Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

Extracellular Vesicles Alter Trophoblast Function in Pregnancies Complicated by COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and resulting coronavirus disease (COVID-19) cause placental dysfunction, which increases the risk of adverse pregnancy outcomes. While abnormal placental pathology resulting from COVID-19 is common, direct infection of the placenta is rare. This suggests that pathophysiology associated with maternal COVID-19, rather than direct placental infection, is responsible for placental dysfunction. We hypothesized that maternal circulating extracellular vesicles (EVs), altered by COVID-19 during pregnancy, contribute to placental dysfunction. To examine this hypothesis, we characterized circulating EVs from pregnancies complicated by COVID-19 and tested their effects on trophoblast cell physiology in vitro. Trophoblast exposure to EVs isolated from patients with an active infection (AI), but not controls, altered key trophoblast functions including hormone production and invasion. Thus, circulating EVs from participants with an AI, both symptomatic and asymptomatic cases, can disrupt vital trophoblast functions. EV cargo differed between participants with COVID-19, depending on the gestational timing of infection, and Controls, which may contribute to the disruption of the placental transcriptome and morphology. Our findings show that COVID-19 can have effects throughout pregnancy on circulating EVs, and circulating EVs are likely to participate in placental dysfunction induced by COVID-19.

Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants

BACKGROUND

The variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration.

RESULTS

To address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data.

CONCLUSIONS

In summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC .

Large-Scale Proteomics in Early Pregnancy and Hypertensive Disorders of Pregnancy

Importance

There is no consensus regarding the best method for prediction of hypertensive disorders of pregnancy (HDP), including gestational hypertension and preeclampsia.

Objective

To determine predictive ability in early pregnancy of large-scale proteomics for prediction of HDP.

Design, Setting, and Participants

This was a nested case-control study, conducted in 2022 to 2023, using clinical data and plasma samples collected between 2010 and 2013 during the first trimester, with follow-up until pregnancy outcome. This multicenter observational study took place at 8 academic medical centers in the US. Nulliparous individuals during first-trimester clinical visits were included. Participants with HDP were selected as cases; controls were selected from those who delivered at or after 37 weeks without any HDP, preterm birth, or small-for-gestational-age infant. Age, self-reported race and ethnicity, body mass index, diabetes, health insurance, and fetal sex were available covariates.

Exposures

Proteomics using an aptamer-based assay that included 6481 unique human proteins was performed on stored plasma. Covariates were used in predictive models.

Main Outcomes and Measures

Prediction models were developed using the elastic net, and analyses were performed on a randomly partitioned training dataset comprising 80% of study participants, with the remaining 20% used as an independent testing dataset. Primary measure of predictive performance was area under the receiver operating characteristic curve (AUC).

Results

This study included 753 HDP cases and 1097 controls with a mean (SD) age of 26.9 (5.5) years. Maternal race and ethnicity were 51 Asian (2.8%), 275 non-Hispanic Black (14.9%), 275 Hispanic (14.9%), 1161 non-Hispanic White (62.8% ), and 88 recorded as other (4.8%), which included those who did not identify according to these designations. The elastic net model, allowing for forced inclusion of prespecified covariates, was used to adjust protein-based models for clinical and demographic variables. Under this approach, no proteins were selected to augment the clinical and demographic covariates. The predictive performance of the resulting model was modest, with a training set AUC of 0.64 (95% CI, 0.61-0.67) and a test set AUC of 0.62 (95% CI, 0.56-0.68). Further adjustment for study site yielded only minimal changes in AUCs.

Conclusions and Relevance

In this case-control study with detailed clinical data and stored plasma samples available in the first trimester, an aptamer-based proteomics panel did not meaningfully add to predictive utility over and above clinical and demographic factors that are routinely available.

Identifying molecular subgroups of patients with preeclampsia through bioinformatics

Preeclampsia (PE) is a pregnancy-related disorder associated with serious complications. Its molecular mechanisms remain undefined; hence, we aimed to identify molecular subgroups of patients with PE using bioinformatics to aid treatment strategies. R software was used to analyze gene expression data of 130 patients with PE and 138 healthy individuals from the Gene Expression Omnibus database. Patients with PE were divided into two molecular subgroups using the unsupervised clustering learning method. Clinical feature analysis of subgroups using weighted gene co-expression network analysis showed that the patients in subgroup I were primarily characterized by early onset of PE, severe symptoms at disease onset, and induced labor as the main delivery method. Patients in subgroup II primarily exhibited late PE onset, relatively mild symptoms, and natural delivery as the main delivery method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the significant enrichment of calcium ion channels in subgroup II indicated the potential efficacy of calcium antagonists and magnesium sulfate therapy. In conclusion, the establishment of PE molecular subgroups can aid in diagnosing and treating PE.

Bioinformatics identification and validation of maternal blood biomarkers and immune cell infiltration in preeclampsia: An observational study

Preeclampsia (PE) is a pregnancy complication characterized by placental dysfunction. However, the relationship between maternal blood markers and PE is unclear. It is helpful to improve the diagnosis and treatment of PE using new biomarkers related to PE in the blood. Three PE-related microarray datasets were obtained from the Gene Expression Synthesis database. The limma software package was used to identify differentially expressed genes (DEGs) between PE and control groups. Least absolute shrinkage and selection operator regression, support vector machine, random forest, and multivariate logistic regression analyses were used to determine key diagnostic biomarkers, which were verified using clinical samples. Subsequently, functional enrichment analysis was performed. In addition, the datasets were combined for immune cell infiltration analysis and to determine their relationships with core diagnostic biomarkers. The diagnostic performance of key genes was evaluated using the receiver operating characteristic (ROC) curve, C-index, and GiViTi calibration band. Genes with potential clinical applications were evaluated using decision curve analysis (DCA). Seventeen DEGs were identified, and 6 key genes (FN1, MYADM, CA6, PADI4, SLC4A10, and PPP4R1L) were obtained using 3 types of machine learning methods and logistic regression. High diagnostic performance was found for PE through evaluation of the ROC, C-index, GiViti calibration band, and DCA. The 2 types of immune cells (M0 macrophages and activated mast cells) were significantly different between patients with PE and controls. All of these genes except SLC4A10 showed significant differences in expression levels between the 2 groups using quantitative reverse transcription-polymerase chain reaction. This model used 6 maternal blood markers to predict the occurrence of PE. The findings may stimulate ideas for the treatment and prevention of PE.

Extracellular vesicles alter trophoblast function in pregnancies complicated by COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and resulting coronavirus disease (COVID-19) causes placental dysfunction, which increases the risk of adverse pregnancy outcomes. While abnormal placental pathology resulting from COVID-19 is common, direct infection of the placenta is rare. This suggests that pathophysiology associated with maternal COVID-19, rather than direct placental infection, is responsible for placental dysfunction and alteration of the placental transcriptome. We hypothesized that maternal circulating extracellular vesicles (EVs), altered by COVID-19 during pregnancy, contribute to placental dysfunction. To examine this hypothesis, we characterized maternal circulating EVs from pregnancies complicated by COVID-19 and tested their effects on trophoblast cell physiology in vitro . We found that the gestational timing of COVID-19 is a major determinant of circulating EV function and cargo. In vitro trophoblast exposure to EVs isolated from patients with an active infection at the time of delivery, but not EVs isolated from Controls, altered key trophoblast functions including hormone production and invasion. Thus, circulating EVs from participants with an active infection, both symptomatic and asymptomatic cases, can disrupt vital trophoblast functions. EV cargo differed between participants with COVID-19 and Controls, which may contribute to the disruption of the placental transcriptome and morphology. Our findings show that COVID-19 can have effects throughout pregnancy on circulating EVs and circulating EVs are likely to participate in placental dysfunction induced by COVID-19.

Pregnancy as a susceptible state for thrombotic microangiopathies

Pregnancy and the postpartum period represent phases of heightened vulnerability to thrombotic microangiopathies (TMAs), as evidenced by distinct patterns of pregnancy-specific TMAs (e.g., preeclampsia, HELLP syndrome), as well as a higher incidence of nonspecific TMAs, such as thrombotic thrombocytopenic purpura or hemolytic uremic syndrome, during pregnancy. Significant strides have been taken in understanding the underlying mechanisms of these disorders in the past 40 years. This progress has involved the identification of pivotal factors contributing to TMAs, such as the complement system, ADAMTS13, and the soluble VEGF receptor Flt1. Regardless of the specific causal factor (which is not generally unique in relation to the usual multifactorial origin of TMAs), the endothelial cell stands as a central player in the pathophysiology of TMAs. Pregnancy has a major impact on the physiology of the endothelium. Besides to the development of placenta and its vascular consequences, pregnancy modifies the characteristics of the women's microvascular endothelium and tends to render it more prone to thrombosis. This review aims to delineate the distinct features of pregnancy-related TMAs and explore the contributing mechanisms that lead to this increased susceptibility, particularly influenced by the "gravid endothelium." Furthermore, we will discuss the potential contribution of histopathological studies in facilitating the etiological diagnosis of pregnancy-related TMAs.

10. Role of high dimensional technology in preeclampsia (omics in preeclampsia)

Preeclampsia is a pregnancy-specific disease that has no known precise cause. Integrative biology approach based on multi-omics has been applied to identify upstream pathways and better understand the pathophysiology of preeclampsia. At DNA level, genomics and epigenomics studies have revealed numerous genetic variants associated with preeclampsia, including those involved in regulating blood pressure and immune response. Transcriptomics analyses have revealed altered expression of genes in preeclampsia, particularly those related to inflammation and angiogenesis. At protein level, proteomics studies have identified potential biomarkers for preeclampsia diagnosis and prediction in addition to revealing the main pathophysiological pathways involved in this disease. At metabolite level, metabolomics has highlighted altered lipid and amino acid metabolisms in preeclampsia. Finally, microbiomics studies have identified dysbiosis in the gut and vaginal microbiota in pregnant women with preeclampsia. Overall, omics technologies have improved our understanding of the complex molecular mechanisms underlying preeclampsia. However, further research is warranted to fully integrate and translate these omics findings into clinical practice.