Life Sciences – Page 4 – UROP Spring Symposium 2021

Life Sciences

Brain Networks for Fear Learning in Infant Rats

In this lab, we are conducting experiments to seek a better understanding about the correlation between behavior and the brain structure. More specifically, we are using fear conditioning and analyzing certain areas of the brain to see this correlation. This study is able to give insight by using infant rats and looking into how the brain network works when they are encountered by a “threatening stimuli.” Through the process of immunohistochemistry, we are able to examine slices of the brain and use specific proteins to highlight neurons that are associated with the behavior that occurs during the fear learning process. The desired section of the brain that is known to be associated with learning is called the amygdala, which is heavily analyzed in our study. This is important for us to understand how fear impacts our brain networks and can possibly reveal limitations that can be further explored (White).

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Brain Networks for Fear Learning in Infant Rats

In this lab, we are conducting experiments to seek a better understanding about the correlation between behavior and the brain structure. More specifically, we are using fear conditioning and analyzing certain areas of the brain to see this correlation. This study is able to give insight by using infant rats and looking into how the brain network works when they are encountered by a “threatening stimuli.” Through the process of immunohistochemistry, we are able to examine slices of the brain and use specific proteins to highlight neurons that are associated with the behavior that occurs during the fear learning process. The desired section of the brain that is known to be associated with learning is called the amygdala, which is heavily analyzed in our study. This is important for us to understand how fear impacts our brain networks and can possibly reveal limitations that can be further explored (White).

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Multimodal Retinal Imaging of Usher Syndrome Animal Model of Photoreceptor Degeneration

Ashley Brown Pronouns: she/her/hers Research Mentor(s): Yannis Paulus, Assistant Professor, Tenure Track Research Mentor School/College/Department: Ophthalmology and Visual Sciences, Michigan Medicine Presentation Date: Thursday, April 22, 2021 Session: Session 5 (3pm-3:50pm) Breakout Room: Room 12 Presenter: 5 Event Link Abstract For privacy concerns this abstract cannot be published at this time. Authors: Ashley Brown, Emilie

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Multimodal Retinal Imaging of Usher Syndrome Animal Model of Photoreceptor Degeneration

Emilie Gilligan Pronouns: she/her Research Mentor(s): Yannis Paulus, Assistant Professor, Tenure Track Research Mentor School/College/Department: Ophthalmology and Visual Sciences, Michigan Medicine Presentation Date: Thursday, April 22, 2021 Session: Session 5 (3pm-3:50pm) Breakout Room: Room 12 Presenter: 5 Event Link Abstract For privacy concerns this abstract cannot be published at this time. Authors: Emilie Gilligan, Ashley

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Golgi biogenesis, function, and deffects in diseases

It has been observed that the Golgi apparatus is involved in signaling and in cell adhesion and migration. Additionally, the unique Golgi stacking structure is an essential aspect of Golgi function. Our lab has demonstrated that GRASP55 and GRASP65 are required proteins for Golgi stacking. It has also been shown that integrin a5ß1 is downregulated in GRASP KO cells and that integrin a5ß1 is involved in Epithelial-to-mesenchymal transition (EMT). Using this information, our lab is working to find a possible mechanism that shows that Golgi stacking formation is required in EMT.

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Modeling Molecular Pathogenesis of Idiopathic Pulmonary Fibrosis associated Lung Cancer (IPF-LC) in mice

Lung diseases are prolific killers in the United States each year. Of the major respiratory illnesses and cancers, lung cancer (LC) and idiopathic pulmonary fibrosis (IPF) are especially notable. In the US alone, there is estimated to be 250,000 new cases of lung cancer diagnosed annually, and around 130,000 patients die from the disease as well (Cancer.org). Meanwhile, IPF affects close to 200,000 people in the United States with a 3-5 year survival rate of 50% (National Heart, Lung, and Blood Institute). For each of the two diseases, treatment options are very different, making it especially difficult to provide sufficient care. In addition, around 22% of IPF patients will go on further to develop non-small cell lung carcinoma (NSCLC), or lung cancer (The Lancet). While it is not completely clear as to why this happens, it is likely due to increased inflammation in the lungs, providing an optimal ground for tumor growth.

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Development of protein property prediction methods from sequence based on deep learning

Although proteins have become increasingly easier to sequence, experimental determination of a protein’s structure remains difficult and time-consuming. Therefore, the prediction of a protein’s structure and properties based on its sequence is a key challenge in making better use of the vast amount of sequencing data. Our project seeks to develop a deep-learning-based method that uses a protein’s sequence to predict its properties, such as phi/psi angles and solvent accessibility. The initial goal of the project was to design and write the deep-learning program using the PyTorch library. After completion of the program, we assembled a training and testing set based on existing data from the Protein Data Bank and used the training data to train the model. We then ran the testing dataset and analyzed the results by comparing the predicted properties to the experimentally determined ones. While we do not have any results yet, we hope to be able to make conclusions about the relative effectiveness of the model we design compared to existing models for prediction. The results we obtain could help us determine which prediction techniques or algorithms are well-suited to this task, or which ones lead to errors and thus may need to be avoided in future research. The results could also contribute to improving the accuracy and efficiency of computational protein structure prediction, allowing scientists to make better use of the available sequencing data without the difficulties of experimental determination.

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Altered Kynurenine Pathway in Aortic Patients is Linked to CKD Atherosclerosis

Skyelar Herriman Pronouns: she/her/hers Research Mentor(s): Anna Mathew, Assistant Professor Research Mentor School/College/Department: Internal Medicine/ Nephrology, Michigan Medicine Presentation Date: Thursday, April 22, 2021 Session: Session 5 (3pm-3:50pm) Breakout Room: Room 11 Presenter: 5 Event Link Abstract For privacy concerns this abstract cannot be published at this time. Authors: Skyelar Herriman, Julian Meza, Biaoxin Chai,

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Mimicking the Architecture and Modulus of Native Brain Tissue onto Neural Implants to Improve Biocompatibility

The objective of this project is to increase longevity of microelectrodes, identify biomarkers to isolate the cause of neuroinflammation, and to analyze large banks of collected data using machine learning Matlab scripts. The data used for this project is collected from two groups of mice, wild type and CD14 knockout (mice with the CD14 gene repressed). Surgeries were conducted on both groups and data was collected two weeks after. Matlab scripts utilized machine learning to analyze the data and isolate patterns of unusual fold changes compared to set upper and lower standards (ie. 0.01 and 1). The scripts utilized to identify biomarkers are unique to this lab and the theory behind them have broad possible applications for other data analysis based on an initial condition to separate data with specific trends. Specific to our research, we find patterns in the up and down regulated genes and compare the mean fold change between different data groups. This project is a continuous work in progress – our goal is to continue to further narrow down the gene targets and identify more specific biomarkers to better target with therapeutic drugs. With successful identification, we hope to be able to decrease the inflammatory reaction due to insertion and increase the efficiency of intracortical microelectrodes. Developing a way to stop or mitigate the inflammatory response would increase the lifespan of devices that rely on the recording capabilities of microelectrodes. From prosthetic devices to increasing the understanding of the human brain, a decrease in the inflammatory response creates a longer period of time for a stable signal, meaning more opportunities for microelectrode applications.

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Bioinformatics and Biochemical studies on cancer proteins

Protein sequence data is abundant, but ancestry information of those proteins and experimental analysis of the structure of those protein sequences are less available, and costly to produce. Thus, machine learning algorithms are being developed to predict protein structure. For structure prediction, available data is collected and parsed for the specific properties one’s algorithm requires, such as torsion angles for tertiary structure prediction. After an algorithm is trained against this data, the algorithm can be tested in its correctness of predicting protein structure. As a new algorithm is developed, an increase in accuracy and precision is expected.

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