[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Article in Journal
Synthesis and Characterization of Amine and Aldehyde-Containing Copolymers for Enzymatic Crosslinking of Gelatine
Previous Article in Journal
Canine Mammary Tumors: Classification, Biomarkers, Traditional and Personalized Therapies
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 25
Issue 5
10.3390/ijms25052896
ijms-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

Identification of MicroRNA Profiles in Fetal Spina Bifida: The Role in Pathomechanism and Diagnostic Significance

by
Angelika Buczyńska
1,*,
Iwona Sidorkiewicz
2,
Magdalena Niemira
1,
Adam Jacek Krętowski
1,3,
Piotr Węgrzyn
4,
Przemysław Kosiński
4,† and
Monika Zbucka-Krętowska
5,*,†
1
Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276 Bialystok, Poland
2
Clinical Research Support Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276 Bialystok, Poland
3
Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276 Bialystok, Poland
4
Department of Obstetrics, Perinatology and Gynecology, Medical University of Warsaw, 63A Zwirki i Wigury, 02-091 Warsaw, Poland
5
Department of Gynecological Endocrinology and Adolescent Gynecology, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276 Bialystok, Poland
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2024, 25(5), 2896; https://doi.org/10.3390/ijms25052896
Submission received: 19 January 2024 / Revised: 16 February 2024 / Accepted: 27 February 2024 / Published: 1 March 2024
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
Browse Figures
Figure 1
<p>The amniotic fluid assessment in SB pathogenesis identification. (<b>A</b>) DE miRNAs in amniotic fluid with significantly different expressions between SB (n = 6) and control group (n = 20) (|FC| &gt; 1.5; FDR &lt; 0.05); (<b>B</b>) the network of the top 10 hub genes; (<b>C</b>) gene ontology (GO) enrichment analysis. Top 10 significantly enriched GO (−log10 (<span class="html-italic">p</span>-value)) terms of the target genes in the cellular components, molecular function, and biological processes. KEGG, Kyoto Encyclopedia of Genes and Genomes.</p> "> Figure 2
<p>ROC analysis was constructed to evaluate the diagnostic values of the selected miRNAs as predictive biomarkers of SB (FC &gt; 1.5; FDR &lt; 0.05): (<b>A</b>) miR-1253; (<b>B</b>) miR-1290; (<b>C</b>) miR-194-5p; (<b>D</b>) miR-302d-3p; (<b>E</b>) miR-3144-3p; (<b>F</b>) let-7b-5p; (<b>G</b>) miR-122-5p; (<b>H</b>) miR-23a-3p; (<b>I</b>) miR-30d-5p.</p> "> Figure 3
<p>ROC analysis was constructed to evaluate the diagnostic values of the selected miRNAs in amniotic fluid as predictive biomarkers of SB (FC &gt; 1.5; FDR &lt; 0.05): (<b>A</b>) miR-320e; (<b>B</b>) miR-4536-5p; (<b>C</b>) miR-549aa + miR-548t-3p; (<b>D</b>) miR-548ar-5p; (<b>E</b>) miR-548n; (<b>F</b>) let-590-5p; (<b>G</b>) miR-612; (<b>H</b>) miR-627-5p; (<b>I</b>) miR-644a; (<b>J</b>) miR-873-3p.</p> "> Figure 4
<p>The plasma analysis among SB pathogenesis identifications. (<b>A</b>) The DE miRNAs in plasma with significantly different expressions between SB (n = 6) and control group (n = 20) (FC &gt; 1.5; FDR &lt; 0.05); (<b>B</b>) the networks of the top 10 hub genes targeted by miR-320e; (<b>C</b>) gene ontology (GO) enrichment analysis. Top significantly enriched GO (−log10 (<span class="html-italic">p</span>-value)) categories of the target genes in the cellular components, and biological processes.</p> "> Figure 5
<p>ROC analysis was conducted to evaluate the diagnostic value of miR-320e as diagnostic biomarker of SB vs. control.</p> ">
Versions Notes

Abstract

:
Distinct miRNA expression patterns may reflect anomalies related to fetal congenital malformations such as spinal bifida (SB). The aim of this preliminary study was to determine the maternal miRNA expression profile of women carrying fetuses with SB. Therefore, six women carrying fetuses with SB and twenty women with euploid healthy fetuses were enrolled in this study. Using NanoString technology, we evaluated the expression level of 798 miRNAs in both plasma and amniotic fluid samples. A downregulation of miR-1253, miR-1290, miR-194-5p, miR-302d-3p, miR-3144-3p, miR-4536-5p, miR-548aa + miR-548t-3p, miR-548ar-5p, miR-548n, miR-590-5p, miR-612, miR-627-5p, miR-644a, and miR-122-5p, and an upregulation of miR-320e, let-7b-5p, miR-23a-3p, miR-873-3p, and miR-30d-5p were identified in maternal amniotic fluid samples in SB when compared to the control group. The target genes of these miRNAs play a predominant role in regulating the synthesis of several biological compounds related to signaling pathways such as those regulating the pluripotency of stem cells. Moreover, the maternal plasma expression of miR-320e was increased in pregnancies with SB, and this marker could serve as a valuable non-invasive screening tool. Our results highlight the SB-specific miRNA signature and the differentially expressed miRNAs that may be involved in SB pathogenesis. Our findings emphasize the role of miRNA as a predictive factor that could potentially be useful in prenatal genetic screening for SB.

1. Introduction

Myelomeningocele, also known as spina bifida (SB), is a non-lethal congenital birth defect of the central nervous system that occurs in 1–10 per 1000 live births worldwide [1]. SB is caused by the incorrect development of the spinal cord due to incomplete closure of the neural tube at 28 days of gestation, resulting in local nerve damage and the sequelae of non-communicating hydrocephalus [2,3]. The pathogenesis of SB is still unknown [4]. However, it has been proven that folic acid deficiency increases the risk of neural tube defects [5].
Most cases of SB are detected prenatally during the second-trimester scan performed between 18 and 22 weeks of gestation. In some selected cases, SB detection is possible even earlier, during the routine 11–13-week ultrasound scan (US) performed by an experienced sonographer [6]. In some countries, biochemical screening for SB based on maternal serum alpha-fetoprotein measurement is available. However, the detection rate is lower compared to ultrasound markers. Women diagnosed with congenital birth defects, including SB, are encouraged to undergo amniocentesis after the 15th week of gestation to exclude the most common chromosomal aberrations [7]. SB screening in most countries is currently based solely on US evaluation. Introducing a novel, non-invasive screening tool with high sensitivity and specificity could potentially identify a high-risk group and improve the accuracy of US [8]. miRNAs play critical regulatory roles in various physiological and pathological processes, including differentiation, proliferation, development, apoptosis, and oncogenesis. Therefore, they may serve as potential diagnostic biomarkers [9].
SB develops in the early stages of embryonic development, typically within the first weeks of pregnancy. However, before anatomical defects become visible through imaging methods such as ultrasound, molecular changes, such as alterations in miRNA expression, may occur at a stage that is not easily detectable with conventional diagnostic tools. In this study, we assessed miRNA in samples collected during a specific gestational period, considering that significant molecular events related to SB may take place earlier. Our goal was to understand whether miRNA profiles during this period could provide information about the risk of SB and whether they could serve as potential biomarkers for this condition [7,8,9]. Moreover, introducing miRNA expression patterns to the diagnostic protocol may improve SB detection and also aid personalized clinical management [10]. Since the pathogenesis of SB is still unknown, the comprehensive knowledge of SB development may introduce new treatment targets, improve surgical techniques, and enhance the prognosis for neonates [11,12,13]. Thus, the main objective of the current study was to evaluate the significance of miRNAs in the pathogenesis of SB. Amniotic fluid miRNA profiling was performed to uncover new molecular features of SB. Additionally, maternal circulating miRNAs were identified as novel non-invasive screening biomarkers for SB.

2. Results

2.1. Amniotic Fluid

The amniotic fluid profiling of 798 miRNAs was performed using the NanoString Technology platform (Seattle, WA, USA), and 19 DE miRNAs were identified between the studied groups based on a threshold of an FDR < 0.05 and an |FC| ≥ 1.5 (Figure 1A).
We utilized the Cytoscape software version 3.8.2 to visualize the construction of the protein–protein interaction (PPI) network of target genes for the DE miRNAs obtained using MIENTURNET. To identify the most important genes in the PPI network, we applied the Maximal Clique Centrality (MCC) algorithm within the CytoHubba plugin. Consequently, we identified the top 10 hub genes, which included the TNRC gene family (trinucleotide-repeat-containing adaptor 6A (TNRC6A), trinucleotide-repeat-containing adaptor 6B (TNRC6B), trinucleotide-repeat-containing adaptor 6C (TNRC6C)), cyclin-dependent kinase 6 (CDK6), argonaute 3 (AGO3), argonaute 4 (AGO4), cyclin D2 (CCND2), argonaute 1 (AGO1), tumor protein 53 (PT53), and cyclin D1 (CCND1) (Figure 1B).
Gene ontology was used for identifying and visualizing the appropriate biological pathways and processes associated with the target genes (Figure 1C).
The analysis of the SB amniotic fluid network revealed a predominant role of the regulation of several biological compounds related to metabolic and synthesis processes, as well as incorrect functions of cellular components such as the neoplasm, chromatin, chromosome, cytosol, nuclear lumen, intracellular organelle lumen, and transcription regulator complex. With respect to molecular function terms, these genes were mainly enriched in transcriptional activator activity (RNA polymerase II core promoter proximal region sequence-specific binding), actin binding, sequence-specific DNA binding, and double-stranded DNA binding. The analysis of cellular components primarily identified those involved in the signaling pathways regulating the pluripotency of stem cells, mitogen-activated protein kinases (MAPK), transforming growth factor β (TGF-beta), prolactin, neurotrophin, and mammalian target of rapamycin (mTOR) signaling pathways. Additionally, incorrect neuronal development was indicated by axon-guidance-related processes.
Accordingly, the diagnostic value of the DE miRNAs as candidate SB biomarkers using the AUC was evaluated. All the identified DE miRNAs showed significantly higher AUCs compared with AUC = 0.50 (borderline of the diagnostic usefulness of the parameter). The highest AUCs, indicating possible clinical usefulness in SB diagnosis, were observed for let-7b-5p (AUC = 1.00), miR-30d-5p (AUC = 1.00), miR-320e (AUC = 1.00), miR-612 (AUC = 1.00), miR-23a-3p (AUC = 0.97), miR-122-5p (AUC = 0.95) and miR-590-5p (AUC = 0.94) (all, p < 0.05) (Figure 2 and Figure 3).

2.2. Maternal Plasma miRNA Profiling

To determine the SB-specific maternal miRNA expression pattern and to assess the potential utility of SB-specific miRNA as a screening tool, we profiled the plasma expression of 798 miRNAs using the NanoString Technology platform. The DE miRNA-320e was detected between the studied groups (p < 0.001) based on a threshold of an FDR < 0.05 and an |FC| ≥ 1.5 (Figure 4A).
We also predicted the DE miRNA-320e target genes using MIENTURNET. The results indicated 62 putative target genes for miRNA-320e, then the CytoHubba algorithm selected 10 hub genes. The STRING database was used to visualize the protein relationships (Figure 4B).
The UBX domain protein 7 (UBXN7) gene family linked to cyclin T1 (CCNT1), mediator complex subunit 7 (MED7), Snf2 related CREBBP activator protein (SRCAP), N-acetyltransferase 10 (NAT10), and heterogeneous nuclear ribonucleoprotein U (HNRNPU) with retinoid X receptor beta (RXRB), nuclear receptor subfamily 1 group H member 2 (NR1H2), SRY-box transcription factor 11 (SOX11), and quiescin sulfhydryl oxidase 1 (QSOX1) were the top 10 hub genes in the PPI network. The GO analysis of the miRNA network related to SB highlighted the predominant involvement of the negative regulation of cellular component organization and the cellular biosynthesis process among the most significantly associated biological processes. Additionally, the genes within the GO cellular component category were found to be connected to the centromeric region (Figure 4C).
Likewise, we assessed the diagnostic value of miR-320e as a potential biomarker for SB by evaluating the AUC. The analysis demonstrated that miR-320e exhibited the AUC value of 0.90, significantly increased when compared with AUC = 0.50, suggesting its potential clinical utility in SB screening (Figure 5).

2.3. Logistic Regression Model

A logistic regression model, a widely used supervised learning algorithm for binary classification tasks, was employed to examine the DE miRNAs and develop a screening signature for SB. Through the feature selection process, we identified two miRNAs that exhibited the most significant diagnostic values. Subsequently, a logistic regression model was constructed using these miRNAs. Notably, the combined use of miR-205-5p and miR-362-5p yielded a higher diagnostic value for SB (AUC = 0.94) compared to miR-320e (Table 1).

3. Discussion

The release of tissue-specific miRNAs in response to stress signals and their role as signaling molecules in health and disease highlights their potential as important tools for investigating deregulated pathways. Furthermore, miRNAs are involved in cellular communication and are likely to play important regulatory roles in maternal–fetal crosstalk [3,4,5,6]. Although the sample size in our study was limited and the low prevalence of SB among the general population should be considered, our findings provide valuable new insights into SB pathogenesis and the development of non-invasive biomarkers for prenatal screening. Therefore, identifying miRNAs associated with fetal SB provides significant contributions to understanding its pathogenesis.
Our study revealed the dysregulation of several miRNAs in amniotic fluid from fetuses with SB. Specifically, miR-1253, miR-1290, miR-194-5p, miR-302d-3p, miR-3144-3p, miR-4536-5p, miR-548aa + miR-548t-3p, miR-548ar-5p, miR-548n, miR-590-5p, miR-612, miR-627-5p, miR-644a, and miR-122-5p expression was downregulated and miR-320e, let-7b-5p, miR-23a-3p, miR-873-3p, miR-30d-5p expression was upregulated in SB when compared to the control group. There is little literature evidence to link the studied miRNAs to SB. Some of them have been connected to neoplastic disease development (miR-1253, miR-1290, miR-194-5p, miR-4536-5p, miR-548ar-5p, miR-590-5p, miR-627-5p, miR-320e, miR-23a-3p, miR-612, miR-23a-3p, miR-30d-5p, and miR-122-5p) [14,15,16,17,18,19,20,21,22,23,24]. The role of let-7b-5p in neuronal development and neuroinflammation has been demonstrated [25]. Li et al. showed a protective role of miR-23a-3p against neuronal injury by inhibiting neuron apoptosis and inflammatory response via reactivating the PTEN/AKT/mTOR signaling pathway [26]. It has been demonstrated that the upregulation of miR-23a-3p suppressed oxidative stress in ischemic brain injury using a mouse model [27]. Moreover, it has been shown that folic acid deficiency might upregulate miR-30d-5p to inhibit the expression of key genes in the endoplasmic reticulum stress pathway in colorectal cancer cells [28]. This suggests the possible involvement of miR-30d-5p in the cellular response to folic acid deficiency in SB pathogenesis.
Based on the role of miRNAs, it can be concluded that miRNA dysregulation results from the SB pathomechanism, rather than the dysregulated miRNAs actively participating in SB development. However, a better understanding of the mechanisms by which miRNAs are involved in SB may provide novel targets for innovative therapeutic strategies. To identify key DE miRNA target genes, a PPI network was constructed, revealing the TNRC gene family (TNRC6A, TNRC6B, and TNRC6C), CDK6, AGO3, AGO4, CCND2, AGO1, PT53, and CCND1 as hub genes. Interestingly, AGO and the TNRC6 family proteins have been shown to be the key players in the miRNA-induced silencing complex (miRISC), the machinery that mediates microRNA function in the cytoplasm [29]. One study established the importance of transcriptional regulation in neuronal differentiation with the central role of miRISC [30]. What should be noted is that the heterogeneity of the miRISC complex composition determines its dynamic function in regulating gene expression. Our results suggest the involvement of miRISC in modulating repressive activity in SB; however, the detailed impact of miRISC in SB pathogenesis is yet to be determined. Furthermore, CCND1 and CCND2 have been implicated in neurogenesis [31,32]. D-type cyclins (CCND1 and CCND2) are known to bind to CDK6 in response to mitogen exposure and activate the signal transduction cascade, resulting in cell division. The synchronized cell cycle progression allows for proper neural tube development [33]. The genetic background of SB is complex; however, our results suggest the involvement of both miRISC and cyclins. There is clearly a need for a detailed molecular study to understand their role in the SB pathogenesis.
Additionally, the GO analysis suggests that the signaling pathways regulating the pluripotency of stem cells, the MAPK signaling pathway, and axon guidance play important roles in the mechanism of T2DM development, which is consistent with the hub gene analysis. The study by Qin et al. demonstrated specific miRNA dysregulation (downregulation of miRNA-9 and miRNA-124a, and upregulation of miRNA-138 and miRNA-134) in amniotic fluid samples obtained from six pregnant rats with fetal SB compared to six control rats with healthy fetuses. The GO analysis revealed the involvement of these miRNAs in the MAPK signaling pathway, neurotrophin pathway, and mTOR signaling [34]. Li et al. demonstrated that neuroactive ligand–receptor interactions, the NF-kappa B signaling pathway, osteoclast differentiation, cytokine–cytokine receptor interactions, and cell adhesion molecules play crucial roles in the pathomechanism of SB [35].
Moreover, the miRNA profile in amniotic fluid may also represent an attractive diagnostic/prognostic biomarker. Let-7b-5p (AUC = 1.00), miR-30d-5p (AUC = 1.00), miR-320e (AUC = 1.00), miR-612 (AUC = 1.00), miR-23a-3p (AUC = 0.97), miR-122-5p (AUC = 0.95), and miR-590-5p (AUC = 0.94) (all, p < 0.05) have demonstrated high diagnostic value as SB biomarkers. Moreover, miR-320e has been shown to be significantly upregulated in the maternal plasma of patients with a fetus with SB compared to control maternal plasma. It can be hypothesized that the increased miR-320e expression in maternal plasma results from upregulation in the fetal compartment, as demonstrated in our study. The PPI analysis showed that UBXN7, CCNT1, MED7, SRCAP, NAT10, HNRNPU, RXRB, NR1H2, SOX11, and QSOX1 were the top 10 genes regulated by the DE miRNA. UBXN7 is a cofactor of ubiquitin ligase complexes and mediates the reciprocal regulation of nuclear factor erythroid 2-related factor 2 (NRF2) and hypoxia-inducible factor (HIF) 1α proteins, directly involved in neurogenesis process, especially in the adaptation of neurons and glia cells to hypoxia and during neuronal differentiation [36,37,38]. The CCNT1 gene has also been linked to the processes of neuron development by nucleic acid binding activity modulation [39]. MED7 is a coactivator involved in the regulation of the transcription of nearly all RNA-polymerase-II-dependent genes, which was connected to folate-dependent cognitive impairment [40]. The SRCAP gene has been implicated in maintaining the self-renewal of embryonic stem cells, indicating its significant contribution to development. Moreover, SRCAP, a chromatin remodeler, promotes self-renewal in intestinal stem cells. On the other hand, NAT10 has been shown to participate in various epigenetic processes such as histone acetylation and mRNA modifications [41]. The HNRNPU gene is implicated in encoding of the heterogeneous nuclear ribonucleoprotein U, a protein that plays essential roles in RNA splicing and chromatin organization. When its function is impaired, it results in the rapid cell death of both post-mitotic neurons and neural progenitors [42]. The RXRB gene encodes a protein that belongs to the retinoid X receptor (RXR) family of nuclear receptors. These receptors play crucial roles in mediating the effects of retinoic acid, especially in the induction of neurogenesis by activating both retinoic acid receptors (RARs) and the peroxisome proliferator-activated receptor β/δ (PPARβ/δ) [43,44]. NR1H2, a nuclear receptor, plays a key role in the regulation of cholesterol uptake through myosin regulatory light chain interacting protein (MYLIP)-dependent ubiquitination, and the regulation of the lipid-induced stress response and inflammation, particularly involved in disturbed neurogenesis and spinal cord injury [45]. The study performed by Wang et al. showed that SOX11 is essential for both embryonic and adult neurogenesis, by transient proliferation deficits in neural progenitor cell proliferation [46,47]. Moreover, QSOX1 has been associated with neural tube defect development via its interaction with valproic acid and folic acid [12]. The potentially disturbed function of genes indicated by DE miRNAs suggests involvement in the processes of neurogenesis, responses to oxidative stress, lipid metabolism, and the transport and distribution of folic acid, which confirms the complex pathogenesis of developmental defects in SB. Thus, more extensive research in this field is still needed.
Furthermore, the GO analysis demonstrated a predominant role of target genes in the negative regulation of cellular component organization and biosynthetic processes, indicating their role in cellular response and metabolism in SB.
Plasma miRNA profiling may also be a useful tool for the non-invasive prenatal screening of SB. Thus, we evaluated the diagnostic value of miR-320e. Since only one miRNA was differentially expressed in our study group when assessed in maternal plasma, all miRNAs in the NanoString panel were screened for their significance when combined into a diagnostic panel. The feature selection chose two miRNAs that did not show differential expression—miR-205-5p + miR-362-5p—and the logistic regression model for the comparison of SB vs. control showed their higher diagnostic value (AUC = 0.94) compared to the AUC found for miR-320e (AUC = 0.90) when assessed in maternal plasma.
miR-205 has garnered significant attention due to its involvement in both normal developmental processes and cancer. During early embryogenesis, miR-205 is expressed in trophoblasts and contributes to placental development by inhibiting the expression of mediator of RNA polymerase II transcription subunit 1 (MED1) [48]. As embryonic development progresses, miR-205 also plays a role in regulating extraembryonic endoderm differentiation and spermatogenesis by targeting genes associated with cell migration and adhesion [49]. Notably, miR-362 is associated with specific medical conditions such as polymyositis and dermatomyositis. Functional studies have further revealed that miR-362 exerts inhibitory effects on the proliferation and migration of vascular smooth muscle cells [50]. These findings raise the possibility of miR-362’s involvement in SB development.
In the context of embryonic development, both miR-205 and miR-362 present intriguing roles that warrant further investigation, particularly regarding their potential participation in processes such as neural tube formation. Such processes are of particular significance in the context of SB development. However, it is crucial to emphasize that our analyses should be considered preliminary, and their validation through larger-scale studies is imperative to confirm their validity [51]. The results of our study are in line with the current knowledge about SB pathogenesis. The expression pattern of miRNAs in SB pregnancies may impact further improvements in prenatal screening by uncovering disturbed molecular pathways.

Study Limitations

While our study demonstrates promising findings regarding the potential of miRNA markers as non-invasive screening tools for SB, it is essential to acknowledge that screening strategies may not directly translate into preventive measures. Despite the high screening accuracy observed for miR-320e in maternal plasma, it is imperative to recognize the limitations and complexities associated with implementing screening programs in clinical practice. Firstly, the transition from screening to prevention requires a comprehensive understanding of the underlying biological mechanisms and modifiable risk factors associated with SB. While our study contributes to elucidating the SB-specific miRNA signature, further research is warranted to investigate the causal relationships between miRNA dysregulation and SB pathogenesis. Additionally, identifying environmental and genetic factors that interact with miRNA expression could enhance risk stratification and inform targeted preventive interventions. Moreover, the effectiveness of screening programs depends not only on diagnostic accuracy but also on factors such as accessibility, acceptability, and cost-effectiveness. Implementing widespread screening for SB based on miRNA markers would necessitate robust evidence supporting their clinical utility, as well as considerations of resource allocation and ethical implications.
Our study was also not without limitations, the main among them being a small sample size, which resulted from the low incidence of SB in the general population. However, our sample size does not differ significantly from that of other similar studies [51,52]. Therefore, the results of our study should be considered preliminary, and they require blind validation in a large patient cohort to confirm the significance of differential miRNA expression in SB screening. While we have collected and analyzed data from samples obtained during amniocentesis, it is crucial to recognize that this procedure is typically performed during the second trimester of pregnancy, typically between the 15th and 18th weeks of gestation. This timing places the collection of amniotic fluid samples at a stage of pregnancy significantly later than when the primary events leading to SB take place. The primary events, including neural tube closure, occur during the initial stages of pregnancy, often before many women are even aware of their pregnancy. Therefore, the miRNA profiles or other biomarkers that we are assessing in the amniotic fluid represent a later developmental stage compared to the critical period when SB initiation and progression primarily occur. Consequently, when interpreting our results, it is imperative to consider this temporal gap between the events of interest and the timing of sample collection. The miRNA profiles observed in the amniotic fluid may reflect a downstream consequence of earlier developmental processes and may not fully capture the initial molecular events associated with SB etiology.

4. Materials and Methods

4.1. Material Collection

An adequate sample size to detect a difference was demonstrated using power analysis. Considering a 95% confidence level, 5% margin of error, and SB incidence, a minimum of 6 samples were required for this study. Therefore, the study group consisted of 6 pregnant women at the Department of Obstetrics, Perinatology and Gynecology, Medical University of Warsaw, Poland, in whom SB was detected prenatally. All cases of lesions were of the open spina bifida type at levels between L1 and S1. The control group comprised 20 women who underwent routine amniocentesis at the Department of Reproduction and Gynecological Endocrinology of the Medical University of Bialystok, Poland. All 26 patients underwent amniocentesis during the gestational period between the 15th and 18th weeks, and all subsequent samples, consisting of 26 plasma and 26 amniotic fluid samples, were included in the analysis.
The exclusion criteria were chronic or acute diseases, hormonal or/and anti-inflammatory treatment. All participants were informed about potential risks prior to the procedure and received relevant information regarding the study. The study participants were matched according to age, ethnicity, socioeconomic status, the course of pregnancy, body mass index (BMI), and the number of pregnancies with marked episodes of pregnancy pathology. All participants in the study were meticulously selected according to specific criteria, including an age range of 20 to 40 years, self-identification as belonging to the Caucasian ethnic group with Eastern European heritage, absence of comorbidities, and no history of chronic medication usage. Statistical analysis demonstrated no significant differences between the studied groups across all criteria (p > 0.05). Both amniotic fluid (5.0 mL) and venous blood (5.5 mL) samples were obtained from participants on the same day. Venous blood was centrifuged, and, subsequently, both plasma and amniotic fluid samples were transferred into DNase- and RNase-free tubes (Eppendorf, Hamburg, Germany), and stored at −80 °C until assayed. The methods were carried out in accordance with approved guidelines and conducted in accordance with the ethical standards of the institutional research committee, with the Helsinki Declaration, and were approved by the local ethics committee of the Medical University of Bialystok, Poland (approval number: APK.002.76.2021).

4.2. nCounter miRNA Expression Assay

The nCounter miRNA Expression pre-designed panel simultaneously detects 798 human miRNAs, including five mRNA reference controls (B2M, GAPDH, RPL19, ACTB, RPLP0). Positive and negative proprietary spike-in controls (probes that recognize exogenous miRNA targets to monitor upstream RNA isolation/purification; osa-miR442, osa-miR414, cel-miR-254, and cel-miR-248), hybridization controls and ligation-specific controls were also included to determine sample integrity, quality, and background. The NanoString platform provided several key advantages, including sensitivity and robustness for analyses of plasma samples. This assay involves sample processing where unique oligonucleotide tags are annealed and subsequently ligated with miRNAs of interest via a target specific bridge oligo. Sequence specificity between each miRNA and its appropriate tag is ensured by careful, stepwise control of annealing and ligation temperatures. NanoString counts single molecules with no amplification step. The nCounter miRNA Expression Assay requires purified total RNA as input material.
miRNA was isolated from 200 µL of plasma and amniotic fluid samples using the miRNeasy Serum/Plasma Advanced Kit (Qiagen, Hilden, Germany), following the manufacturer’s instructions. miRNA was eluted in a final volume of 15 μL.
The RNA concentration was measured using Qubit (Invitrogen, Carlsbad, CA, USA) and 100 ng total RNA was used as input material. Next, the samples were prepared for nCounter miRNA expression profiling as per the manufacturer’s recommendations (NanoString Technologies, Seattle, WA, USA). Briefly, sample preparation involved a multiplexed annealing of the specific tags to their target miRNA, a ligation reaction, an enzymatic purification to remove unligated tags, followed by an overnight hybridization (65 °C) with nCounter Reporter and Capture probes. Subsequently, the samples were placed into the nCounter Prep Station for automated sample purification and reporter capture. Each sample was scanned (550 fields of view) on the nCounter Digital Analyzer for data collection. The NanoString data were deposited in the Gene Expression Omnibus (GEO) database (Accession Number: GSE242364).

4.3. Data Analysis

The nSolver 4.0 Analysis software (NanoString) was utilized for data analysis, which included a normalization step using the ligation method. The p-values were adjusted using the false discovery rate (FDR) correction for multiple comparisons, which was limited to 0.05 for both plasma and amniotic fluid samples. A threshold value for significance of fold change |FC| of ≥1.5 was applied to define differentially expressed (DE) miRNAs. Statistical analyses were conducted using GraphPad PRISM (v.9.1.1; GraphPad Software, San Diego, CA, USA). To assess the screening utility, the receiver operating characteristic (ROC) curves were analyzed and the area under the ROC curves (AUC) was calculated.

4.4. miRNA Target Prediction and Functional Annotation

To examine the functions of the identified miRNAs, miRNA target prediction was performed using MicroRNA Enrichment Turned Network (MIENTURNET, Rome, Italy; http://userver.bio.uniroma1.it/apps/mienturnet/, accessed on 15 February 2023), which utilizes TargetScan and miRTarBase databases. To identify highly connected hub genes in the protein–protein interaction (PPI), the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) was used (accessed on 15 February 2023), with the interaction score > 0.4. The CytoHubba plugin based on Cytoscape version 3.8.2 (http://cytoscape.org, accessed on 16 February 2023) was applied. To examine the functions of the identified target genes, gene ontology (GO) analysis and functional annotation clustering were carried out using the following online databases: Gene Ontology enrichment analysis and visualization tool (GOrilla; http://cbl-gorilla.cs.technion.ac.il/, accessed on 16 February 2023), DAVID (Gene Ontology and KEGG Enrichment Analysis; https://david.ncifcrf.gov/, accessed on 16 February 2023), g: Profiler (https://biit.cs.ut.ee/gprofiler/gos accessed on 16 February 2023), and Metascape (https://metascape.org, accessed on 16 February 2023). GO analysis allows for associating a given gene list with specific functional annotations, which are further divided into functional clusters listed according to their enrichment p-value.

4.5. Modeling

To construct a model that combines miRNAs for the diagnosis of fetal SB, we used Weka software (v.3.8.6; Hamilton, New Zealand). Attribute selection was performed using the Info Gain evaluator and Ranker search to select miRNAs with the highest ranks for further modeling. Logistic regression, naive Bayes, and J48 tree-based algorithms were calculated for each combination, and confusion matrices were prepared for each model to evaluate its performance. To control for errors, we used the “Leave One Out Cross-Validation” method, as the sample size was limited. The performance of the multi-miRNA classifiers was evaluated using the classification precision and the AUC.

5. Conclusions

Our analysis demonstrates the usefulness of evaluating miRNA expression patterns in the prenatal diagnosis of spinal bifida (SB). Additionally, a screening panel comprising miR-205-5p + miR-362-5p and miR-320e may be introduced as a potential tool for the maternal serum prenatal screening of SB. Furthermore, characterizing the regulatory network of DE miRNAs and target genes in amniotic fluid is important for investigating the pathways that contribute to a better understanding of SB pathogenesis. Nonetheless, further studies are required to understand the role of dysregulated miRNAs in the molecular mechanisms underlying the occurrence of SB-related comorbidities.

Author Contributions

Conceptualization, A.B. and I.S.; methodology, A.B. and A.J.K.; software, M.N. and P.W.; validation, A.B., I.S. and M.Z.-K.; formal analysis, I.S.; investigation, M.Z.-K.; resources, M.Z.-K.; data curation, A.B.; writing—original draft preparation, A.B.; writing—review and editing, I.S. and M.Z.-K.; visualization, A.B.; supervision, M.Z.-K. and P.K.; project administration, M.Z.-K.; funding acquisition, M.Z.-K. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by the funds of the Ministry of Education and Science within the project “Excellence Initiative—Research University”.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Medical University of Bialystok, Poland (APK.002.76.2021). Informed consent was obtained from all the subjects who were involved in this study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Phillips, L.A.; Burton, J.M.; Evans, S.H. Spina Bifida Management. Curr. Probl. Pediatr. Adolesc. Health Care 2017, 47, 173–177. [Google Scholar] [CrossRef]
  2. Adzick, N.S. Prenatal diagnosis and treatment of spina bifida. Preface. Fetal Diagn. Ther. 2015, 37, 165. [Google Scholar] [CrossRef]
  3. Rocque, B.G.; Hopson, B.D.; Blount, J.P. Caring for the Child with Spina Bifida. Pediatr. Clin. N. Am. 2021, 68, 915–927. [Google Scholar] [CrossRef] [PubMed]
  4. Guilbaud, L.; Zerah, M.; Jouannic, J.M.; Quarello, E. [The paradoxes of spina bifida in the prenatal period]. Gynecol. Obstet. Fertil. Senol. 2021, 49, 569–572. [Google Scholar] [CrossRef] [PubMed]
  5. Oakley, G.P.; Erickson, J.D.; James, L.M.; Mulinare, J.; Cordero, J.F. Prevention of folic acid-preventable spina bifida and anencephaly. Ciba Found. Symp. 1994, 181, 212–231. [Google Scholar] [CrossRef] [PubMed]
  6. Syngelaki, A.; Hammami, A.; Bower, S.; Zidere, V.; Akolekar, R.; Nicolaides, K.H. Diagnosis of fetal non-chromosomal abnormalities on routine ultrasound examination at 11–13 weeks’ gestation. Ultrasound Obstet. Gynecol. 2019, 54, 468–476. [Google Scholar] [CrossRef]
  7. Wilson, R.D. Anomalies fœtales affectant le tube neural : Dépistage / diagnostic prénatal et prise en charge de la grossesse. J. Obstet. Gynaecol. Can. 2016, 38, S496–S511. [Google Scholar] [CrossRef] [PubMed]
  8. Shookhoff, J.M.; Gallicano, G.I. A new perspective on neural tube defects: Folic acid and microRNA misexpression. Genesis 2010, 48, 282–294. [Google Scholar] [CrossRef] [PubMed]
  9. Gu, H.; Li, H.; Zhang, L.; Luan, H.; Huang, T.; Wang, L.; Fan, Y.; Zhang, Y.; Liu, X.; Wang, W.; et al. Diagnostic role of microRNA expression profile in the serum of pregnant women with fetuses with neural tube defects. J. Neurochem. 2012, 122, 641–649. [Google Scholar] [CrossRef]
  10. Greene, N.D.E.; Copp, A.J. Could microRNAs be biomarkers for neural tubedefects? J. Neurochem. 2012, 122, 485. [Google Scholar] [CrossRef]
  11. Scott Adzick, N. Fetal surgery for spina bifida: Past, present, future. Semin. Pediatr. Surg. 2013, 22, 10–17. [Google Scholar] [CrossRef]
  12. White, M.; Arif-Pardy, J.; Connor, K.L. Identification of novel nutrient-sensitive gene regulatory networks in amniotic fluid from fetuses with spina bifida using miRNA and transcription factor network analysis. medRxiv 2022, 2022. [Google Scholar] [CrossRef]
  13. Detrait, E.R.; George, T.M.; Etchevers, H.C.; Gilbert, J.R.; Vekemans, M.; Speer, M.C. Human neural tube defects: Developmental biology, epidemiology, and genetics. Neurotoxicol. Teratol. 2005, 27, 515–524. [Google Scholar] [CrossRef] [PubMed]
  14. Kanchan, R.K.; Perumal, N.; Atri, P.; Chirravuri Venkata, R.; Thapa, I.; Klinkebiel, D.L.; Donson, A.M.; Perry, D.; Punsoni, M.; Talmon, G.A.; et al. MiR-1253 exerts tumor-suppressive effects in medulloblastoma via inhibition of CDK6 and CD276 (B7-H3). Brain Pathol. 2020, 30, 732–745. [Google Scholar] [CrossRef] [PubMed]
  15. Ghafouri-Fard, S.; Khoshbakht, T.; Hussen, B.M.; Taheri, M.; Samadian, M. A Review on the Role of miR-1290 in Cell Proliferation, Apoptosis and Invasion. Front. Mol. Biosci. 2021, 8, 1250. [Google Scholar] [CrossRef] [PubMed]
  16. Yen, Y.T.; Yang, J.C.; Chang, J.B.; Tsai, S.C. Down-Regulation of miR-194-5p for Predicting Metastasis in Breast Cancer Cells. Int. J. Mol. Sci. 2021, 23, 325. [Google Scholar] [CrossRef]
  17. Macerola, E.; Poma, A.M.; Proietti, A.; Rago, T.; Romani, R.; Vignali, P.; Ugolini, C.; Torregrossa, L.; Basolo, A.; Santini, F.; et al. Down-regulation of miR-7-5p and miR-548ar-5p predicts malignancy in indeterminate thyroid nodules negative for BRAF and RAS mutations. Endocrine 2022, 76, 677–686. [Google Scholar] [CrossRef]
  18. Zheng, G.D.; Xu, Z.Y.; Hu, C.; Lv, H.; Xie, H.X.; Huang, T.; Zhang, Y.Q.; Chen, G.P.; Fu, Y.F.; Cheng, X.D. Exosomal miR-590-5p in Serum as a Biomarker for the Diagnosis and Prognosis of Gastric Cancer. Front. Mol. Biosci. 2021, 8, 8. [Google Scholar] [CrossRef]
  19. Zhao, D.Y.; Zhou, L.; Yin, T.F.; Zhou, Y.C.; Zhou, G.Y.J.; Wang, Q.Q.; Yao, S.K. Circulating miR-627-5p and miR-199a-5p are promising diagnostic biomarkers of colorectal neoplasia. World J. Clin. Cases 2022, 10, 5165. [Google Scholar] [CrossRef]
  20. Perez-Carbonell, L.; Sinicrope, F.A.; Alberts, S.R.; Oberg, A.L.; Balaguer, F.; Castells, A.; Boland, C.R.; Goel, A. MiR-320e is a novel prognostic biomarker in colorectal cancer. Br. J. Cancer 2015, 113, 83. [Google Scholar] [CrossRef]
  21. Lee, Y.; Kim, S.J.; Choo, J.; Heo, G.; Yoo, J.W.; Jung, Y.; Rhee, S.H.; Im, E. miR-23a-3p is a Key Regulator of IL-17C-Induced Tumor Angiogenesis in Colorectal Cancer. Cells 2020, 9, 1363. [Google Scholar] [CrossRef] [PubMed]
  22. Zhao, Q.; Yuan, X.; Zheng, L.; Xue, M. miR-30d-5p: A Non-Coding RNA With Potential Diagnostic, Prognostic and Therapeutic Applications. Front. Cell Dev. Biol. 2022, 10, 829435. [Google Scholar] [CrossRef] [PubMed]
  23. Tao, Z.H.; Wan, J.L.; Zeng, L.Y.; Xie, L.; Sun, H.C.; Qin, L.X.; Wang, L.; Zhou, J.; Ren, Z.G.; Li, Y.X.; et al. miR-612 suppresses the invasive-metastatic cascade in hepatocellular carcinoma. J. Exp. Med. 2013, 210, 789. [Google Scholar] [CrossRef] [PubMed]
  24. Dai, C.; Zhang, Y.; Xu, Z.; Jin, M. MicroRNA-122-5p inhibits cell proliferation, migration and invasion by targeting CCNG1 in pancreatic ductal adenocarcinoma. Cancer Cell Int. 2020, 20, 98. [Google Scholar] [CrossRef] [PubMed]
  25. Ponnusamy, V.; Ip, R.T.H.; Mohamed, M.A.E.K.; Clarke, P.; Wozniak, E.; Mein, C.; Schwendimann, L.; Barlas, A.; Chisholm, P.; Chakkarapani, E.; et al. Neuronal let-7b-5p acts through the Hippo-YAP pathway in neonatal encephalopathy. Commun. Biol. 2021, 4, 1143. [Google Scholar] [CrossRef] [PubMed]
  26. Li, Z.; Xu, R.; Zhu, X.; Li, Y.; Wang, Y.; Xu, W. MicroRNA-23a-3p improves traumatic brain injury through modulating the neurological apoptosis and inflammation response in mice. Cell Cycle 2020, 19, 24. [Google Scholar] [CrossRef] [PubMed]
  27. Zhao, H.; Tao, Z.; Wang, R.; Liu, P.; Yan, F.; Li, J.; Zhang, C.; Ji, X.; Luo, Y. MicroRNA-23a-3p attenuates oxidative stress injury in a mouse model of focal cerebral ischemia-reperfusion. Brain Res. 2014, 1592, 65–72. [Google Scholar] [CrossRef] [PubMed]
  28. Lu, L.; Zhao, H.; Liu, J.; Zhang, Y.; Wang, X. miRNA-mRNA Regulatory Network Reveals miRNAs in HCT116 in Response to Folic Acid Deficiency via Regulating Vital Genes of Endoplasmic Reticulum Stress Pathway. BioMed Res. Int. 2021, 2021, 6650181. [Google Scholar] [CrossRef]
  29. La Rocca, G.; Cavalieri, V. Roles of the Core Components of the Mammalian miRISC in Chromatin Biology. Genes 2022, 13, 414. [Google Scholar] [CrossRef]
  30. Nawalpuri, B.; Ravindran, S.; Muddashetty, R.S. The Role of Dynamic miRISC During Neuronal Development. Front. Mol. Biosci. 2020, 7, 8. [Google Scholar] [CrossRef]
  31. Lukaszewicza, A.I.; Anderson, D.J. Cyclin D1 promotes neurogenesis in the developing spinal cord in a cell cycle-independent manner. Proc. Natl. Acad. Sci. USA 2011, 108, 11632–11637. [Google Scholar] [CrossRef] [PubMed]
  32. Krupp, D.R.; Xu, P.T.; Thomas, S.; Dellinger, A.; Etchevers, H.C.; Vekemans, M.; Gilbert, J.R.; Speer, M.C.; Ashley-Koch, A.E.; Gregory, S.G. Transcriptome profiling of genes involved in neural tube closure during human embryonic development using long serial analysis of gene expression (long-SAGE). Birth Defects Res. Part A Clin. Mol. Teratol. 2012, 94, 683–692. [Google Scholar] [CrossRef] [PubMed]
  33. Molina, A.; Pituello, F. Playing with the cell cycle to build the spinal cord. Dev. Biol. 2017, 432, 14–23. [Google Scholar] [CrossRef] [PubMed]
  34. Qin, B.Y.; Chacko, B.M.; Lam, S.S.; De Caestecker, M.P.; Correia, J.J.; Lin, K. Structural basis of Smad1 activation by receptor kinase phosphorylation. Mol. Cell 2001, 8, 1303–1312. [Google Scholar] [CrossRef] [PubMed]
  35. Li, X.; Yuan, Z.; Wei, X.; Li, H.; Zhao, G.; Miao, J.; Wu, D.; Liu, B.; Cao, S.; An, D.; et al. Application potential of bone marrow mesenchymal stem cell (BMSCs) based tissue-engineering for spinal cord defect repair in rat fetuses with spina bifida aperta. J. Mater. Sci. Mater. Med. 2016, 27, 77. [Google Scholar] [CrossRef]
  36. Mitroshina, E.V.; Savyuk, M.O.; Ponimaskin, E.; Vedunova, M.V. Hypoxia-Inducible Factor (HIF) in Ischemic Stroke and Neurodegenerative Disease. Front. Cell Dev. Biol. 2021, 9, 703084. [Google Scholar] [CrossRef]
  37. Di Gregorio, J.; Cilenti, L.; Ambivero, C.T.; Andl, T.; Liao, R.; Zervos, A.S. UBXN7 cofactor of CRL3KEAP1 and CRL2VHL ubiquitin ligase complexes mediates reciprocal regulation of NRF2 and HIF-1α proteins. Biochim. Biophys. Acta Mol. Cell Res. 2021, 1868, 118963. [Google Scholar] [CrossRef] [PubMed]
  38. Zoungrana, L.I.; Krause-Hauch, M.; Wang, H.; Fatmi, M.K.; Bates, L.; Li, Z.; Kulkarni, P.; Ren, D.; Li, J. The Interaction of mTOR and Nrf2 in Neurogenesis and Its Implication in Neurodegenerative Diseases. Cells 2022, 11, 2048. [Google Scholar] [CrossRef]
  39. Varderidou-Minasian, S.; Varderidou-Minasian, S.; Verheijen, B.M.; Schätzle, P.; Hoogenraad, C.C.; Pasterkamp, R.J.; Altelaar, M.; Altelaar, M. Deciphering the Proteome Dynamics during Development of Neurons Derived from Induced Pluripotent Stem Cells. J. Proteome Res. 2020, 19, 2391–2403. [Google Scholar] [CrossRef]
  40. Lawton, A.; Morgan, C.R.; Schreiner, C.G.C.R.; Schreiner, C.G.C.R.; Baumann, J.; Upchurch, B.; Xu, F.; Price, M.S.; Isaacs, G.D. Folate-Dependent Cognitive Impairment Associated With Specific Gene Networks in the Adult Mouse Hippocampus. Front. Nutr. 2020, 7, 574730. [Google Scholar] [CrossRef]
  41. Guo, X.F.; Wang, X.H.; Fu, Y.L.; Meng, Q.; Huang, B.Y.; Yang, R.; Guo, Y.; Du, Y.R.; Wang, X.; Gao, Y.; et al. Elevation of N-acetyltransferase 10 in hippocampal neurons mediates depression- and anxiety-like behaviors. Brain Res. Bull. 2022, 185, 91–98. [Google Scholar] [CrossRef]
  42. Sapir, T.; Kshirsagar, A.; Gorelik, A.; Olender, T.; Porat, Z.; Scheffer, I.E.; Goldstein, D.B.; Devinsky, O.; Reiner, O. Heterogeneous nuclear ribonucleoprotein U (HNRNPU) safeguards the developing mouse cortex. Nat. Commun. 2022, 13, 4209. [Google Scholar] [CrossRef]
  43. Girardi, C.S.; Rostirolla, D.C.; Lini, F.J.M.; Brum, P.O.; Delgado, J.; Ribeiro, C.T.; Teixeira, A.A.; Peixoto, D.O.; Heimfarth, L.; Kunzler, A.; et al. Nuclear RXRα and RXRβ receptors exert distinct and opposite effects on RA-mediated neuroblastoma differentiation. Biochim. Biophys. Acta—Mol. Cell Res. 2019, 1866, 317–328. [Google Scholar] [CrossRef]
  44. Yu, S.; Levi, L.; Siegel, R.; Noy, N. Retinoic Acid Induces Neurogenesis by Activating Both Retinoic Acid Receptors (RARs) and Peroxisome Proliferator-activated Receptor β/δ (PPARβ/δ). J. Biol. Chem. 2012, 287, 42195. [Google Scholar] [CrossRef] [PubMed]
  45. Wu, J.; Zhao, Z.; Kumar, A.; Lipinski, M.M.; Loane, D.J.; Stoica, B.A.; Faden, A.I. Endoplasmic Reticulum Stress and Disrupted Neurogenesis in the Brain Are Associated with Cognitive Impairment and Depressive-Like Behavior after Spinal Cord Injury. J. Neurotrauma 2016, 33, 1919. [Google Scholar] [CrossRef]
  46. Wang, Y.; Lin, L.; Lai, H.; Parada, L.F.; Lei, L. Transcription factor Sox11 is essential for both embryonic and adult neurogenesis. Dev. Dyn. 2013, 242, 638–653. [Google Scholar] [CrossRef] [PubMed]
  47. Sha, L.; Kitchen, R.; Porteous, D.; Blackwood, D.; Muir, W.; Pickard, B. SOX11 target genes: Implications for neurogenesis and neuropsychiatric illness. Acta Neuropsychiatr. 2012, 24, 16–25. [Google Scholar] [CrossRef] [PubMed]
  48. Ferrari, E.; Gandellini, P. Unveiling the ups and downs of miR-205 in physiology and cancer: Transcriptional and post-transcriptional mechanisms. Cell Death Dis. 2020, 11, 980. [Google Scholar] [CrossRef] [PubMed]
  49. Mouillet, J.F.; Chu, T.; Nelson, D.M.; Mishima, T.; Sadovsky, Y. MiR-205 silences MED1 in hypoxic primary human trophoblasts. FASEB J. 2010, 24, 2030. [Google Scholar] [CrossRef] [PubMed]
  50. Li, C.; Finkelstein, D.; Sherr, C.J. Arf tumor suppressor and miR-205 regulate cell adhesion and formation of extraembryonic endoderm from pluripotent stem cells. Proc. Natl. Acad. Sci. USA 2013, 110, E1112–E1121. [Google Scholar] [CrossRef] [PubMed]
  51. Dasargyri, A.; González Rodríguez, D.; Rehrauer, H.; Reichmann, E.; Biedermann, T.; Moehrlen, U. scRNA-Seq of Cultured Human Amniotic Fluid from Fetuses with Spina Bifida Reveals the Origin and Heterogeneity of the Cellular Content. Cells 2023, 12, 1577. [Google Scholar] [CrossRef] [PubMed]
  52. Karwowski, W.; Lekesiz, K.; Koc-Żórawska, E.; Wnuczko, K.; Borysewicz-Sanczyk, H.; Naumnik, B. Effects of 17β-estradioland raloxifene on endothelial OPG and RANKL secretion. Ginekol. Pol. 2017, 88, 167–173. [Google Scholar] [CrossRef] [PubMed]
Ijms 25 02896 g001
Figure 1. The amniotic fluid assessment in SB pathogenesis identification. (A) DE miRNAs in amniotic fluid with significantly different expressions between SB (n = 6) and control group (n = 20) (|FC| > 1.5; FDR < 0.05); (B) the network of the top 10 hub genes; (C) gene ontology (GO) enrichment analysis. Top 10 significantly enriched GO (−log10 (p-value)) terms of the target genes in the cellular components, molecular function, and biological processes. KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 1. The amniotic fluid assessment in SB pathogenesis identification. (A) DE miRNAs in amniotic fluid with significantly different expressions between SB (n = 6) and control group (n = 20) (|FC| > 1.5; FDR < 0.05); (B) the network of the top 10 hub genes; (C) gene ontology (GO) enrichment analysis. Top 10 significantly enriched GO (−log10 (p-value)) terms of the target genes in the cellular components, molecular function, and biological processes. KEGG, Kyoto Encyclopedia of Genes and Genomes.
Ijms 25 02896 g001
Ijms 25 02896 g002
Figure 2. ROC analysis was constructed to evaluate the diagnostic values of the selected miRNAs as predictive biomarkers of SB (FC > 1.5; FDR < 0.05): (A) miR-1253; (B) miR-1290; (C) miR-194-5p; (D) miR-302d-3p; (E) miR-3144-3p; (F) let-7b-5p; (G) miR-122-5p; (H) miR-23a-3p; (I) miR-30d-5p.
Figure 2. ROC analysis was constructed to evaluate the diagnostic values of the selected miRNAs as predictive biomarkers of SB (FC > 1.5; FDR < 0.05): (A) miR-1253; (B) miR-1290; (C) miR-194-5p; (D) miR-302d-3p; (E) miR-3144-3p; (F) let-7b-5p; (G) miR-122-5p; (H) miR-23a-3p; (I) miR-30d-5p.
Ijms 25 02896 g002
Ijms 25 02896 g003
Figure 3. ROC analysis was constructed to evaluate the diagnostic values of the selected miRNAs in amniotic fluid as predictive biomarkers of SB (FC > 1.5; FDR < 0.05): (A) miR-320e; (B) miR-4536-5p; (C) miR-549aa + miR-548t-3p; (D) miR-548ar-5p; (E) miR-548n; (F) let-590-5p; (G) miR-612; (H) miR-627-5p; (I) miR-644a; (J) miR-873-3p.
Figure 3. ROC analysis was constructed to evaluate the diagnostic values of the selected miRNAs in amniotic fluid as predictive biomarkers of SB (FC > 1.5; FDR < 0.05): (A) miR-320e; (B) miR-4536-5p; (C) miR-549aa + miR-548t-3p; (D) miR-548ar-5p; (E) miR-548n; (F) let-590-5p; (G) miR-612; (H) miR-627-5p; (I) miR-644a; (J) miR-873-3p.
Ijms 25 02896 g003
Ijms 25 02896 g004
Figure 4. The plasma analysis among SB pathogenesis identifications. (A) The DE miRNAs in plasma with significantly different expressions between SB (n = 6) and control group (n = 20) (FC > 1.5; FDR < 0.05); (B) the networks of the top 10 hub genes targeted by miR-320e; (C) gene ontology (GO) enrichment analysis. Top significantly enriched GO (−log10 (p-value)) categories of the target genes in the cellular components, and biological processes.
Figure 4. The plasma analysis among SB pathogenesis identifications. (A) The DE miRNAs in plasma with significantly different expressions between SB (n = 6) and control group (n = 20) (FC > 1.5; FDR < 0.05); (B) the networks of the top 10 hub genes targeted by miR-320e; (C) gene ontology (GO) enrichment analysis. Top significantly enriched GO (−log10 (p-value)) categories of the target genes in the cellular components, and biological processes.
Ijms 25 02896 g004
Ijms 25 02896 g005
Figure 5. ROC analysis was conducted to evaluate the diagnostic value of miR-320e as diagnostic biomarker of SB vs. control.
Figure 5. ROC analysis was conducted to evaluate the diagnostic value of miR-320e as diagnostic biomarker of SB vs. control.
Ijms 25 02896 g005
Table 1. Maternal circulating miRNA panel for SB screening.
Table 1. Maternal circulating miRNA panel for SB screening.
PANELTP RateFP RatePrecisionAUCInterceptCoefficients
x1 = miR-205-5p
x2 = miR-362-5p		0.8330.1680.8440.94413.9851a1 = −19.2974
a2 = 0.2402
TP Rate—true positive rate; FP Rate—false positive rate; AUC—area under the receiver operating characteristic curve.
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

Buczyńska, A.; Sidorkiewicz, I.; Niemira, M.; Krętowski, A.J.; Węgrzyn, P.; Kosiński, P.; Zbucka-Krętowska, M. Identification of MicroRNA Profiles in Fetal Spina Bifida: The Role in Pathomechanism and Diagnostic Significance. Int. J. Mol. Sci. 2024, 25, 2896. https://doi.org/10.3390/ijms25052896

AMA Style

Buczyńska A, Sidorkiewicz I, Niemira M, Krętowski AJ, Węgrzyn P, Kosiński P, Zbucka-Krętowska M. Identification of MicroRNA Profiles in Fetal Spina Bifida: The Role in Pathomechanism and Diagnostic Significance. International Journal of Molecular Sciences. 2024; 25(5):2896. https://doi.org/10.3390/ijms25052896

Chicago/Turabian Style

Buczyńska, Angelika, Iwona Sidorkiewicz, Magdalena Niemira, Adam Jacek Krętowski, Piotr Węgrzyn, Przemysław Kosiński, and Monika Zbucka-Krętowska. 2024. "Identification of MicroRNA Profiles in Fetal Spina Bifida: The Role in Pathomechanism and Diagnostic Significance" International Journal of Molecular Sciences 25, no. 5: 2896. https://doi.org/10.3390/ijms25052896

APA Style

Buczyńska, A., Sidorkiewicz, I., Niemira, M., Krętowski, A. J., Węgrzyn, P., Kosiński, P., & Zbucka-Krętowska, M. (2024). Identification of MicroRNA Profiles in Fetal Spina Bifida: The Role in Pathomechanism and Diagnostic Significance. International Journal of Molecular Sciences, 25(5), 2896. https://doi.org/10.3390/ijms25052896

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop