOCK hub proteins are involved in circadian pathways. Similarly, IFNG, STAT3, NFKB1, IRF1, TBX21, STAT5B, GATA3, STAT4, JUN, SP1, GATA1, EGR1, ATF3, RELA, YY1, EP300, CREB1, NR3C1, STAT5A, and STAT1 proteins corresponding to downregulated hub genes are involved in cell survival, cellular growth and development, cell homeostasis, cell metabolism, immune modulation, and inflammation in ovarian, uterine, and placental tissues (Table 6). This demonstrates that the aberration of these hub genes results in PE instead of normal pregnancy.

4.5. Hub Genes with Diagnostic and Therapeutic Perspectives

Betweenness centrality measures the extent to which a miRNA/gene lies on paths between other miRNAs/genes. MicorRNAs/genes with high betweenness may influence information passing between others within the network [73]. The top three upregulated hub genes with high degree and betweenness scores were TGFBR1, DUSP4, and TMCC1. The top three downregulated hub genes with high degree and betweenness scores were KPNA6, ATP6V0E1, and KLF6. The higher a protein’s betweenness, the more important it is for the efficient flow of goods in a network. It should be noted that proteins with a high degree centrality are of important for the diagnosis of disease, and proteins with a high degree of betweenness are important for drug discovery [74].
Differentially methylated circadian clock genes ARNTL1, CLOCK, and BHLHE40 were observed in umbilical cord leukocytes and placental tissue in PE [75]. ARNTL and CLOCK are positive activators and drive the transcription of clock genes by binding to E-box elements on their promoters. The DNA methylation status of the circadian clock and clock-controlled genes in placental tissue and umbilical cord leukocytes is different between patients with EOPE and normal controls. This may be explained by a longer exposure to placental oxidative stress as compared with pregnancies complicated by late-onset preeclampsia. In term PE patients, the most enriched pathways that were correlated were hypoxia-related pathways and the membrane trafficking and autophagy-related pathways, which increased or decreased, respectively. Furthermore, CLOCK mRNA and protein expressions were reduced in the term PE placenta [76]. This suggests that circadian clock genes could be plausible candidates for the pathogenesis and etiology of PE.
The present work contains extensive bioinformatic analysis of genes, microRNAs, proteins, and biological processes between preeclampsia and normal pregnancy. However, this study may have limitations. The retrospective data extensively analyzed in this current study were originally obtained from relatively small biological samples. However, five-fold mean differences in relative expressions were used in this study. To detect these differences with adequate statistical power (1 − β = 0.8) and statistical significance (α = 0.05), at least three samples per group were needed. The exact age (absolute age) of the pregnancy was not provided rather than stating that the normal and preeclampsia placental samples were obtained from less than 32 weeks of pregnancy. Early- and late-onset preeclampsia both result from the same problem, utero-placental malperfusion, which has different causes [77]. It has been suggested that early-onset preeclampsia is more strongly associated with internal placental factors, whereas the late-onset preeclampsia form may be primarily due to predisposing maternal factors. Some studies [78,79] found that the effect of risk factors varies according to the subtype of preeclampsia, whereas others did not [80]. Further, specific PE-related pregnancy complications are not distributed evenly across ages [81].
The association between the expression of placental tissue miRNAs and circulating miRNAs would help identify diagnostic and prognostic biomarkers. Cirkovic et al. (2021) observed increased miRNA-155 expression in both the placental tissue (SMD = 2.99, 95%CI = 0.83–5.14) and peripheral blood of women with PE (SMD = 2.06, 95%CI = 0.35–3.76) compared with women without PE [82]. However, an increased expression of miR-16a in placental tissue and significantly lower expression in peripheral blood of women with PE (SMD = –0.47, 95%CI = –0.91 to –0.03) was also observed. Several studies generated potential biomarkers utilizing samples from established PE, with less focus on prediction [83,84,85,86,87]. It is conceivable that coalescing biomarkers derived from different sources (multiple organ and cellular sources) may yield the best prediction. Utilizing large prospective cohort collections in unselected populations provides the best avenue for discovering novel biomarkers. However, miRNA expression differs according to the severity of PE [88] and during normal pregnancy [89]. So, these markers or combinations must be rigorously validated in external cohorts to ensure they achieve their potential to improve outcomes for pregnant people and their babies.

5. Conclusions

The evidence summarized in this article reveals the role of miRNAs in the pathogenesis of PE. The pathogenesis of PE is apparently determined by a range of miRNA molecules and their target genes and the degree of changes in their expression levels, which are associated with impairment of vascular and cellular development, circadian dysregulation, inflammation, and immunosuppression at the fetal–maternal interface, ultimately leading to impaired placental growth and hypoxic injury, which generally manifest as placental insufficiency. These miRNAs, genes, or proteins differentially expressed in placental tissue and in circulation can serve as novel diagnostic and therapeutic targets.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cimb46040216/s1. Supplementary File S1: Transcriptomic profiling of mRNA (up- and downregulated at 5-fold relative expression) between PE and normal placentae. Supplementary File S2: STRING-based Gene Ontology terms for upregulated genes. Supplementary File S3: STRING-based Gene Ontology terms for downregulated genes. Supplementary File S4: Enrichment path from ClueGo nested network analysis.

Author Contributions

Conceptualization, V.K. and R.K.; methodology, V.K. and R.K.; software, V.K. and R.K.; validation, V.K. and R.K.; formal analysis, V.K. and R.K.; investigation, V.K. and R.K.; resources, V.K. and R.K.; data curation, V.K. and R.K.; writing—original draft preparation, R.K.; writing—review and editing, V.K. and R.K.; visualization, V.K. and R.K.; supervision, V.K. and R.K.; project administration, V.K. and R.K. All authors have read and agreed to the published version of this manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are available from the corresponding author upon reasonable request.

Acknowledgments

The authors thank the College of Veterinary Medicine, Washington State University, for the support.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Say, L.; Chou, D.; Gemmill, A.; Tunçalp, Ö.; Moller, A.B.; Daniels, J.; Gülmezoglu, A.M.; Temmerman, M.; Alkema, L. Global causes of maternal death: A WHO systematic analysis. Lancet Glob. Health 2014, 2, e323–e333. [Google Scholar] [CrossRef]
  2. Dawson, E.L. Preeclampsia, Genomics and Public Health. 2022. Available online: https://blogs.cdc.gov/genomics/2022/10/25/preeclampsia/ (accessed on 7 January 2023).
  3. Wright, D.; Syngelaki, A.; Akolekar, R.; Poon, L.C.; Nicolaides, K.H. Competing risks model in screening for preeclampsia by maternal characteristics and medical history. Am. J. Obstet. Gynecol. 2015, 213, 62.e1–62.e10. [Google Scholar] [CrossRef] [PubMed]
  4. Moufarrej, M.N.; Vorperian, S.K.; Wong, R.J.; Campos, A.A.; Quaintance, C.C.; Sit, R.V.; Tan, M.; Detweiler, A.M.; Mekonen, H.; Neff, N.F.; et al. Early prediction of preeclampsia in pregnancy with cell-free RNA. Nature 2022, 602, 689–694. [Google Scholar] [CrossRef] [PubMed]
  5. Poon, L.C.; Shennan, A.; Hyett, J.A.; Kapur, A.; Hadar, E.; Divakar, H.; McAuliffe, F.; da Silva Costa, F.; von Dadelszen, P.; McIntyre, H.D.; et al. The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention. Int. J. Gynaecol. Obstet. 2019, 145 (Suppl. 1), 1–33. [Google Scholar] [CrossRef]
  6. Uzan, J.; Carbonnel, M.; Piconne, O.; Asmar, R.; Ayoubi, J.M. Pre-eclampsia: Pathophysiology, diagnosis, and management. Vasc. Health Risk Manag. 2011, 7, 467–474. [Google Scholar] [CrossRef]
  7. Nakashima, A.; Tsuda, S.; Kusabiraki, T.; Aoki, A.; Ushijima, A.; Shima, T.; Cheng, S.B.; Sharma, S.; Saito, S. Current Understanding of Autophagy in Pregnancy. Int. J. Mol. Sci. 2019, 20, 2342. [Google Scholar] [CrossRef] [PubMed]
  8. Gong, S.; Gaccioli, F.; Dopierala, J.; Sovio, U.; Cook, E.; Volders, P.J.; Martens, L.; Kirk, P.D.W.; Richardson, S.; Smith, G.C.S.; et al. The RNA landscape of the human placenta in health and disease. Nat. Commun. 2021, 12, 2639. [Google Scholar] [CrossRef] [PubMed]
  9. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [PubMed]
  10. Davis, S.; Meltzer, P.S. GEOquery: A bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007, 23, 1846–1847. [Google Scholar] [CrossRef]
  11. Chang, L.; Zhou, G.; Soufan, O.; Xia, J. miRNet 2.0: Network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020, 48, W244–W251. [Google Scholar] [CrossRef]
  12. Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; et al. The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021, 49, D605–D612. [Google Scholar] [CrossRef]
  13. Gustavsen, J.A.; Pai, S.; Isserlin, R.; Demchak, B.; Pico, A.R. RCy3: Network biology using Cytoscape from within R. F1000Research 2019, 8, 1774. [Google Scholar] [CrossRef]
  14. Chin, C.H.; Chen, S.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8 (Suppl. S4), S11. [Google Scholar] [CrossRef]
  15. Bindea, G.; Mlecnik, B.; Hackl, H.; Charoentong, P.; Tosolini, M.; Kirilovsky, A.; Fridman, W.H.; Pagès, F.; Trajanoski, Z.; Galon, J. ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009, 25, 1091–1093. [Google Scholar] [CrossRef]
  16. Ren, Z.; Gao, Y.; Gao, Y.; Liang, G.; Chen, Q.; Jiang, S.; Yang, X.; Fan, C.; Wang, H.; Wang, J.; et al. Distinct placental molecular processes associated with early-onset and late-onset preeclampsia. Theranostics 2021, 11, 5028–5044. [Google Scholar] [CrossRef]
  17. Ashtiani, M.; Salehzadeh-Yazdi, A.; Razaghi-Moghadam, Z.; Hennig, H.; Wolkenhauer, O.; Mirzaie, M.; Jafari, M. A systematic survey of centrality measures for protein-protein interaction networks. BMC Syst. Biol. 2018, 12, 80. [Google Scholar] [CrossRef]
  18. Tang, Y.; Li, M.; Wang, J.; Pan, Y.; Wu, F.X. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 2015, 127, 67–72. [Google Scholar] [CrossRef]
  19. Peláez, N.; Carthew, R.W. Biological robustness and the role of microRNAs: A network perspective. Curr. Top. Dev. Biol. 2012, 99, 237–255. [Google Scholar] [CrossRef]
  20. Viacava Follis, A. Centrality of drug targets in protein networks. BMC Bioinform. 2021, 22, 527. [Google Scholar] [CrossRef]
  21. Li, M.O.; Wan, Y.Y.; Panjabi, S.; Robertson, A.K.; Flavell, R.A. Transforming growth factor-beta regulation of immune responses. Ann. Rev. Immunol. 2006, 24, 99–146. [Google Scholar] [CrossRef]
  22. Hedin, C.H.; Moustakas, A. Signaling Receptors for TGF-β Family Members. Cold Spring Herb. Perspect. Biol. 2016, 8, a022053. [Google Scholar] [CrossRef]
  23. Feng, X.H.; Derynck, R. Specificity and versatility in tgf-beta signaling through Smads. Annu. Rev. Cell Dev. Biol. 2005, 21, 659–693. [Google Scholar] [CrossRef]
  24. Derynck, R.; Zhang, Y.E. Smad-dependent and Smad-independent pathways in TGF-beta family signalling. Nature 2003, 425, 577–584. [Google Scholar] [CrossRef]
  25. Djurovic, S.; Schjetlein, R.; Wisløff, F.; Haugen, G.; Husby, H.; Berg, K. Plasma concentrations of Lp(a) lipoprotein and TGF-beta1 are altered in preeclampsia. Clin. Genet. 1997, 52, 371–376. [Google Scholar] [CrossRef]
  26. Benian, A.; Madazli, R.; Aksu, F.; Uzun, H.; Aydin, S. Plasma and placental levels of interleukin-10, transforming growth factor-beta1, and epithelial-cadherin in preeclampsia. Obstet. Gynecol. 2002, 100, 327–331. [Google Scholar] [CrossRef]
  27. Naicker, T.; Khedun, S.M.; Moodley, J. Transforming growth factor beta(1) levels in platelet depleted plasma in African women with pre-eclampsia. J. Obstet. Gynaecol. 2002, 22, 279–282. [Google Scholar] [CrossRef]
  28. Zhang, J.; Dunk, C.E.; Shynlova, O.; Caniggia, I.; Lye, S.J. TGFb1 suppresses the activation of distinct dNK subpopulations in preeclampsia. EBioMedicine 2019, 39, 531–539. [Google Scholar] [CrossRef]
  29. Ramkissoon, A.; Chaney, K.E.; Milewski, D.; Williams, K.B.; Williams, R.L.; Choi, K.; Miller, A.; Kalin, T.V.; Pressey, J.G.; Szabo, S.; et al. Targeted inhibition of the dual specificity phosphatases DUSP1 and DUSP6 suppress MPNST growth via JNK. Clin. Cancer Res. 2019, 25, 4117–4127. [Google Scholar] [CrossRef]
  30. Xu, Y.; Wu, D.; Hui, B.; Shu, L.; Tang, X.; Wang, C.; Xie, J.; Yin, Y.; Sagnelli, M.; Yang, N.; et al. A novel regulatory mechanism network mediated by lncRNA TUG1 that induces the impairment of spiral artery remodeling in preeclampsia. Mol. Ther. 2022, 30, 1692–1705. [Google Scholar] [CrossRef]
  31. Christie, G.R.; Williams, D.J.; Macisaac, F.; Dickinson, R.J.; Rosewell, I.; Keyse, S.M. The dual-specificity protein phosphatase DUSP9/MKP-4 is essential for placental function but is not required for normal embryonic development. Mol. Cell. Biol. 2005, 25, 8323–8333. [Google Scholar] [CrossRef]
  32. Meng, T.; Chen, H.; Sun, M.; Wang, H.; Zhao, G.; Wang, X. Identification of differential gene expression profiles in placentas from preeclamptic pregnancies versus normal pregnancies by DNA microarrays. OMICS 2012, 16, 301–311. [Google Scholar] [CrossRef]
  33. Zhang, N.; Zhu, H.P.; Huang, W.; Wen, X.; Xie, X.; Jiang, X.; Peng, C.; Han, B.; He, G. Unraveling the structures, functions and mechanisms of epithelial membrane protein family in human cancers. Exp. Hematol. Oncol. 2022, 11, 69. [Google Scholar] [CrossRef]
  34. Hannan, N.J.; Stock, O.; Spencer, R.; Whitehead, C.; David, A.L.; Groom, K.; Petersen, S.; Henry, A.; Said, J.M.; Seeho, S.; et al. Circulating mRNAs are differentially expressed in pregnancies with severe placental insufficiency and at high risk of stillbirth. BMC Med. 2020, 18, 145. [Google Scholar] [CrossRef]
  35. Mi, C.; Ye, B.; Gao, Z.; Du, J.; Li, R.; Huang, D. BHLHE40 plays a pathological role in pre-eclampsia through upregulating SNX16 by transcriptional inhibition of miR-196a-5p. Mol. Hum. Reprod. 2020, 26, 532–548. [Google Scholar] [CrossRef]
  36. Rasmussen, K.D.; Helin, K. Role of TET enzymes in DNA methylation, development, and cancer. Genes. Dev. 2016, 30, 733–750. [Google Scholar] [CrossRef]
  37. Sun, Z.; Wu, T.; Zhao, F.; Lau, A.; Birch, C.M.; Zhang, D.D. KPNA6 (Importin {alpha}7)-mediated nuclear import of Keap1 represses the Nrf2-dependent antioxidant response. Mol. Cell. Biol. 2011, 31, 1800–1811. [Google Scholar] [CrossRef]
  38. Kopacz, A.; Kloska, D.; Forman, H.J.; Jozkowicz, A.; Grochot-Przeczek, A. Beyond repression of Nrf2: An update on Keap1. Free Radic. Biol. Med. 2020, 157, 63–74. [Google Scholar] [CrossRef]
  39. Mundal, S.B.; Rakner, J.J.; Silva, G.B.; Gierman, L.M.; Austdal, M.; Basnet, P.; Elschot, M.; Bakke, S.S.; Ostrop, J.; Thomsen, L.C.V.; et al. Divergent Regulation of Decidual Oxidative-Stress Response by NRF2 and KEAP1 in Preeclampsia with and without Fetal Growth Restriction. Int. J. Mol. Sci. 2022, 23, 1966. [Google Scholar] [CrossRef]
  40. Gong, J.S.; Kim, G.J. The role of autophagy in the placenta as a regulator of cell death. Clin. Exp. Reprod. Med. 2014, 41, 97–107. [Google Scholar] [CrossRef]
  41. Saha, S.; Panigrahi, D.P.; Patil, S.; Bhutia, S.K. Autophagy in health and disease: A comprehensive review. Biomed. Pharmacother. 2018, 104, 485–495. [Google Scholar] [CrossRef]
  42. Dewi, N.M.; Triana, R.; Chouw, A.; Darmayanti, S. Role of Autophagy in Preeclampsia. Indones. J. Clin. Pharm. 2020, 9, 50–55. [Google Scholar] [CrossRef]
  43. Spinazzola, A.; Zeviani, M. Mitochondrial diseases: A cross-talk between mitochondrial and nuclear genomes. Adv. Exp. Med. Biol. 2009, 652, 69–84. [Google Scholar] [CrossRef]
  44. Youle, R.J.; Van Der Bliek, A.M. Mitochondrial fission, fusion, and stress. Science 2012, 337, 1062–1065. [Google Scholar] [CrossRef] [PubMed]
  45. Pacheco, F.J.; Almaguel, F.G.; Evans, W.; Rios-Colon, L.; Filippov, V.; Leoh, L.S.; Rook-Arena, E.; Mediavilla-Varela, M.; De Leon, M.; Casiano, C.A. Docosahexanoic acid antagonizes TNF-a-induced necroptosis by attenuating oxidative stress, ceramide production, lysosomal dysfunction, and autophagic features. Inflamm. Res. 2014, 63, 859–871. [Google Scholar] [CrossRef]
  46. Marín, R.; Chiarello, D.I.; Abad, C.; Rojas, D.; Toledo, F.; Sobrevia, L. Oxidative stress and mitochondrial dysfunction in early onset and late-onset preeclampsia. Biochim. Biophys. Acta Mol. Basis Dis. 2020, 1866, 165961. [Google Scholar] [CrossRef]
  47. Holland, O.; Dekker Nitert, M.; Gallo, L.A.; Vejzovic, M.; Fisher, J.J.; Perkins, A.V. Placental mitochondrial function and structure in gestational disorders. Placenta 2017, 54, 2–9. [Google Scholar] [CrossRef] [PubMed]
  48. Burton, G.J.; Yung, H.W.; Murray, A.J. Mitochondrial—Endoplasmic reticulum interactions in the trophoblast: Stress and senescence. Placenta 2017, 52, 146–155. [Google Scholar] [CrossRef]
  49. McElwain, C.J.; Tuboly, E.; McCarthy, F.P.; McCarthy, C.M. Mechanisms of endothelial dysfunction in pre-eclampsia and gestational diabetes mellitus: Windows into future cardiometabolic health? Front. Endocrinol. 2020, 11, 655. [Google Scholar] [CrossRef]
  50. Bustamante, J.; Ramírez-Vélez, R.; Czerniczyniec, A.; Cicerchia, D.; Aguilar de Plata, A.C.; Lores-Arnaiz, S. Oxygen metabolism in human placenta mitochondria. J. Bioenerg. Biomembr. 2014, 46, 459–469. [Google Scholar] [CrossRef]
  51. Shi, Z.; Long, W.; Zhao, C.; Guo, X.; Shen, R.; Ding, H. Comparative proteomics analysis suggests that placental mitochondria are involved in the development of pre-eclampsia. PLoS ONE 2013, 8, e64351. [Google Scholar] [CrossRef]
  52. Jiang, Z.; Zou, Y.; Ge, Z.; Zuo, Q.; Huang, S.Y.; Sun, L. A role of sFlt-1 in oxidative stress and apoptosis in human and mouse pre-eclamptic trophoblasts. Biol. Reprod. 2015, 93, 73. [Google Scholar] [CrossRef] [PubMed]
  53. Zsengellér, Z.K.; Rajakumar, A.; Hunter, J.T.; Salahuddin, S.; Rana, S.; Stillman, I.E.; Ananth Karumanchi, S. Trophoblast mitochondrial function is impaired in preeclampsia and correlates negatively with the expression of soluble fms-like tyrosine kinase 1. Pregnancy Hypertens. 2016, 6, 313–319. [Google Scholar] [CrossRef] [PubMed]
  54. Holland, O.J.; Cuffe, J.S.M.; Dekker Nitert, M.; Callaway, L.; Kwan Cheung, K.A.; Radenkovic, F.; Perkins, A.V. Placental mitochondrial adaptations in preeclampsia associated with progression to term delivery. Cell Death Dis. 2018, 9, 1150. [Google Scholar] [CrossRef] [PubMed]
  55. Miranda, A.L.; Racca, A.C.; Kourdova, L.T.; Rojas, M.L.; Cruz Del Puerto, M.; Rodriguez-Lombardi, G.; Salas, A.V.; Travella, C.; da Silva, E.C.O.; de Souza, S.T.; et al. Krüppel-like factor 6 (KLF6) requires its amino terminal domain to promote villous trophoblast cell fusion. Placenta 2022, 117, 139–149. [Google Scholar] [CrossRef] [PubMed]
  56. Li, X.; Liu, L.; Whitehead, C.; Li, J.; Thierry, B.; Le, T.D.; Winter, M. Identifying preeclampsia-associated genes using a control theory method. Brief. Funct. Genom. 2022, 21, 296–309. [Google Scholar] [CrossRef] [PubMed]
  57. Enquobahrie, D.; Qiu, C.; Muhie, S.Y.; Williams, M.A. Maternal peripheral blood gene expression in early pregnancy and preeclampsia. Int. J. Mol. Epidemiol. Genet. 2011, 2, 78–94. [Google Scholar]
  58. Ashley, B.; Simner, C.; Manousopoulou, A.; Jenkinson, C.; Hey, F.; Frost, J.M.; Rezwan, F.I.; White, C.H.; Lofthouse, E.M.; Hyde, E.; et al. Placental uptake and metabolism of 25(OH)vitamin D determine its activity within the fetoplacental unit. Elife 2022, 11, e71094. [Google Scholar] [CrossRef]
  59. Yeung, K.R.; Chiu, C.L.; Pidsley, R.; Makris, A.; Hennessy, A.; Lind, J.M. DNA methylation profiles in preeclampsia and healthy control placentas. Am. J. Physiol. Heart Circ. Physiol. 2016, 310, H1295–H1303. [Google Scholar] [CrossRef]
  60. Chhabra, D.; Sharma, S.; Kho, A.T.; Gaedigk, R.; Vyhlidal, C.A.; Leeder, J.S.; Morrow, J.; Carey, V.J.; Weiss, S.T.; Tantisira, K.G.; et al. Fetal lung and placental methylation is associated with in utero nicotine exposure. Epigenetics 2014, 9, 1473–1484. [Google Scholar] [CrossRef]
  61. Timofeeva, A.V.; Fedorov, I.S.; Brzhozovskiy, A.G.; Bugrova, A.E.; Chagovets, V.V.; Volochaeva, M.V.; Starodubtseva, N.L.; Frankevich, V.E.; Nikolaev, E.N.; Shmakov, R.G.; et al. miRNAs and Their Gene Targets-A Clue to Differentiate Pregnancies with Small for Gestational Age Newborns, Intrauterine Growth Restriction, and Preeclampsia. Diagnostics 2021, 11, 729. [Google Scholar] [CrossRef]
  62. Peng, P.; Song, H.; Xie, C.; Zheng, W.; Ma, H.; Xin, D.; Zhan, J.; Yuan, X.; Chen, A.; Tao, J.; et al. miR-146a-5p-mediated suppression on trophoblast cell progression and epithelial-mesenchymal transition in preeclampsia. Biol. Res. 2021, 54, 30. [Google Scholar] [CrossRef]
  63. Yuan, Y.; Zhao, L.; Wang, X.; Lian, F.; Cai, Y. Ligustrazine-induced microRNA-16-5p inhibition alleviates preeclampsia through IGF-2. Reproduction 2020, 160, 905–917. [Google Scholar] [CrossRef]
  64. Akkoc, Y.; Gozuacik, D. MicroRNAs as major regulators of the autophagy pathway. Biochim. Biophys. Acta Mol. Cell Res. 2020, 1867, 118662. [Google Scholar] [CrossRef]
  65. Ding, J.; Cheng, Y.; Zhang, Y.; Liao, S.; Yin, T.; Yang, J. The miR-27a-3p/USP25 axis participates in the pathogenesis of recurrent miscarriage by inhibiting trophoblast migration and invasion. J. Cell. Physiol. 2019, 234, 19951–19963. [Google Scholar] [CrossRef]
  66. Wang, M.; Zhang, L.; Huang, X.; Sun, Q. Ligustrazine promotes hypoxia/reoxygenation-treated trophoblast cell proliferation and migration by regulating the microRNA-27a-3p/ATF3 axis. Arch. Biochem. Biophys. 2023, 737, 109522. [Google Scholar] [CrossRef]
  67. Chen, F.; Hu, S.J. Effect of microRNA-34a in cell cycle, differentiation, and apoptosis: A review. J. Biochem. Mol. Toxicol. 2012, 26, 79–86. [Google Scholar] [CrossRef]
  68. Kofman, A.V.; Kim, J.; Park, S.Y.; Dupart, E.; Letson, C.; Bao, Y.; Ding, K.; Chen, Q.; Schiff, D.; Larner, J.; et al. microRNA-34a promotes DNA damage and mitotic catastrophe. Cell Cycle 2013, 12, 3500–3511. [Google Scholar] [CrossRef]
  69. Liu, C.; Zhao, Y.; Xu, X.; Zhang, L.; Cui, F.; Chen, Q.; Li, H.; Sang, R.; Li, G.; He, Y. Puerarin Reduces Radiation-Induced Vascular Endothelial Cell Damage Via miR-34a/Placental Growth Factor. Dose Response 2022, 20, 15593258211068649. [Google Scholar] [CrossRef]
  70. Doridot, L.; Houry, D.; Gaillard, H.; Chelbi, S.T.; Barbaux, S.; Vaiman, D. miR-34a expression, epigenetic regulation, and function in human placental diseases. Epigenetics 2014, 9, 142–151. [Google Scholar] [CrossRef]
  71. Sun, M.; Chen, H.; Liu, J.; Tong, C.; Meng, T. MicroRNA-34a inhibits human trophoblast cell invasion by targeting MYC. BMC Cell Biol. 2015, 16, 21. [Google Scholar] [CrossRef]
  72. Zhou, G.; Winn, E.; Nguyen, D.; Kasten, E.P.; Petroff, M.G.; Hoffmann, H.M. Co-alterations of circadian clock gene transcripts in human placenta in preeclampsia. Sci. Rep. 2022, 12, 17856. [Google Scholar] [CrossRef]
  73. Gu, Z.; Liu, J.; Cao, K.; Zhang, J.; Wang, J. Centrality-based pathway enrichment: A systematic approach for finding significant pathways dominated by key genes. BMC Syst. Biol. 2012, 6, 56. [Google Scholar] [CrossRef]
  74. Wang, X.; Thijssen, B.; Yu, H. Target essentiality and centrality characterize drug side effects. PLoS Comput. Biol. 2013, 9, e1003119. [Google Scholar] [CrossRef]
  75. Van den Berg, C.B.; Chaves, I.; Herzog, E.M.; Willemsen, S.P.; van der Horst, G.T.J.; Steegers-Theunissen, R.P.M. Early and late-onset preeclampsia and the DNA methylation of circadian clock and clock-controlled genes in placental and newborn tissues. Chronobiol. Int. 2017, 34, 921–932. [Google Scholar] [CrossRef]
  76. Li, Y.; Li, J.; Hou, Y.; Huang, L.; Bian, Y.; Song, G.; Qiao, C. Circadian clock gene Clock is involved in the pathogenesis of preeclampsia through hypoxia. Life Sci. 2020, 247, 117441. [Google Scholar] [CrossRef]
  77. Redman, C.W. Early and late onset preeclampsia: Two sides of the same coin. Pregnancy Hypertens. 2017, 7, 58. [Google Scholar] [CrossRef]
  78. Aksornphusitaphong, A.; Phupong, V. Risk factors of early and late onset pre-eclampsia. J. Obstet. Gynaecol. Res. 2013, 39, 627–631. [Google Scholar] [CrossRef]
  79. Lisonkova, S.; Joseph, K.S. Incidence of preeclampsia: Risk factors and outcomes associated with early- versus late-onset disease. Am. J. Obstet. Gynecol. 2013, 209, 544.e1–544.e12. [Google Scholar] [CrossRef]
  80. Wójtowicz, A.; Zembala-Szczerba, M.; Babczyk, D.; Kołodziejczyk-Pietruszka, M.; Lewaczyńska, O.; Huras, H. Early- and Late-Onset Preeclampsia: A Comprehensive Cohort Study of Laboratory and Clinical Findings according to the New ISHHP Criteria. Int. J. Hypertens. 2019, 2019, 4108271. [Google Scholar] [CrossRef]
  81. Sheen, J.J.; Huang, Y.; Andrikopoulou, M.; Wright, J.D.; Goffman, D.; D’Alton, M.E.; Friedman, A.M. Maternal Age and Preeclampsia Outcomes during Delivery Hospitalizations. Am. J. Perinatol. 2020, 37, 44–52. [Google Scholar] [CrossRef]
  82. Cirkovic, A.; Stanisavljevic, D.; Milin-Lazovic, J.; Rajovic, N.; Pavlovic, V.; Milicevic, O.; Savic, M.; Kostic Peric, J.; Aleksic, N.; Milic, N.; et al. Preeclamptic Women Have Disrupted Placental microRNA Expression at the Time of Preeclampsia Diagnosis: Meta-Analysis. Front. Bioeng. Biotechnol. 2021, 9, 782845. [Google Scholar] [CrossRef] [PubMed]
  83. Yoffe, L.; Gilam, A.; Yaron, O.; Polsky, A.; Farberov, L.; Syngelaki, A.; Nicolaides, K.; Hod, M.; Shomron, N. Early Detection of Preeclampsia Using Circulating Small Non-Coding RNA. Sci. Rep. 2018, 8, 3401. [Google Scholar] [CrossRef] [PubMed]
  84. Zhou, S.; Li, J.; Yang, W.; Xue, P.; Yin, Y.; Wang, Y.; Tian, P.; Peng, H.; Jiang, H.; Xu, W.; et al. Noninvasive preeclampsia prediction using plasma cell-free RNA signatures. Am. J. Obstet. Gynecol. 2023, 229, 553.e1–553.e16. [Google Scholar] [CrossRef] [PubMed]
  85. Ogoyama, M.; Takahashi, H.; Suzuki, H.; Ohkuchi, A.; Fujiwara, H.; Takizawa, T. Non-Coding RNAs and Prediction of Preeclampsia in the First Trimester of Pregnancy. Cells 2022, 11, 2428. [Google Scholar] [CrossRef] [PubMed]
  86. Morey, R.; Poling, L.; Srinivasan, S.; Martinez-King, C.; Anyikam, A.; Zhang-Rutledge, K.; To, C.; Hakim, A.; Mochizuki, M.; Verma, K.; et al. Discovery and verification of extracellular microRNA biomarkers for diagnostic and prognostic assessment of preeclampsia at triage. Sci. Adv. 2023, 9, eadg7545. [Google Scholar] [CrossRef] [PubMed]
  87. MacDonald, T.M.; Walker, S.P.; Hannan, N.J.; Tong, S.; Kaitu’u-Lino, T.J. Clinical tools and biomarkers to predict preeclampsia. EBioMedicine 2022, 75, 103780. [Google Scholar] [CrossRef]
  88. Jairajpuri, D.S.; Malalla, Z.H.; Mahmood, N.; Almawi, W.Y. Circulating microRNA expression as predictor of preeclampsia and its severity. Gene 2017, 627, 543–548. [Google Scholar] [CrossRef]
  89. Cai, M.; Kolluru, G.K.; Ahmed, A. Small Molecule, Big Prospects: MicroRNA in Pregnancy and Its Complications. J. Pregnancy 2017, 2017, 6972732. [Google Scholar] [CrossRef]
Cimb 46 00216 g001
Figure 1. Gene–miRNA interaction networks for upregulated genes including 45 upregulated genes that interacted with 829 miRNAs and 33 transcription factors (p < 0.05). Green circles denote genes. Yellow circles denote transcription factors. Size indicates significance. Blue squares denote miRNAs.
Figure 1. Gene–miRNA interaction networks for upregulated genes including 45 upregulated genes that interacted with 829 miRNAs and 33 transcription factors (p < 0.05). Green circles denote genes. Yellow circles denote transcription factors. Size indicates significance. Blue squares denote miRNAs.
Cimb 46 00216 g001
Cimb 46 00216 g002
Figure 2. Gene–miRNA interaction network for downregulated genes including 36 downregulated genes that interacted with 1057 miRNAs and 39 transcription factors (p < 0.05). Green circles denote genes. Yellow circles denote transcription factors. Size indicates significance. Blue squares denote miRNAs.
Figure 2. Gene–miRNA interaction network for downregulated genes including 36 downregulated genes that interacted with 1057 miRNAs and 39 transcription factors (p < 0.05). Green circles denote genes. Yellow circles denote transcription factors. Size indicates significance. Blue squares denote miRNAs.
Cimb 46 00216 g002
Cimb 46 00216 g003
Figure 3. Gene–miRNA interaction network for the top 20 upregulated genes. Green circles denote genes. Yellow circles denote transcription factors. Size indicates significance. Blue squares denote miRNAs.
Figure 3. Gene–miRNA interaction network for the top 20 upregulated genes. Green circles denote genes. Yellow circles denote transcription factors. Size indicates significance. Blue squares denote miRNAs.
Cimb 46 00216 g003
Cimb 46 00216 g004
Figure 4. Gene–miRNA interaction network for the top 20 downregulated genes. Green circles denote genes. Yellow circles denote transcription factors. Size indicates significance. Blue squares denote miRNAs.
Figure 4. Gene–miRNA interaction network for the top 20 downregulated genes. Green circles denote genes. Yellow circles denote transcription factors. Size indicates significance. Blue squares denote miRNAs.
Cimb 46 00216 g004
Cimb 46 00216 g005
Figure 5. STRING protein–protein interaction (PPI) network. PPI network for the upregulated genes (≥5-fold expression; 78 nodes; 193 edges; PPI enrichment with p < 1.0 × 10−16). The node color represents proteins. The edges represent interactions. Note: Some interacting proteins/transcription factors are common for upregulated and downregulated genes.
Figure 5. STRING protein–protein interaction (PPI) network. PPI network for the upregulated genes (≥5-fold expression; 78 nodes; 193 edges; PPI enrichment with p < 1.0 × 10−16). The node color represents proteins. The edges represent interactions. Note: Some interacting proteins/transcription factors are common for upregulated and downregulated genes.
Cimb 46 00216 g005
Cimb 46 00216 g006
Figure 6. STRING protein–protein interaction (PPI) network. PPI network for the downregulated genes (≥5-fold expression; 73 nodes; 293 edges; PPI enrichment with p < 1.0 × 10−16). The node color represents proteins. The edges represent interactions. Note: Some interacting proteins/transcription factors are common for upregulated and downregulated genes.
Figure 6. STRING protein–protein interaction (PPI) network. PPI network for the downregulated genes (≥5-fold expression; 73 nodes; 293 edges; PPI enrichment with p < 1.0 × 10−16). The node color represents proteins. The edges represent interactions. Note: Some interacting proteins/transcription factors are common for upregulated and downregulated genes.
Cimb 46 00216 g006
Cimb 46 00216 g007
Figure 7. Interactions among hub genes (ARNT, ARNTL, CLOCK, CREBBP, CREBP1, E2F1, EGR1, EPAS1, ESR1, ETS1, NFKB1, NR3C1, RB1, RELA, SMARCA4, SP1, TFD1, TP53, VDR and VHL) of upregulated genes in the protein–protein interaction network. The dark to light colors denotes high to low degrees of expression. Black lines indicate interactions between genes.
Figure 7. Interactions among hub genes (ARNT, ARNTL, CLOCK, CREBBP, CREBP1, E2F1, EGR1, EPAS1, ESR1, ETS1, NFKB1, NR3C1, RB1, RELA, SMARCA4, SP1, TFD1, TP53, VDR and VHL) of upregulated genes in the protein–protein interaction network. The dark to light colors denotes high to low degrees of expression. Black lines indicate interactions between genes.
Cimb 46 00216 g007
Cimb 46 00216 g008
Figure 8. Interactions among hub genes (ATF3, CREB1, EGR1, EP300, GATA1, GATA3, IFNG, IRF1, JUN, NFKB1, NR3C1, RELA, SP1, STAT1, STAT3, STAT4, STAT5A, STAT5B, TBX21, and YY1) of downregulated genes in the protein–protein interaction network. The dark to light colors denotes high to low degrees of expression. Black lines indicate interactions between genes.
Figure 8. Interactions among hub genes (ATF3, CREB1, EGR1, EP300, GATA1, GATA3, IFNG, IRF1, JUN, NFKB1, NR3C1, RELA, SP1, STAT1, STAT3, STAT4, STAT5A, STAT5B, TBX21, and YY1) of downregulated genes in the protein–protein interaction network. The dark to light colors denotes high to low degrees of expression. Black lines indicate interactions between genes.
Cimb 46 00216 g008
Cimb 46 00216 g009
Figure 9. ClueGO analysis of upregulated genes. (A) Functionally grouped network with terms as nodes linked based on their kappa score level (≥0.4), where only the label of the most significant term per group is shown. The node size represents the term enrichment significance. Functionally related groups partially overlap. The grey color gradient shows the gene proportion of each cluster associated with the term. (B) Overview chart with functional groups including specific terms for upregulated genes. ** p < 0.001. (C) GO/pathway terms specific for upregulated genes. The bars represent the number of genes (in red) associated with the terms. The percentage of genes per term is shown as a bar label.
Figure 9. ClueGO analysis of upregulated genes. (A) Functionally grouped network with terms as nodes linked based on their kappa score level (≥0.4), where only the label of the most significant term per group is shown. The node size represents the term enrichment significance. Functionally related groups partially overlap. The grey color gradient shows the gene proportion of each cluster associated with the term. (B) Overview chart with functional groups including specific terms for upregulated genes. ** p < 0.001. (C) GO/pathway terms specific for upregulated genes. The bars represent the number of genes (in red) associated with the terms. The percentage of genes per term is shown as a bar label.
Cimb 46 00216 g009
Cimb 46 00216 g010
Figure 10. ClueGO analysis of downregulated genes. (A) Functionally grouped network with terms as nodes linked based on their kappa score level (≥0.4), where only the label of the most significant term per group is shown. The node size represents the term enrichment significance. Functionally related groups partially overlap. The grey color gradient shows the gene proportion of each cluster associated with the term. (B) Overview chart with functional groups including specific terms for upregulated genes. ** p < 0.001. (C) GO/pathway terms specific for upregulated genes. The bars represent the number of genes associated with the terms. The percentage of genes per term is shown as a bar label.
Figure 10. ClueGO analysis of downregulated genes. (A) Functionally grouped network with terms as nodes linked based on their kappa score level (≥0.4), where only the label of the most significant term per group is shown. The node size represents the term enrichment significance. Functionally related groups partially overlap. The grey color gradient shows the gene proportion of each cluster associated with the term. (B) Overview chart with functional groups including specific terms for upregulated genes. ** p < 0.001. (C) GO/pathway terms specific for upregulated genes. The bars represent the number of genes associated with the terms. The percentage of genes per term is shown as a bar label.
Cimb 46 00216 g010
Table 1. Top 20 upregulated genes in the placenta with high degree and betweenness centrality in preeclamptic compared to normotensive women.
Table 1. Top 20 upregulated genes in the placenta with high degree and betweenness centrality in preeclamptic compared to normotensive women.
High Degree CentralityHigh Betweenness Centrality
#IDDegreeBetweenness#IDDegreeBetweenness
1TGFBR112962,386.636031DUSP412466,214.0901
2DUSP412466,214.09012TGFBR112962,386.63603
3TMCC112260,207.542043TMCC112260,207.54204
4EMP111359,488.022094EMP111359,488.02209
5BHLHE4011153,832.727715BHLHE4011153,832.72771
6PDS5A10546,221.209356PDS5A10546,221.20935
7PPIG9641,670.735437PPIG9641,670.73543
8IPPK7028,805.246428SFT2D36132,179.26096
9STIP16527,238.037649IPPK7028,805.24642
10DESI26217,175.8383510STIP16527,238.03764
11SFT2D36132,179.2609611PHLDA25226,712.22297
12SORL15921,899.5005712FLT15723,913.42422
13FLT15723,913.4242213MRPL494423,540.21993
14PHLDA25226,712.2229714GJB74022,523.85633
15MRPL494423,540.2199315SORL15921,899.50057
16GJB74022,523.8563316TMEM543618,180.58635
17TMEM543618,180.5863517DESI26217,175.83835
18DHFR3410,104.0344718SSX52213,882.13629
19RASSF63213,330.896619RASSF63213,330.8966
20HLA-DQA12812,741.0327820HLA-DQA12812,741.03278
All genes that showed high degree centrality also had high betweenness centrality except the gene in bold letters.
Table 2. Top 20 downregulated genes in the placenta with high degree and betweenness centrality in preeclamptic compared to normotensive women.
Table 2. Top 20 downregulated genes in the placenta with high degree and betweenness centrality in preeclamptic compared to normotensive women.
High Degree CentralityHigh Betweenness Centrality
#IDDegreeBetweenness#IDDegreeBetweenness
1KPNA6223161,133.3711KPNA6223161,133.371
2ATP6V0E115290,977.34192ATP6V0E115290,977.34186
3KLF612975,754.06893KLF612975,754.06887
4SIKE111860,992.27684PLEKHG211271,728.72334
5PLEKHG211271,728.72335ZNF859867,140.10746
6ZNF859867,140.10756SIKE111860,992.27675
7EMC39253,114.76737EMC39253,114.76726
8GALNT28338,016.45278VDAC26952,426.38509
9TBC1D158348,514.1119TBC1D158348,514.11101
10ATF28135,905.829710GALNT28338,016.45269
11VDAC26952,426.385111ATF28135,905.8297
12AMBRA15527,918.540712IFNG4132,732.16167
13RAB40C5123,353.29513AMBRA15527,918.54066
14ZNF2575127,233.843814ZNF4864927,772.96946
15ZNF4295124,069.76715EXOC24927,644.24328
16EXOC24927,644.243316ZNF2575127,233.84385
17ZNF4864927,772.969517GUCY1A24725,416.96916
18ZNF2534723,149.480318ZNF4295124,069.76705
19GUCY1A24725,416.969219RAB40C5123,353.29504
20POU3F24422,680.877320ZNF2534723,149.48028
All genes that showed high degree centrality also had high betweenness centrality except the genes in bold letters.
Table 3. Top 20 upregulated genes (in the placenta with a high degree and betweenness centrality) and their tissue and single-cell expressions, associated genes, and functions.
Table 3. Top 20 upregulated genes (in the placenta with a high degree and betweenness centrality) and their tissue and single-cell expressions, associated genes, and functions.
GeneTissue ExpressionSingle-Cell Normalized Expression (nTPM)Associated GenesFunctions
TGFBR1Ovary, uterus placentaCyto 22.1; Syncytio: 18.4; extravillous: 7.3; Endometrium 21.2FKBP1A, TGFB1, TGFB3, TGFBR2, SMAD7Regulates cellular process: proliferation, maturation, differentiation, motility, and apoptosis
DUSP4Ovary, uterus placentaCyto 3.0; Syncytio: 24.9; extravillous: 48.8; Endometrium 13.7MAPK1, MAPK3, MAPK7, MAPK8, MAPK9Regulates cell proliferation and differentiation
TMCC1Ovary, uterus placentaCyto: 10.4; Syncytio: 27.3; extravillous: 0.6; Endometrium 14.2PLEC, RSP10, RSP10-NUDT3, RSP12, RSP18A, RSP19 Regulates endosome fission; endosome membrane tubulation; and membrane fission
EMP1Ovary, uterus placentaCyto: 0.7; Syncytio: 0.7; extravillous: 0.6; Endometrium 161.6CCL4, LPAR6, LAPTM4B, PMP22, SMIM3Regulates cell proliferation and migration
BHLHE40Ovary, uterus placentaCyto: 31,8; Syncytio: 165.5; extravillous: 94.7; Endometrium 68.0BTRC, HDAC1, RXRA, TP53, SMAP2 Regulates circadian rhythm and cell differentiation
PDS5AOvary, uterus placentaCyto: 32.6; Syncytio: 37.7; extravillous: 39.0; Endometrium 47.3RAD21, SMC1A, SMC3, STAG2, WAPALRegulates chromatid cohesion during mitosis
PPIGOvary, uterus placentaCyto: 186.3; Syncytio: 241.4; extravillous: 200.9; Endometrium 146.2BUD31, PCBP1, PRPF8, PRPF19, SNW1Regulates folding, transport, and assembly of proteins, and pre-mRNA splicing
IPPKOvary, uterus placentaCyto: 10.9; Syncytio: 23.7; extravillous: 14.8;
Endometrium 4.6		EPB41L4A, FRMD5, LPAR1, MPKAPK5, VRK1Regulates DNA repair, endocytosis, and mRNA export
STIP1Ovary, uterus placentaCyto: 127.7; Syncytio: 210.3; extravillous: 143.4; Endometrium 48.8HSP8, HSPA1A, HSP90AA1, HSP90AB1, PTGES3Regulates heat shock proteins
DESI2Ovary, uterus placentaCyto: 30.7; Syncytio: 43.8; extravillous: 42.9; Endometrium 39.9DDX5, E2F8, NPM1, NUP107, RPA1, UBE21Regulates protein deubiquitination
SFT2D3Ovary, uterus placentaCyto: 4.3; Syncytio: 3.0; extravillous: 4.2; Endometrium 8.9ADHFE1, ADACC, COG1, PSAT1, TMEM24, TSGA13Regulates protein transport and vesicle-mediated transport
SORL1Ovary, uterus placentaCyto: 0.2; Syncytio: 0.4; extravillous: 2444.5; Endometrium 2.9APP, APOE, CGA1, LRPAP1, VPS35 Regulates protein transport
FLT1Ovary, uterus placentaCyto: 182.7; Syncytio: 10,058.3; extravillous: 980.8; Endometrium 1.4KDR, PGF, PTPN11, VEGFA, VEGFBRegulates angiogenesis and vasculogenesis
PHLDA2Ovary, uterus placentaCyto: 4565.5; Syncytio: 365.0; extravillous: 336.1; Endometrium 27.9RANBP9, SUCO, SRCRegulates fetal and placental growth
MRPL49Ovary, uterus placentaCyto: 63.8; Syncytio: 119.5; extravillous: 49.1; Endometrium 11.3COX15, TIMM10, METTL18, NXF1, FBXW11Regulates protein metabolism and mitochondrial translation
GJB7Ovary, uterus placentaCyto: 10.7; Syncytio: 8.9; extravillous: 4.7; Endometrium 0.7ARVCF, FYN, PAG1, PPP2R5E, ULBP2Regulates gap junction trafficking and vesicle-mediated transport
TMEM54Ovary, uterus placentaCyto: 48.2; Syncytio: 64.7; extravillous: 169.6; Endometrium 16.9CREB3, CDK2, HDAC1, LMNA, PEX19, RARARegulates membrane function
DHFROvary, uterus placentaCyto: 34.5; Syncytio: 12.1; extravillous: 40.1; Endometrium 6.9FOX1, HSPD1, MDM2, FKBP1A, TP53,Regulates folate metabolism and glycine and purine synthesis
RASSF6Ovary, uterus placentaCyto: 54.5; Syncytio: 48.9; extravillous: 24.8; Endometrium 2.0AMY1A, DLG1, KDM3A, HECTD1, SAV1, STK4Regulates cell cycle arrest and apoptosis
HLA-DQA1Ovary, uterus placentaCyto: 6.9; Syncytio: 4.8; extravillous: 10.7; Endometrium 33.4CD74, HLA-DQB1, KCNJ8, ST7, SLC38A9, TMEM214Regulates immune function
SSX5Ovary, uterus placentaCyto: 0; Syncytio: 0; extravillous: 0; Endometrium 0AGTRAP, PCBD2, NFE2, SSX2, ZSCAN1Regulates immune function
Cyto—Cytotrophoblast; Syncytio—syncytiotrophoblast; extravillous—extravillous trophoblast; Endometrium—endometrial stromal cells.
Table 4. Top 20 downregulated genes (in the placenta with a high degree and betweenness centrality) and their tissue and single-cell expressions, associated genes, and functions.
Table 4. Top 20 downregulated genes (in the placenta with a high degree and betweenness centrality) and their tissue and single-cell expressions, associated genes, and functions.
GeneTissue ExpressionSingle-Cell Normalized Expression (nTPM)Associated GenesFunctions
KPNA6Ovary, uterus placentaCyto 41.2; Syncytio: 138.3; extravillous: 37.6; Endometrium 39.7HDAC1, KPNB1, LMNA, NUP50, RELBRegulates protein transport
ATP6V0E1Ovary, uterus placentaCyto 511.0; Syncytio: 985.2; extravillous: 643.8; Endometrium 199.9ACP2, SLC7A2, CCDC115, PTPRF, TMEM199 Regulates protein transport and pH of intercellular compartments
KLF6Ovary, uterus placentaCyto: 176.5; Syncytio: 217.0; extravillous: 539.4; Endometrium 616.8HDAC3, KLF4, LCOR, RELA, SP1 Regulates cell growth
SIKE1Ovary, uterus placentaCyto: 30.8; Syncytio: 38.0; extravillous: 36.2; Endometrium 34.4PPP2R1A, PPP2CA, STRN4, STK24, STK25, TRAF3IP3Plays inhibitory role in virus- and TLR3-triggered IRF3
PLEKHG2Ovary, uterus placentaCyto: 0.7; Syncytio: 0.6; extravillous: 2.9; Endometrium 18.6CDC42, GNB1, GNG2, RAC1, RHOA Regulates lymphocyte chemotaxis via Rac and Cdc42 activation and actin polymerization
ZNF85Ovary, uterus placentaCyto: 10.5; Syncytio: 6.1; extravillous: 15.4; Endometrium 4.0CEP76, TRIM28Regulates DNA templated transcription
EMC3Ovary, uterus placentaCyto: 50.9; Syncytio: 91.6; extravillous: 57.7; Endometrium 50.2EMC1, EMC2, EMC4, EMC6, MMGT1Regulates membrane insertase activity
GALNT2Ovary, uterus placentaCyto: 6.9; Syncytio: 14.1; extravillous: 141.2;
Endometrium 13.3		AP4M1, AP4S1, MMGT1, MRPS5, ZMPSTE24Regulates glycosylation of protein
TBC1D15Ovary, uterus placentaCyto: 20.5; Syncytio: 48.2; extravillous: 16.3; Endometrium 39.6CCDC121, CEP23, OPTN, TBC1D17, UBXN8Regulates GTPase activator activity and mitochondrial morphology
ATF2Ovary, uterus placentaCyto: 13.9; Syncytio: 6.0; extravillous: 13.5; Endometrium 28.1FOS, JUN, MAPK8, MAPK9, MAPK14Regulates transcription of various genes involved in apoptosis, cell growth, proliferation, inflammation, and DNA damage response
VDAC2Ovary, uterus placentaCyto: 334.2; Syncytio: 399.4; extravillous: 470.9; Endometrium 107.0COX4I1, NDUFS4, PHB, PHB2, VDAC2Regulates oxidative metabolism, ion transport, cell apoptosis
AMBRA1Ovary, uterus placentaCyto: 3.9; Syncytio: 7.3; extravillous: 2.0; Endometrium 4.8BECN1, CUL4A, DDA1, DDB1, TCEB2 Regulates mitophagy, cell proliferation, cell cycle progression
RAB40COvary, uterus placentaCyto: 26.0; Syncytio: 51.3; extravillous: 15.8; Endometrium 6.7CUX2, CUX2, ENSP00000447000, RAB40B, SARNPRegulates protein metabolism and autophagy
ZNF257Ovary, uterus placentaCyto: 4.2; Syncytio: 3.0; extravillous: 5.5; Endometrium 1.4HIST1H3A, SSRP1, CTCF, GL13, ZNF 513, ZNF710, ZNF768Regulates DNA templated transcription, apoptosis, protein folding and assembly, and lipid binding
ZNF429Ovary, uterus placentaCyto: 14.7; Syncytio: 12.7; extravillous: 11.7; Endometrium 10.3CTCF, GL13, ZNF 513, ZNF710, ZNF768Regulates transcription by RNA polymerase II, apoptosis, protein folding and assembly, and lipid binding
EXOC2Ovary, uterus placentaCyto: 15.3.; Syncytio: 13.9; extravillous: 6.6; Endometrium 6.2EXOC3, EXOC4, EXOC5, EXOC6, EXOC7Regulates polarized targeting of exocytic vesicles to specific docking sites on the plasma membrane
ZNF486Ovary, uterus placentaCyto: 4.6; Syncytio: 1.8; extravillous: 15.7; Endometrium 6.5CTCF, GL13, ZNF 513, ZNF710, ZNF768Regulates DNA templated transcription, apoptosis, protein folding and assembly, and lipid binding
ZNF253Ovary, uterus placentaCyto: 5.5; Syncytio: 4.5; extravillous: 3.4; Endometrium 5.2AKR1B1, LDOC1, CTCF, ZNF 513, ZNF710Regulates DNA templated transcription, apoptosis, protein folding and assembly, and lipid binding
GUCY1A2Ovary, uterus placentaCyto: 0.1; Syncytio: 0.2; extravillous: 0.0; Endometrium 2.0GUCY1B3, DLG1, DLG2, DLG3, DLG4Regulates conversion of GTP to 3’,5’-cyclic GMP and pyrophosphate
POU3F2Ovary, uterus placentaCyto: 0.0; Syncytio: 0.0; extravillous: 0.1; Endometrium 0.1POU4F1, POU4F2, POU4F3, SOX10, TFCP2Regulates neuronal differentiation and activation of CRH regulated genes
IFNGOvary, uterus placentaCyto: 0.1; Syncytio: 0.1; extravillous: 0.1; Endometrium 0.9FOXP3, IFNGR1, IFNGR2, RUNX1, TRIM2Regulates cellular response to viral and microbial infections
Cyto—Cytotrophoblast; Syncytio—syncytiotrophoblast; extravillous—extravillous trophoblast; Endometrium—endometrial stromal cells.
Table 5. Top 20 upregulated hub genes and their tissue and single-cell expressions, associated genes, and functions.
Table 5. Top 20 upregulated hub genes and their tissue and single-cell expressions, associated genes, and functions.
Hub GeneTissue ExpressionSingle-Cell Normalized Expression (nTPM)Associated GenesFunctions
ARNTLOvary, uterus placentaCyto 17.0; Syncytio: 6.1; extravillous: 1.3; Endometrium 13.9CLOCK, CRY1 CRY2, NPAS2, PER2Regulates molecular circadian rhythm, myogenesis, adipogenesis, hormone production, cell proliferation
CLOCKOvary, uterus placentaCyto 11.3; Syncytio: 6.3; extravillous: 7.0; Endometrium 35.2ARNTL, CIPC, CRY1 CRY2, PER2Regulates molecular circadian rhythm
NR3C1Ovary, uterus placentaCyto: 48.6; Syncytio: 36.6; extravillous: 44.2; Endometrium 28.5HSP90AA1, NCOA1, NCOa2, NCOR, SMARCA4Regulates hypothalamic–pituitary–adrenal (HPA) axis by modulating availability of the cortisol
ETS1Ovary, uterus placentaCyto: 0.1; Syncytio: 0.3; extravillous: 0.4; Endometrium 49.7CREBBP, FOXO1, NFKB2, PAX5, RUNX1Regulates immune cell function
EGR1Ovary, uterus placentaCyto: 154.9; Syncytio: 165.7; extravillous: 106.1; Endometrium 783.3EP300, JUNDB, JUNDD, NAB1, TP53 Regulates attachment and survival of normal cells and induces apoptosis in abnormal cells
NFKB1Ovary, uterus placentaCyto: 15.2; Syncytio:13.5; extravillous: 17.3; Endometrium 60.4NFKB1A, RELA, CHUK, IFBKB, RELBRegulate genes
CREBBPOvary, uterus placentaCyto: 17.2; Syncytio: 32.2; extravillous: 11.1; Endometrium 52.1CREB1, HIF1A, KMT2A, MYB, TP53Regulates cell growth and division and prompting cells to mature and differentiate
SMARCA4Ovary, uterus placentaCyto: 72.5; Syncytio: 70.3; extravillous: 62.4;
Endometrium 46.0		SMARCB1, SMARCC1, SMARCC2, SMARCD1, SMARCE1Regulates chromatin remodeling
ESR1Ovary, uterus placentaCyto: 0.1; Syncytio: -; extravillous: -; Endometrium 72.4EP300, NCOA1, NCOA2, NR2F1, NR2F2Regulates many biological functions including growth, differentiation and function of female reproductive system, hormone binding, immune function
RELAOvary, uterus placentaCyto: 23.0; Syncytio: 47.7; extravillous: 27.7; Endometrium 24.8BRD4, CREBBPEP300, NFKB1, NFKB1ARegulate genes involved in apoptosis, inflammation, the immune response, and proliferation
CREB1Ovary, uterus placentaCyto: 30.1; Syncytio: 18.7; extravillous: 25.9; Endometrium 37.8CREBBP, CRTC2, EP300, RPS6KA5, TP53Regulates proliferation, migration, and invasion of cells
VDROvary, uterus placentaCyto: 0.1; Syncytio: 0.2; extravillous: 0.1; Endometrium 0.5NCOA1, NCOA2, NCOA3, MED1, RXRA Induces a surge of cell signaling to maintain healthy Ca2+ levels that serve to regulate several biological functions
TP53Ovary, uterus placentaCyto: 39.7; Syncytio: 20.4; extravillous: 40.6; Endometrium 28.3CREBBP, EP300, MDM2, MDM4, RPZ27ARegulates cell division and apoptosis
EPAS1Ovary, uterus placentaCyto: 118.5; Syncytio: 365.0; extravillous: 336.1; Endometrium 31.3ARNT, EGLN1, VHL, TCEB1, TCEB2Regulates cell division, angiogenesis, adaptation to changing oxygen level
ARNTOvary, uterus placentaCyto: 24.1; Syncytio: 32.4; extravillous: 40.3; Endometrium 21.8AHR, EPAS1, HIF1A, NPAS3, SIM2Regulates placentation
VHLOvary, uterus placentaCyto: 35.3; Syncytio: 34.0; extravillous: 35.0; Endometrium 37.8EPAS1, CUL2, HIF1A, TCEB1, TCEB2Regulates cell growth and division
SP1Ovary, uterus placentaCyto: 16.3; Syncytio: 22.4; extravillous: 17.0; Endometrium 22.3EP300, ESR1, HDAC1, HDAC2, TP53Regulates cell cycle, hormonal activation, apoptosis, and angiogenesis
E2F1Ovary, uterus placentaCyto: 5.3; Syncytio: 2.1; extravillous: 8.8; Endometrium 1.0CCNA2, DP2, RB1, RBL1, TFDP1Regulates cell cycle progression, DNA repair, apoptosis
TFDP1Ovary, uterus placentaCyto: 85.2; Syncytio: 60.0; extravillous: 123.9; Endometrium 27.1E2F1, E2F4, E2F5, E2F6, RB1Regulates cell cycle progression
RB1Ovary, uterus placentaCyto: 6.9; Syncytio: 4.8; extravillous: 10.7; Endometrium 33.4CCND1, CDK4, DNMT1, E2F1, TFDP1Regulates cell growth and division
Cyto—Cytotrophoblast; Syncytio—syncytiotrophoblast; extravillous—extravillous trophoblast; Endometrium—endometrial stromal cells.
Table 6. Top 20 downregulated hub genes and their tissue and single-cell expressions, associated genes, and functions.
Table 6. Top 20 downregulated hub genes and their tissue and single-cell expressions, associated genes, and functions.
Hub GeneTissue ExpressionSingle-Cell Normalized Expression (nTPM)Associated GenesFunctions
IFNGOvary, uterus placentaEndometrium 0.9IFNGR1, IFNGR2, FOXP3, RUNX1, TRIM28Regulates cell differentiation, activation, expansion, homeostasis, and survival
STAT3Ovary, uterus placentaCyto 27.3; Syncytio: 35.9; extravillous: 49.3; Endometrium 194.6BMX, EGFR, JK1, MAPK1, PIAS3Controls cell proliferation, migration, apoptosis
NFKB1Ovary, uterus placentaCyto: 15.2; Syncytio:13.5; extravillous: 17.3; Endometrium 60.4NFKB1A, RELA, CHUK, IFBKB, RELBRegulate genes
IRF1Ovary, uterus placentaCyto: 25.0; Syncytio: 9.4; extravillous: 46.9; Endometrium 179.7IRF8, STUB1, STAT1, EP300, KAT2BRegulate innate and adaptive immune responses
TBX21Ovary, uterus placenta-CREBBP, EP300, GATA3, SP1, UBC, TBX21Regulates development of naive T lymphocytes
STAT5BOvary, uterus placentaCyto: 8.0; Syncytio: 13.8; extravillous: 5.9; Endometrium 20.5EGFR, INSR, JAK1, JAK2, JAK3Regulates formation of tissues and organs; maintains immune homeostasis
GATA3Ovary, uterus placentaCyto: 329.4; Syncytio: 1237.7; extravillous: 843.6; Endometrium 0.4HDAC1, HDAC2, HDAC3, LMO1, TAL1Regulates cell maturation with proliferation arrest and cell survival
STAT4Ovary, uterus placentaCyto: 0.4; Syncytio: 0.4; extravillous: 3.4;
Endometrium 0.4		JUN, IL12RB2, PIAS2, STAT1, ZNF467Regulates innate and adaptive immune responses
JUNOvary, uterus placentaCyto: 666.6; Syncytio: 405.9; extravillous: 61.9; Endometrium 2873.0ATF2, FOS, MAPK8, MAPK9, MAPK10Cell proliferation, apoptosis and survival, and tissue morphogenesis
SP1Ovary, uterus placentaCyto: 16.3; Syncytio: 22.4; extravillous: 17.0; Endometrium 22.3EP300, ESR1, HDAC1, HDAC2, TP53Regulates cell cycle, hormonal activation, apoptosis, and angiogenesis
GATA1Ovary, uterus placenta-BRD3, FLJI1, LMO2, TAL1, ZFPM1Regulates development of multipotential progenitors and hematopoietic stem cells
EGR1Ovary, uterus placentaCyto: 154.9; Syncytio: 165.7; extravillous: 106.1; Endometrium 783.3EP300, JUNDB, JUNDD, NAB1, TP53 Regulates attachment and survival of normal cells and induces apoptosis in abnormal cells
ATF3Ovary, uterus placentaCyto: 179.2; Syncytio: 507.9; extravillous: 365.5; Endometrium 321.4DDIT3, JUN, JUNB, MDM2, TP53Regulates metabolism, immunity, inflammation, cell proliferation, and apoptosis
RELAOvary, uterus placentaCyto: 23.0; Syncytio: 47.7; extravillous: 27.7; Endometrium 24.8BRD4, CREBBPEP300, NFKB1, NFKB1ARegulate genes involved in apoptosis, inflammation, the immune response, and proliferation
YY1Ovary, uterus placentaCyto: 121.3; Syncytio: 177.1; extravillous: 126.4; Endometrium 129.9EP300, HDAC2, HDAC3, MBTD1, RUVBL2, Regulates several biological functions—embryogenesis, differentiation, replication, and cellular proliferation
EP300Ovary, uterus placentaCyto: 17.7; Syncytio: 34.4; extravillous: 19.0; Endometrium 49.1CITED2, HIF1A, SMAD3, TCF3, TP53Regulates cell growth and division and prompts cell maturation and cells to take specialized functions
CREB1Ovary, uterus placentaCyto: 30.1; Syncytio: 18.7; extravillous: 25.9; Endometrium 37.8CREBBP, CRTC2, EP300, RPS6KA5, TP53Regulates proliferation, migration, and invasion of cells
NR3C1Ovary, uterus placentaCyto: 48.6; Syncytio: 36.6; extravillous: 44.2; Endometrium 28.5HSP90AA1, NCOA1, NCOa2, NCOR, SMARCA4Regulates hypothalamic–pituitary–adrenal (HPA) axis by modulating availability of cortisol
STAT5AOvary, uterus placentaCyto: 1.2; Syncytio: 1.3; extravillous: 2.9; Endometrium 5.0EGFR, ERBB4, JAK1, JAK2, JAK3Relates IL2 signaling, modulates cytokine and growth factor action, modifies chromatin organization
STAT1Ovary, uterus placentaCyto: 13.7; Syncytio: 7.9; extravillous: 60.8; Endometrium 45.2CREBBP, JAK2, PIAS1, STAT2, STAT3Regulates proinflammation and immune function
Cyto—Cytotrophoblast; Syncytio—syncytiotrophoblast; extravillous—extravillous trophoblast; Endometrium—endometrial stromal cells.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kasimanickam, R.; Kasimanickam, V. MicroRNAs in the Pathogenesis of Preeclampsia—A Case-Control In Silico Analysis. Curr. Issues Mol. Biol. 2024, 46, 3438-3459. https://doi.org/10.3390/cimb46040216

AMA Style

Kasimanickam R, Kasimanickam V. MicroRNAs in the Pathogenesis of Preeclampsia—A Case-Control In Silico Analysis. Current Issues in Molecular Biology. 2024; 46(4):3438-3459. https://doi.org/10.3390/cimb46040216

Chicago/Turabian Style

Kasimanickam, Ramanathan, and Vanmathy Kasimanickam. 2024. "MicroRNAs in the Pathogenesis of Preeclampsia—A Case-Control In Silico Analysis" Current Issues in Molecular Biology 46, no. 4: 3438-3459. https://doi.org/10.3390/cimb46040216

Article Metrics

Back to TopTop