The cytotrophoblast of human placenta transitions into an outer multinucleated syncytiotrophoblast (STB) layer that covers chorionic villi which are in contact with maternal blood in the intervillous space. During pregnancy, the Zika virus (ZIKV) poses a serious prenatal threat. STB cells are resistant to ZIKV infections, yet placental cells within the mesenchyme of chorionic villi are targets of ZIKV infection. We seek to determine whether ZIKV can open the paracellular pathway of STB cells. This route is regulated by tight junctions (TJs) which are present in the uppermost portion of the lateral membranes of STB cells. We analyzed the paracellular permeability and expression of E-cadherin, occludin, JAMs –B and –C, claudins -1, -3, -4, -5 and -7, and ZO-1, and ZO-2 in the STB of placentae from ZIKV-infected and non-infected women. In ZIKV-infected placentae, the pattern of expression of TJ proteins was preserved, but the amount of claudin-4 diminished. Placentae from ZIKV-infected women were permeable to ruthenium red, and had chorionic villi with a higher mean diameter and Hofbauer hyperplasia. Finally, ZIKV added to the basolateral surface of a trophoblast cell line reduced the transepithelial electrical resistance. These results suggest that ZIKV can open the paracellular pathway of STB cells.

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Preeclampsia (PE) and Intrauterine Growth Restriction (IUGR) are major contributors to perinatal morbidity and mortality. These pregnancy disorders are associated with placental dysfunction and share similar pathophysiological features. The aim of this study was to compare the placental gene expression profiles including mRNA and lncRNAs from pregnant women from four study groups: PE, IUGR, PE-IUGR, and normal pregnancy (NP). Gene expression microarray analysis was performed on placental tissue obtained at delivery and results were validated using RTq-PCR. Differential gene expression analysis revealed that the largest transcript variation was observed in the IUGR samples compared to NP (n = 461; 314 mRNAs: 252 up-regulated and 62 down-regulated; 133 lncRNAs: 36 up-regulated and 98 down-regulated). We also detected a group of differentially expressed transcripts shared between the PE and IUGR samples compared to NP (n = 39), including 9 lncRNAs with a high correlation degree (p < 0.05). Functional enrichment of these shared transcripts showed that cytokine signaling pathways, protein modification, and regulation of JAK-STAT cascade are over-represented in both placental ischemic diseases. These findings contribute to the molecular characterization of placental ischemia showing common epigenetic regulation implicated in the pathophysiology of PE and IUGR.

Fetal exposure to essential and toxic metals can influence life-long health trajectories. The placenta regulates chemical transmission from maternal circulation to the fetus and itself exhibits a complex response to environmental stressors. The placenta can thus be a useful matrix to monitor metal exposures and stress responses in utero, but strategies to explore the biologic effects of metal mixtures in this organ are not well-developed. In this proof-of-concept study, we used laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to measure the distributions of multiple metals in placental tissue from a low-birth-weight pregnancy, and we developed an approach to identify the components of metal mixtures that colocalized with biological response markers. Our novel workflow, which includes custom-developed software tools and algorithms for spatial outlier identification and background subtraction in multidimensional elemental image stacks, enables rapid image processing and seamless integration of data from elemental imaging and immunohistochemistry. Using quantitative spatial statistics, we identified distinct patterns of metal accumulation at sites of inflammation. Broadly, our multiplexed approach can be used to explore the mechanisms mediating complex metal exposures and biologic responses within placentae and other tissue types. Our LA-ICP-MS image processing workflow can be accessed through our interactive R Shiny application ‘shinyImaging’, which is available at https://mniedz.shinyapps.io/shinyImaging/.

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2′-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R2 = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2′-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.