UWEC CERCA 2025

Grazing steers partner with their rumen microbiomes to efficiently convert plant-derived carbohydrates into meat. Considering the socioeconomic importance of the beef industry, it is critical to develop strategies that maintain quality while lessening negative environmental impacts. Diet supplementation and hormonal implants have been shown to variably impact methane emissions and animal performance. The response of the rumen microbiome to such treatments remains unknown. Here, we will analyze 16S rRNA gene amplicon sequencing of the rumen microbiome from grazing steers across four treatment groups: diet supplemented, hormonal implanted, combined diet and implant, and no intervention. The diet, implant, and combined treatment showed no significant impact on methane emission or N excretion over the 90-day grazing trial. Given this lack of difference, we hypothesize the rumen microbial communities will not be different across treatments. However, we hypothesize the 90 days of grazing will significantly alter the rumen microbiome. Results from this study will provide insight into rumen microbiome dynamics during the life cycle of a grazing steer, further informing management strategies.

Understanding how proteins and ligands interact is essential for drug discovery, especially for prolyl-tRNA synthetase (ProRS), which is responsible for attaching proline to the corresponding tRNA molecule, a key step in protein biosynthesis in all living organisms. Thus, species-specific inhibitor design for this target holds a key promise in the development of antibiotics with minimal side effects. In the current study, the binding affinities of ligands as well as protein-ligand interactions have been studied for several ProRSs across different host species. Both the physics-based and machine learning models have been utilized, as the latter group of models are computationally inexpensive. The classical physics-based model predicts the affinities by combining the hydrogen bonding, electrostatic, van der Waals, and implicit solvation, while the machine learning model utilizes a deep learning architecture through graph convolutional neural network stitched to artificial neural network. The latter approach enables a faster and more scalable screening of potential drug candidates. Results obtained from the screening method will be compared against a physics-based simulation of molecular interactions and their corresponding binding affinities for the various ProRS enzymes. This research has the potential to enhance drug discovery by improving the speed and scalability of molecular interaction predictions.

Polyethylene glycol (PEG) is a flexible, non-toxic polymer. It is considered biologically inert and has numerous applications in medicine and industry. PEG is often attached to drug molecules in a process called PEGylation to enhance their stability and solubility, decrease the immune response, and increase circulation time throughout the body. Recently, PEGylated lipids have been included as an ingredient in COVID-19 vaccines. Additionally, PEG molecules of variable sizes are commonly used for studying the effects of molecular crowding and confinement on the conformation and function of proteins and nucleic acids. Despite being considered biologically inert, recent studies have shown that PEG interacts with biomolecules such as proteins. To gain a deeper understanding of PEG-protein interactions, we are using Raman Spectroscopy to investigate the effect of PEG of variable sizes on the vibrational modes of amino acids and proteins. This vibrational spectroscopic technique identifies unique fingerprints of molecules based on the inelastic scattering of monochromatic light. We will present the preliminary results of our study.