Cracking the Cardiovagal Code: Novel Molecular Targets for Heart Disease Treatment
Maira Jalil, Biology, Laboratory of John Campbell, PhD
Sarah Goggin, Biomedical Engineering, Laboratory of Eli Zunder, PhD
Neurons of the parasympathetic nervous system generate cardiovagal tone and regulate resting heart rate. Our project aims to identify conserved markers and therapeutic targets for cardiovagal neurons in mice and humans. Eventually, this will help in increasing cardiovagal tone in humans and will improve clinical outcomes from cardiovascular diseases, including arrhythmia and hypertension.
Characterizing the regulatory role of TET2 in peritoneal cavity B-1 cell subtypes
Emily Dennis, Laboratory of Coleen McNamara, School of Medicine
Maria Murach, Laboratory of Stefan Bekiranov, Biochemistry and Molecular Genetics
Emily Dennis of the McNamara Lab and Maria Murach of the Bekiranov Lab are interested in discovering the novel role TET2 plays in regulating B-1 cell immunoglobulin production and its potential for contributing to precision medicine in treatment of atherosclerosis. They are utilizing epigenetic and transcriptomic tools to assess differential gene expression, methylation, and BCR sequence clonality as well as measuring immune cell population changes and immunoglobulin production via flow cytometry and ELISAs. They plan to study if loss of TET2 in B-1 cells reduces atherogenesis, which would indicate that TET2 CHIP should be treated on a cell-specific basis with regards to preventing the acceleration of atherosclerosis. They are grateful for the funding and support from iPRIME.
Machine learning tool for lineage tracing and phenotypic prediction for personalized therapeutics for atherosclerosis
Anita Salamon, Laboratory of Gary Owens, Molecular Physiology and Biological Physics
Victoria Milosek, Laboratory of Gary Owens, Molecular Physiology and Biological Physics
This project aims to develop a deep learning model that predicts the origin and trajectory of smooth muscle cells (SMC) within atherosclerotic plaques to promote plaque stability in patients at high risk of myocardial infarction and stroke. A stable atherosclerotic plaque is characterized by a thick, SMC-rich fibrous cap. These SMC undergo phenotypic transitions, downregulating classic SMC markers such as MYH11 and ACTA2 while markers of other phenotypic states are upregulated. By leveraging large transcriptomics data and SMC-lineage tracing technology from our in vitro and in vivo models, we will create a neural network capable of identifying unique transcriptional signatures of SMC phenotypic states. This model will then be applied to predict cell state and origin in unannotated single-cell RNA-sequencing data from human atherosclerotic lesions, ultimately leading to personalized treatments for advanced atherosclerosis.