During gestation, a human fetus is fully dependent on the placenta which provides oxygen, nutrients, regulates waste transport, and acts as an immunological barrier. Glial Cells Missing Transcription Factor 1 (GCM1) is a transcription factor (TF) that plays a crucial role in placental development. Our goal is to understand the downstream effects of GCM1 knockdown on genes necessary for placental development. The BeWo choriocarcinoma cell line is a model of placental syncytiotrophoblasts cells which undergo a cell fusion process called syncytialization to form multinucleated cells that help exchange nutrients. BeWo cells were treated with Forskolin (FSK) to induce syncytialization to represent syncytiotrophoblast cells. RNA sequencing was conducted after GCM1 knockdown using siRNA to quantify downstream gene expression changes. BeWo cells were treated in 4 different conditions: FSK, GCM1 siRNA (GCM1 KD), FSK treatment first then GCM1 siRNA treatment (FSK + GCM1 KD), and lastly, GCM1 siRNA treatment first and then FSK treatment (GCM1 KD + FSK). In the GCM1 KD group, a total of 533 genes exhibited significant differential expression (FDR < 0.5), when compared to control BeWo cells, with 236 genes upregulated and 297 genes downregulated. Subsequently, in the FSK + GCM1 KD group, 961 genes were significantly altered, where 386 were upregulated and 575 were downregulated. In the GCM1 KD + FSK group, a notably higher number of genes, 7,165, displayed significant differential expression, with 3527 genes being upregulated and 3638 genes being downregulated. We also identified a number of biological pathways such as focal adhesion and adherens junction that may be crucial to GCM1-induced syncytialization. Overall, our results will improve our understanding of how GCM1 coordinates gene expression in the placenta during pregnancy.

Vertical velocities are a fundamental component of ocean flow and are vital to characterizing global circulation. However, vertical velocities are small compared to horizontal velocities and are thus difficult to measure. Previous studies attempting to estimate them ignore the impacts of topography, mesoscale eddies, internal waves, and spatial variability. Novel estimates from the Argo float array allow for direct estimates of vertical velocities. This project will focus on comparing these new Argo estimates with vertical velocity observations from moorings in the Southern Ocean. The Southern Ocean is an important site of vertical volume transport for mass ocean circulation with global implications, particularly the Antarctic Circumpolar Current which dynamically links many of these interactions. We expect vertical velocity characterized by moorings to maintain coherency with Argo float estimates. Differences may occur, however, due to mismatches in spatial resolution between Argo-based estimates and mooring-based estimates, which rely on mass conservation across larger scales. In comparing novel Argo datasets to known mooring values, we gain a more complete understanding of vertical velocities in the Southern Ocean which have direct implications for data assimilation in models and parameterization of energy pathways.

The placenta is a crucial fetal organ providing oxygen and nutrients to the developing infant. Placental cell models, which are derived from immortalized or placental cancer cells, are typically used to study the organ. The use of placental cell models is important because human samples are difficult to obtain, and placental physiology is highly species-specific. However, our understanding of these models and how they compare to placental tissue samples is limited. This project aims to determine which placental cell model most directly reflects the gene expression of the human placenta. We obtained twenty-eight RNA sequencing datasets from the HTR-8/SVneo, JEG-3, JAR and BeWo placental cell models as well as human placental villous explants and primary trophoblast cells using the Gene Expression Omnibus database. Fetal sex was determined by quantifying expression of the Y-chromosome for each of the models. From this analysis we identified that HTR-8/Svneo was of female origin, while JEG-3, JAR and BeWo was of male origin. A clustering analysis was also conducted which identified groups of genes that showed similar expression profiles across the groups of cell lines and placenta tissue. This was subsequently used within a pathway analysis to identify which biological pathway defined the cluster. Pathways are chains of reactions leading to products or changes in a cell. The analysis showed at 22 of the 53 clusters were enriched for 1 or more pathways, which helps provide insight into the biological functions of these clusters and indicates biological processes that may be different between these models. With this information we have created an interactive web application. This site allows users to search a given gene and identify the expression data across all the models. This tool aims to provide a resource to the placental biology research community in further investigations of the placenta.

In order to design effective countermeasures against HIV, we must first understand the forces that drive it to evolve resistance within hosts. While linkage patterns in genetic data are potentially a powerful tool to quantify the relative contributions of multiple evolutionary forces (mutation, recombination, selection) acting during an HIV infection, the severe viral population bottlenecks accompanying drug therapy complicate these patterns. To interpret genetic linkage in the context of such major changes in population structure (which are themselves driven by specific mutations), we develop a simulation framework for viral evolution in which genetics and population structure influence each other. This framework overcomes limitations from both dynamical modeling, in which patterns of linked variation are ignored, and from population genetic modeling, in which population structure is predetermined. Using few parameters, we are able to reproduce linkage patterns and population bottlenecks that broadly conform to those observed in vivo. As a case study to demonstrate this model’s utility, we consider a recent hypothesis that viral recombination is suppressed during population bottlenecks due to diminished opportunities for coinfection. In simulating populations with and without recombination suppression during population contraction, we show that this effect measurably changes genetic diversity in rebounding populations, but is less visible when examining simulated viral loads or resistance mutations alone. Then, using this model as a null expectation for linkage patterns, we assess if the linkage structure in HIV populations treated with bNAbs is consistent with density-dependent recombination in vivo. Collectively, our work demonstrates that, by generating realistic null expectations of linkage under complex changes in population structure, we can employ linkage patterns as a powerful source of information for evaluating viral evolutionary hypotheses.