Multiomics Studies Investigating Recurrent Pregnancy Loss: An Effective Tool for Mechanism Exploration

Syncytiotrophoblast Markers Are Downregulated in Placentas from Idiopathic Stillbirths

The trophoblast cells are responsible for the transfer of nutrients between the mother and the foetus and play a major role in placental endocrine function by producing and releasing large amounts of hormones and growth factors. Syncytiotrophoblast cells (STB), formed by the fusion of mononuclear cytotrophoblasts (CTB), constitute the interface between the foetus and the mother and are essential for all of these functions. We performed transcriptome analysis of human placental samples from two control groups-live births (LB), and stillbirths (SB) with a clinically recognised cause-and from our study group, idiopathic stillbirths (iSB). We identified 1172 DEGs in iSB, when comparing with the LB group; however, when we compared iSB with the SB group, only 15 and 12 genes were down- and upregulated in iSB, respectively. An assessment of these DEGs identified 15 commonly downregulated genes in iSB. Among these, several syncytiotrophoblast markers, like genes from the PSG and CSH families, as well as ALPP, KISS1, and CRH, were significantly downregulated in placental samples from iSB. The transcriptome analysis revealed underlying differences at a molecular level involving the syncytiotrophoblast. This suggests that defects in the syncytial layer may underlie unexplained stillbirths, therefore offering insights to improve clinical obstetrics practice.

Clinical Relevance of Vaginal and Endometrial Microbiome Investigation in Women with Repeated Implantation Failure and Recurrent Pregnancy Loss

Recent studies have investigated if and how the vaginal and endometrial microbiome might affect endometrial receptivity and reproductive health. Although there is no consensus on the existence of a core uterine microbiome yet, evidence shows that the dominance of Lactobacillus spp. in the female reproductive tract is generally associated with eubiosis and improved chances of successful implantation and an ongoing pregnancy. Conversely, vaginal and endometrial dysbiosis can cause local inflammation and an increase of pro-inflammatory cytokines, compromising the integrity and receptivity of the endometrial mucosa and potentially hampering successful embryonic implantation. This review provides a critical appraisal of the influence of the vaginal and endometrial microbiome as parts of the female reproductive tract on fertility outcomes, focusing on repeated implantation failure (RIF) and recurrent pregnancy loss (RPL). It seems that RIF as well as RPL are both associated with an increase in microbiome diversity and a loss of Lactobacillus dominance in the lower female reproductive system.

Glycolysis: A fork in the path of normal and pathological pregnancy

Glucose metabolism is vital to the survival of living organisms. Since the discovery of the Warburg effect in the 1920s, glycolysis has become a major research area in the field of metabolism. Glycolysis has been extensively studied in the field of cancer and is considered as a promising therapeutic target. However, research on the role of glycolysis in pregnancy is limited. Recent evidence suggests that blastocysts, trophoblasts, decidua, and tumors all acquire metabolic energy at specific stages in a highly similar manner. Glycolysis, carefully controlled throughout pregnancy, maintains a dynamic and coordinated state, so as to maintain the homeostasis of the maternal-fetal interface and ensure normal gestation. In the present review, we investigate metabolic remodeling and the selective propensity of the embryo and placenta for glycolysis. We then address dysregulated glycolysis that occurs in the cellular interactive network at the maternal-fetal interface in miscarriage, preeclampsia, fetal growth restriction, and gestational diabetes mellitus. We provide new insights into the field of maternal-fetal medicine from a metabolic perspective, thus revealing the mystery of human pregnancy.

Relationship between air pollutants and spontaneous abortion in a coal resource valley city: a retrospective cohort study

OBJECTIVE

Pollutants in the atmosphere have been linked to poor pregnancy outcomes in women. However, such investigations are scarce in metropolitan northern China. The major exposure window of air pollution affecting pregnant women is also unknown.

METHODS

For the analysis, this retrospective cohort study enrolled 6960 pregnant women recorded at Tongchuan People's Hospital from January 2018 to December 2019. Pollutant concentration values from the nearest monitoring station to the pregnant women were used to estimate exposure doses for each exposure window. Logistic regression models were created to investigate the connection between pollutants and spontaneous abortion while controlling for confounding factors.

RESULTS

PM2.5 was a risk factor for spontaneous abortion in T3 (30-60 days before the first day of the last menstrual period [LMP]), (OR: 1.305, 95% CI: 1.143-1.490) and T4 (60-90 days before the first day of the LMP),(OR: 1.450, 95% CI: 1.239-1.696) after controlling for covariates. In the same window, PM10 was a risk factor (OR: 1.308, 95% CI: 1.140-1.500), (OR: 1.386, 95% CI: 1.184-1.621). In T2 (30 days before the first day of the LMP), T3, and T4, SO2 was a risk factor for spontaneous abortion (OR: 1.185, 95% CI: 1.025-1.371), (OR: 1.219, 95% CI: 1.071-1.396), (OR: 1.202, 95% CI: 1.040-1.389). In T3 and T4, NO2 was a risk factor (OR: 1.171, 95% CI: 1.019- 1.346), (OR: 1.443, 95% CI: 1.259-1.655). In T1 (from the first day of the LMP to the date of abortion), O3 was found to be a risk factor (OR: 1.366, 95% CI: 1.226-1.521).

CONCLUSION

Exposure to high levels of air pollutants before and during pregnancy may be a risk factor for spontaneous abortion in pregnant women. This study further illustrates the importance of reducing air pollution emissions.

Role of the HLA-G regulatory region polymorphisms in idiopathic recurrent spontaneous abortions (RSA)

PROBLEM

HLA-G polymorphisms have a functional impact on its expression and may cause a breakdown of maternal tolerance towards the semi-allogenic fetus, resulting in recurrent spontaneous abortions (RSA). This study reports on the association of HLA-G regulatory region polymorphisms with idiopathic RSA.

METHODS

Seventy-five couples with ≥2 spontaneous abortions were recruited in comparison to 75 healthy couples who had normal pregnancies. About 5 mL of blood samples were collected from all the participants, and DNA was extracted. Screening of HLA-G 5'-upstream regulatory region (5'-URR) was done by direct sequencing in 50 each of RSA and healthy couples, respectively. The 14 bp deletion/insertion polymorphism in the 3'-untranslated region (3'-UTR) was genotyped in 75 each of RSA and healthy couples, respectively, by PCR amplification of HLA-G exon 8. MedCalc, GraphPad Prism, Haploview, PLINK, and multifactor dimensionality reduction were used to analyze the data.

RESULTS

HLA-G screening revealed the presence of -762C/T, -725C/G, -716T/G, -689A/G, -486C/A, and -477C/G single nucleotide polymorphisms (SNPs) in the 5'-URR. At positions -762 and -477, the frequency of CC homozygotes was significantly higher in controls compared to the patients. The 14 bp deletion/insertion polymorphism in the 3'-UTR showed an association with RSA with the heterozygous genotype being significantly higher in RSA compared to controls.

CONCLUSIONS

The study indicates a protective role of the CC genotypes of the two HLA-G 5'-URR polymorphisms, -762C/T and -477C/G, against RSA. It also suggests that women with the 14 bp deletion/insertion genotype have a significantly higher risk of RSA.

Intercellular communication involving macrophages at the maternal-fetal interface may be a pivotal mechanism of URSA: a novel discovery from transcriptomic data

Unexplained recurrent spontaneous abortion (URSA) is a severe challenge to reproductive females worldwide, and its etiology and pathogenesis have not yet been fully clarified. Abnormal intercellular communication between macrophages (Mφ) and decidual stromal cells (DSCs) or trophoblasts has been supposed to be the key to URSA. However, the exact molecular mechanisms in the crosstalk are not yet well understood. This study aimed to explore the potential molecule mechanism that may be involved in the communication between Mφ and DSC or trophoblast cells and determine their diagnostic characteristics by using the integrated research strategy of bioinformatics analysis, machine learning and experiments. First, microarrays of decidual tissue (GSE26787, GSE165004) and placenta tissue (GSE22490) in patients with URSA, as well as microarrays involving induced decidualization (GSE94644) and macrophage polarization in vitro (GSE30595) were derived from the gene expression omnibus (GEO) database. And 721 decidua-differentially expressed genes (DEGs), 613 placenta-DEGs, 510 Mφ polarization DEGs were obtained in URSA by differential expression analysis. Then, the protein-protein interaction (PPI) network was constructed, and the hub genes were identified by CytoHubba in Cytoscape software and validated by real-time PCR assay. Subsequently, immune enrichment analysis on decidua-DEGs and placenta-DEGs by ClueGO verified their regulation effects on Mφ. Besides, functional enrichment analysis was performed on Mφ polarization DEGs and the essential module genes derived from the weighted gene co-expression network analysis (WGCNA) to uncover the biological function that were related to abnormal polarization of Mφ. Furthermore, we screened out 29, 43 and 22 secreted protein-encoding genes from DSC-DEGs, placenta-DEGs and Mφ polarization DEGs, respectively. Besides, the hub secreted-protein-encoding genes were screened by CytoHubba. Moreover, we conducted functional enrichment analysis on these genes. And spearman correlation analysis between hub secreted-protein-encoding genes from donor cells and hub genes in recipient cells was performed to further understand the molecular mechanism of intercellular communication further. Moreover, signature genes with diagnostic value were screened from secreted protein-encoding genes by machine learning and validated by immunofluorescence co-localization analysis with clinical samples. Finally, three biomarkers of DSCs (FGF9, IL1R2, NID2) and three biomarkers of Mφ (CFB, NID2, CXCL11) were obtained. In conclusion, this project provides new ideas for understanding the mechanism regulatory network of intercellular communication involving macrophages at the maternal-fetal interface of URSA. Also, it provides innovative insights for the diagnosis and treatment of URSA.

Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. Machine learning applied to FT-Raman spectra study

The presented article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood serum samples in patients with diagnosed recurrent pregnancy loss (RPL) versus healthy individuals who were followed at the Gynecology department. A total of 120 participants, RPL disease (n = 60) and healthy individuals (n = 60), participated in the study. First, we investigated the effect of circulating nerve growth factor (NGF) in RPL and healthy groups. To show NGF's effect, we measured the level of oxidative loads such as Total Antioxidant Level (TAS), Total Oxidant Level (TOS), and Oxidative Stress Index (OSI) with Beckman Coulter AU system and biochemical assays. We find a correlation between oxidative load and NGF level. Oxidative load mainly causes structural changes in the blood. Therefore, we obtained Raman measurements of the participant's serum. Then we selected two Raman regions, 800 and 1800 cm^-1, and between 2700 cm^-1 and 3000 cm^-1, to see chemical changes. We noted that Raman spectra obtained for RPL and healthy women differed. The findings confirm that the imbalance between reactive oxygen species and antioxidants has important implications for the pathogenesis of RPL and that NGF levels accompany the level of oxidative load in the RPL state. Biomolecular structure and composition were determined using Raman spectroscopy and machine learning methods, and the correlation of these parameters was studied alongside machine learning technologies to advance toward clinical translation. Here we determined and validated the development of instrumentation for the Analysis of RPL patients' serum that can differentiate from control individuals with an accuracy of 100% using the Raman region corresponding to structural changes. Furthermore, this study found a correlation between traditional biochemical parameters and Raman data. This suggests that Raman spectroscopy is a sensitive tool for detecting biochemical changes in serum caused by RPL or other diseases.

Current Status and Prospects of the Single-Cell Sequencing Technologies for Revealing the Pathogenesis of Pregnancy-Associated Disorders

Single-cell RNA sequencing (scRNA-seq) is a method that focuses on the analysis of gene expression profile in individual cells. This method has been successfully applied to answer the challenging questions of the pathogenesis of multifactorial diseases and open up new possibilities in the prognosis and prevention of reproductive diseases. In this article, we have reviewed the application of scRNA-seq to the analysis of the various cell types and their gene expression changes in normal pregnancy and pregnancy complications. The main principle, advantages, and limitations of single-cell technologies and data analysis methods are described. We discuss the possibilities of using the scRNA-seq method for solving the fundamental and applied tasks related to various pregnancy-associated disorders. Finally, we provide an overview of the scRNA-seq findings for the common pregnancy-associated conditions, such as hyperglycemia in pregnancy, recurrent pregnancy loss, preterm labor, polycystic ovary syndrome, and pre-eclampsia.

Towards reproducible research in recurrent pregnancy loss immunology: Learning from cancer microenvironment deconvolution

The most recent international guidelines regarding recurrent pregnancy loss (RPL) exclude most of the immunological tests recommended for RPL since they do not reach an evidence-based level. Comparisons for metanalysis and systematic reviews are limited by the ambiguity in terms of RPL definition, etiological and risk factors, diagnostic work-up, and treatments applied. Therefore, cohort heterogeneity, the inadequacy of numerosity, and the quality of data confirm a not standardized research quality in the RPL field, especially for immunological background, for which potential research application remains confined in a separate single biological layer. Innovative sequencing technologies and databases have proved to play a significant role in the exploration and validation of cancer research in the context of dataset quality and bioinformatics tools. In this article, we will investigate how bioinformatics tools born for large-scale cancer immunological research could revolutionize RPL immunological research but are limited by the nature of current RPL datasets.

The Progress of Research on Genetic Factors of Recurrent Pregnancy Loss

Recurrent pregnancy loss (RPL) is both mental and physical health problem affecting about 1-5% of women of childbearing age. The etiology of RPL is complex, involving chromosomal abnormalities, autoimmune diseases, metabolic disorders, and endometrial dysfunction. The causes of abortion are still unknown in more than 50% of these cases. With the development of science and technology, an increasing number of scholars focus on this field and find that genetic factors may play an essential role in unexplained RPL, such as embolism-related genes, immune factor-related genes, and chromosomal numeric, and structural variation. This review summarizes the genetic factors associated with RPL, including genetic mutations and genetic polymorphisms, chromosomal variants, and chromosomal polymorphisms. Many related genetic factors have been found to be demographically and geographically relevant, some of which can be used for risk prediction or screening for the etiology of RPL. However, it is difficult to predict and prevent RPL due to uncertain pathogenesis and highly variable clinical presentation. Therefore, the genetic factors of RPL still need plentiful research to obtain a more accurate understanding of its pathogenesis and to provide more detection means for the screening and prevention of RPL.

Prevalence and associated factors of recurrent pregnancy loss in Nigeria according to different national and international criteria (ASRM/ESHRE vs. WHO/RCOG)

Background

In low-and middle-income countries, no conclusive research explains the prevalence and associated factors of women with a history of recurrent pregnancy loss (RPL). Some authorities have recommended further scientific research on the effect of various definitions of RPL.

Objective

To assess prevalence and associated factors of RPL among pregnant women in Nigeria according to different national and international criteria: the American Society for Reproductive Medicine/ European Society for Human Reproduction and Embryology (ASRM/ESHRE; two losses) and the World Health Organization/ Royal College of Obstetricians and Gynecologists (WHO/RCOG; three consecutive losses) criteria.

Methods

This is a cross-sectional analytical study wherein, pregnant women with prior RPL were investigated. The outcome measures were prevalence and risk factors. The associations between independent variables and outcome variable were explored using bivariate and multivariable logistic regression models. The results of these analyses were reported as adjusted odds ratios (AORs) with 95% confidence intervals (95%CI). Factors associated with RPL were identified using multivariate regression models.

Result

Of the 378 pregnant women interviewed, the overall prevalence of RPL in this study was found to be 15.34% (95% confidence interval = 11.65%-19.84%). The prevalence of RPL was 15.34% (58/378; 95%CI = 11.65%-19.84%) and 5.29% (20/378; 95%CI = 3.23%-8.17) according to the ASRM and the WHO criterion respectively. Regardless of diagnostic criteria, unexplained (AOR = 23.04; 95%CI: 11.46-36.32), endocrine disturbances (AOR = 9.76; 95%CI: 1.61-63.19), uterine abnormalities (AOR = 13.57; 95%CI: 3.54-50.60), and antiphospholipid syndrome (AOR = 24.59; 95%CI: 8.45-71.04) were positively and independently associated with RPL. No significant risk factors were seen when the ASRM/ ESHRE criterion vs. WHO/RCOG criterion were compared. Advanced maternal age was significantly higher in secondary than in primary type of RPL.

Conclusion

The prevalence of RPL was 15.34% and 5.29% according to ASRM/ESHRE and WHO/RCOG criterion respectively, with secondary type predominating. No significant differences with regard to risk factors were seen according to diagnostic criteria studied, though advanced maternal age was significantly higher in secondary RPL. Further research is needed to confirm our findings and to better characterize the magnitude of differences.

Perspectives from metabolomics in the early diagnosis and prognosis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnant women. The early detection of GDM provides an opportunity for the effective treatment of hyperglycemia in pregnancy, thus decreasing the risk of adverse perinatal outcomes for mothers and newborns. Metabolomics, an emerging technique, offers a novel point of view in understanding the onset and development of diseases and has been repeatedly used in various gestational periods in recent studies of GDM. Moreover, metabolomics provides varied opportunities in the different diagnoses of GDM from prediabetes or predisposition to diabetes, the diagnosis of GDM at a gestational age several weeks earlier than that used in the traditional method, and the assessment of prognosis considering the physiologic subtypes of GDM and clinical indexes. Longitudinal metabolomics truly facilitates the dynamic monitoring of metabolic alterations over the course of pregnancy. Herein, we review recent advancements in metabolomics and summarize evidence from studies on the application of metabolomics in GDM, highlighting the aspects of the diagnosis and differential diagnoses of GDM in an early stage. We also discuss future study directions concerning the physiologic subtypes, prognosis, and limitations of metabolomics.