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.

Distinguishing peaks in the fingerprint region of an infrared spectra can be extremely difficult and is often ignored due to the large number of overlapping peaks from various molecular vibrations. The specific peak associated with the carbon sulfur bond within a thiocyanate ligand is located in the fingerprint region. To determine the spectral shift, two copper containing compounds were synthesized and isolated. The metal centers were coordinated to two varied thiocyanate complexes, one containing natural abundance sulfur and one with isotopically labeled sulfur. The individual vibrations were analyzed using a Fourier-Transform Infrared spectroscopy and the small chemical shift was identified within the fingerprint region a spectral subtraction was completed. The unit cell structure and space groups were found using X-ray crystallography.

Comparing the biochemical activity of Methylobacterium extorquens AM1 grown in separate medias with La3+ and Ca2+ as cofactors of methanol dehydrogenase (MDH). Recent studies have demonstrated that some enzymes in bacteria isolated from lanthanide-rich areas use lanthanides as metal cofactors in place of more common metals like calcium and that these lanthanide-enzymes have enhanced catalytic properties. The bioelectrocatalytic activity of MDH from M. extorquens grown in La3+ rich media is compared to MDH from M. extorquens grown in typical Ca2+ rich media. A coupled assay of phenazine methosulfate-dichlorophenolindophenol is performed to determine the enzyme activity. Different redox polymer films have been tested to determine the optimal film to immobilize the bacteria while still allowing bioelectrocatalysis to be performed. The bioelectrochemical activities from these bacteria have not previously been compared. If La3+ grown M. extorquens has higher bioelectrochemical activity than Ca2+ grown M. extorquens, then improved biofuel cells and sensors can be created.

Jamie E. Neumann, Bailee C. Higgins, and Jennifer A. DahlA series of organic-inorganic composite films composed of close-packed, alkanethiol-capped gold nanoparticles and rigid, aromatic dithiol crosslinking molecules were assembled upon the air-water interface within a Langmuir trough. The mechanical properties of films were assessed by the Langmuir isotherm, yielding measurements of minimum collapse pressures and structural responses to collapse. The results of this study address the individual roles of nanoscale materials components, further enabling the rational design of nanoarchitectures with specific chemical, physical, and mechanical properties.

Our research is focused on the synthesis of a bridged biphenyl molecule with an amino donor and tetraethylene glycol solubilizing groups (TEG). This three-state biphenyl molecule, due to its chemical properties, will find applications as nanoscale fluorescent sensors and molecular mechanical devices. Biphenyl molecules have known dihedral angles, leading to differing optical and conducting properties when manipulated. Utilizing a lactone-bridge, we can force the molecule into and out of planarity. At low pH, the molecule takes a planar conformation (“ON”), while at high pH it's non-planar (“OFF”). Previous research has shown similar two-state molecules’ effectiveness at readily switching conformations when exposed to different chemical environments. Prior research combined cyano and nitro acceptors with differing amino donors within biphenyl molecules to enhance optical properties and pH sensitivity. This pH sensitivity will be more precise with the addition of a third “OFF” state. At low pH, the amino group should become protonated, leading to the second “OFF” state and giving a narrow “ON” state. The “ON” state results in visible color differences from the “OFF” states of the molecule. These characteristics improve the usefulness of these molecules as pH sensors. Our research aims to synthesize a biphenyl molecule with a cyano acceptor, and TEGs. Prior research shows nitrile fluoresces better than its nitro counterpart. Long TEGs will increase the solubility of the molecule, enhancing the practicality of the planar biphenyl molecule as a dye. We have successfully synthesized one of our target molecules, a benzene ring with an iodine and a para-TEG group. We will be continuing our work to synthesize a three-state donor-acceptor biaryl lactone molecular switch with a cyano acceptor and TEGs, enhancing solubility and fluorescence.

Ozone is an important aspect of characterizing local air quality due to its effects as a respiratory irritant, causing inflammation of the respiratory system, coughing, exacerbation of asthma symptoms and can lead to respiratory infections over long term exposure. Formation of ozone in low altitude conditions comes as a byproduct of reactions between NOx emissions and VOC’s (Volatile Organic Compounds) in the presence of sunlight. This is of particular concern around heavy industrial areas as NOx emissions are a common byproduct of many industrial processes. The focus of the WiscoDISCO-22 field campaign was to characterize the local atmospheric conditions around the Lake Michigan shoreline in southeastern Wisconsin, with the specific focus of determining the lake breeze’s effect on local air mixing. Data was collected by flying unmanned aerial systems equipped with instruments to measure ozone concentration, and meteorological variables over both land and water. The current focus is on removing surface effects from the start of each flight and optimizing each data set to best showcase the vertical profile to be interpreted in continuing analysis of the different profiles seen between overland and overwater flights.

Residents on the shoreline of Lake Michigan in southwest Wisconsin are subject to air quality issues from high ozone concentrations near ground level. Meteorological data was collected for the August 2023 AGES+ campaign concerning ozone concentration, temperature, wind speed, and wind direction. Measurements were conducted using a DJI M300, with two IMETs and POM sensors attached, with flights occurring over Lake Michigan near the Chiwaukee Prairie area. Results were then correlated with the Wisconsin DNR’s ground station in Chiwaukee Prairie, which found moderate correlation of data between measurements conducted above water and on land.

Air quality is of concern to the communities along Lake Michigan’s shores in easternWisconsin. In the troposphere oxides of nitrogen, like NO2, react with volatile organic compounds, like formaldehyde, to form ozone,which is a harmful pollutant to human health. Lake Michigan traps these harmful chemicals in the troposphere, which results in ahigher-than-normal amount of ozone in these communities’ air. During the summer of 2023 in Kenosha, WI, the OPSIS DOASinstrument was placed on the Kenosha Municipal Building and the water treatment plant and took measurements of O3, NO2 and SO2 during the months of July and August. During this time, the AGES+ field campaign was also taking place, where ground, satellite, and aircraft observations were targeted around the Chicago, New York and Toronto regions. This data has been uploaded a repository that is part of the field campaign, AGES+. My poster will display the OPSIS DOAS data from the Kenosha, Wisconsin and data gathered from overwater aircraft sampling by University of Alabama Huntsville and perform an analysis to determine data correlations and find possible trends.

The development of sustainable routes to organic building blocks is a critical endeavor for reducing the environmental impact of traditional organic chemical synthesis. Biocatalysts offer an alternative method to facilitate sustainable synthesis, as they perform highly selective reactions at an increased rate. Ring-cleaving dioxygenases (RCDs) are a class of enzymes responsible for selectively breaking open the ring of benzene derivatives to provide a carbon source for microorganisms in bioremediation. To access the biocatalysts, many microbiology methods were utilized. The E. coli cells were transformed to contain the desired gene, the cells were then grown until there optical density was at the ideal value for cell viability (0.6-0.8) and induced with IPTG to facilitate protein expression. After heterologous expression, the enzyme was purified to homogeneity by immobilized metal affinity chromatography. We continue to analyze RCD types (type I, II, and III) through endpoint screening and product isolation using various substituted catechols. We envision that this approach to muconic acid synthesis will contribute to ongoing efforts to streamline synthesis of these important organic building blocks and reduce the usage of fossil fuels for organic synthesis.

Polyfluoroalkyl substances (PFASs), also known as “forever chemicals”, are man-made chemicals consisting of C-chains, either poly- or per-fluorinated. PFASs have long been detected in the environment, but recent studies found their presence in the human body too. Due to the stable C-F bonds, these compounds resist degradation, and the long-term effects and toxicity on the human body are still unknown. In humans, PFASs have been associated with xenobiotic metabolism, immunity, hepatic steatosis, kidney cancer, liver toxicity, and more. Moreover, PFASs have been detected in the human brain, and it’s hypothesized their potential interference with neurotransmitter synthesis and act as receptor-binding site competitors, potentially leading to cognitive and developmental dysfunctions. However, the mechanism by which these chemicals enter the brain and can cross or bypass the blood-brain barrier transporters is still unclear. Thus, in this study, we aim to conduct a systematic study of PFASs and their binding into some of the known target proteins using computational chemistry tools, including cheminformatics, quantum chemical computation, and molecular docking. The presentation will document the absolute hardnesses, absolute electronegativities, and binding affinities to some known targets in our body, including human serum albumin, liver fatty acid binding proteins, organic anion-transporting polypeptides, and dopamine transporter.

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.

This research looks at the effect of inert environments on the structures of pyridine-SO2 complexes. These effects were able to be observed both experimentally through low temperature FTIR as well as computational models. Experimental FTIR data illustrates environmental effects through measured vibrational frequency shifts between the fragments and the complex. Computational models provide detailed structural information as well as predicted frequencies that can be compared to experimental data. At this point we have observed the spectra of both pyridine-SO2 and 3,5-Difluoropyridine-SO2 in solid Ne at 6K, and we note no difference between most of these data and the predicted values. The exception is the SO2 asymmetric stretching vibration, and the discrepancy here may indicate a solvent effect on the structure, or a failure of the theory to accurately predict the gas-phase structure. In the 3,5-Difuoropyride-SO2 spectra, this peak is observed at a slightly higher frequency, consistent with a weaker interaction upon addition of the fluorenes. Collection and analysis of spectra in solid N2 are in progress.

This project is concerned with solvent effects on the structural properties of nitrogen-donor-SO2 complexes, including: H3N-SO2, and its methylated (CH3 containing) analogues. The goal is to assess the extent to which inert, condensed-phase environments (solid neon, argon, and nitrogen) induce structural change in these systems. Experimentally, we utilize infrared spectroscopy to observe shifts in key vibrational modes that parallel the compression of the N-S bond. Theoretically, we use quantum-chemical calculations (computer simulations of the bonding) to predict gas-phase structural properties, bond energies, vibrational frequencies as well as the energy profile along the N-S bond. Our preliminary computational results indicate that these complexes will undergo significant structural changes. A great deal of effort went into identifying the optimal computational methods to make this determination. The first consideration was identifying which among twelve methods tested best predicated the experimental frequencies of SO2, and using this method, we will report gas-phase and structures and predicted frequencies for H3N-SO2, CH3H2N-SO2, (CH3)2HN-SO2 and (CH3)3N-SO2. Collectively these complexes span a great range of interaction strengths, specifically: H3N-SO2: –6.6 kcal/mol (RNS=2.685 Å), CH3H2N-SO2: -8.4 kcal/mol (RNS=2.509 Å), (CH3)2H2N-SO2: -11.0 kcal/mol (RNS=2.334 Å), (CH3)3H2N-SO2: -13.4 kcal/mol (RNS=2.302 Å). We also explored the performance of various computational methods for energetic results by comparing them to a very high-level model and also compared predicted frequencies to those previously measured in solid nitrogen.

Depression affects millions worldwide, with the effectiveness of antidepressants often linked to interactions between medication and neuroreceptors. Laboratory testing and clinical studies of these interactions are extremely expensive and time consuming, which makes the computational approach an increasingly important step for drug discovery. The relationship between frontier molecular orbitals (HOMO-LUMO) and ligand-protein interactions is not well understood. In the present study, the interactions of neurotransmitters and antidepressants with several neuroreceptors are thoroughly examined using classical mechanical, quantum chemical, and machine learning methods. The quantum chemistry-based computations use electronic structure theory to compute molecular orbitals, while the machine learning architecture utilizes a large volume of pre-computed data and a deep learning architecture involving graph convolutional neural networks resulting in a faster and accurate prediction of molecular properties. The binding of these molecules has been simulated in selected neuroreceptors using docking and classical physics-based calculations. Our initial results suggest correlations between molecular hardness, substructures, HOMO-LUMO energy gaps, and binding affinity, potentially offering a cost-effective approach to predict drug-receptor interactions and accelerate antidepressant development.

Enoate reductase from Bacillus coagulans (ERBC) is a promising biocatalyst that has been shown to reduce the carbon-carbon double bonds of cis,cis-muconic acid in vivo, generating adipic acid, an important precursor used in the synthesis nylon-6,6. Our research has shown that ERBC is capable of reducing carbon-carbon double bonds in a variety of molecules produced using the extradiol dioxygenase BphC. Since the native substrate of ERBC is unknown, studying its activity with a variety of similar substrates will be beneficial for evaluating the scope of its reactivity. Our research aims to identify promising substrates using UV-visible light spectroscopy and to characterize enzymatic products through high performance liquid chromatography (HPLC) analysis. Identification of possible substrates and subsequent engineering and enhancement of the catalytic activity of ERBC can enable the development of environmentally benign synthetic methods for the production of a variety of commodity chemicals.

Dichloromethane (DCM) is an organic solvent with complimentary properties such as low boiling point, slight polarity, and efficiency in dissolving organic molecules that have resulted in DCM being used in many chemical industries with various important applications in synthesis and purification. New restrictions of DCM use are taking effect May 2025, that aim to minimize the risk to human and environmental health via controlled exposure limits. In our undergraduate organic synthesis research, DCM is used in separations during the purification process and as a reaction solvent, therefore it is ideal to find viable alternatives to DCM to have an informed decision on when to opt into using DCM. Alternative solvents and mixtures that possess similar properties regarding solubility and volatility have been identified by the American Chemical Society and career chemists, but each reaction needs to be independently optimized. Diethyl ether, ethyl acetate/ethanol (3:1), and dioxane will be tested as both reaction solvents and purification solvents for several organic synthesis reactions and evaluated based on purity and percent yield for viability as replacement for DCM use. Spectroscopic methods including 1H-NMR and FT-IR will be used to compare the effectiveness of the chosen alternatives.

Biomolecular condensates (BMCs) are naturally occurring membraneless organelles formed through liquid-liquid phase separation (LLPS). They play significant roles in various cellular processes, including signal transduction, gene expression, and stress response. The thermodynamics of condensate formation involve a complex interplay between entropy and enthalpy. The loss in entropy due to ordered assembly formation inside the liquid-like condensate is compensated by the increase in intermolecular interaction enthalpy. The main factors that promote LLPS include changes in biomolecule concentration and intermolecular interactions. The LLPS process is sensitive to pH, temperature, and ionic strength. LLPS of intrinsically disordered proteins (IDPs) and unstructured domains/regions of proteins (IDPRs) are well documented. Recent studies suggest that globular proteins also form crowder-induced biomolecular condensates. However, the precise role of molecular crowding in LLPS-driven biomolecular condensate formation remains understudied, especially for globular proteins. A thorough understanding of the molecular mechanism of protein condensate formation and the impact of phase separation on enzymatic reactions is crucial to addressing issues related to cellular physiology. To investigate the molecular mechanism of condensate formation in crowded environments, globular proteins such as bovine serum albumin and prolyl-tRNA synthetase are being used alongside synthetic crowders like polyethylene glycols of variable sizes. Additionally, variable salt concentrations are employed to understand the effects of multivalent interactions in BMC formation. We will present the preliminary results of different globular protein samples with and without polyethylene glycol (PEG) as crowding agents at high and low salt concentrations.

Cattle that eat the same feed and come from the same environment can emit methane (CH4), a potent greenhouse gas, at vastly different levels. An estimated 32% of anthropogenic CH4 can be traced to ‘enteric fermentation’ in livestock production. During enteric fermentation, specialized microorganisms will digest complex plant fiber to create compounds like acetate and hydrogen (H2). Some of these organisms, called methanogens, will consume and use these products to produce CH4. Emerging data suggests natural inter-animal variation in CH4 emissions could derive from host genetics or differences in rumen microbial digestion. Here, we will analyze 16S rRNA gene amplicon sequencing from rumen microbiomes to look for differences in the structure and composition of microbial communities from the rumen of twenty beef cattle of varying CH4 emission levels. There is no significant difference in microbial community diversity by CH4 emission level. We will analyze microbial community structure and composition to identify microbial taxa associated with high and low CH4 emissions. The findings of our work will begin to explain why some cattle emit higher methane levels compared to others, and may aid in finding solutions to reduce methane emissions in cattle while keeping their feeding efficiency and meat production high.

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.

Extradiol dioxygenases are known to oxidatively cleave aromatic pollutants, such as catechol. DfdB is an extradiol dioxygenase whose activity on substituted catechols has not been studied. Catechols and other aromatic hydrocarbons are a by-product of coal conversion, coal tar chemical production and other coal industries and are found in the air and wastewater surrounding these facilities. As catechol substrates are possible human carcinogens, their potential breakdown by DfdB is a significant area of interest. Ultimately, this research aims to define the conditions under which DfdB breaks down catechol substrates most efficiently and characterize the products of this bioremediation pathway. To accomplish this goal, the enzyme kinetics of DfdB were measured with varying concentrations of catechol substrates using Ultraviolet-Visible (UV-vis) Spectroscopy, and initial rates of reaction were calculated. Upon analysis, the data suggests that DfdB experiences concentration-dependent substrate inhibition, which has been noted for other extradiol dioxygenases. By measuring kinetic profiles for a variety of substituted catechols, we have better defined the characteristics of DfdB as a potential bioremediation catalyst. This information will be leveraged to improve the utility of this catalyst broadly for synthesis and bioremediation.

The cell of an organism is a complex environment filled with molecules, which are essential for survival. To fully understand the cellular environment, it is imperative to have powerful techniques that can be used to analyze molecular interactions. Spectroscopy, which is the study of the interaction of light with matter, is one of the primary ways to analyze molecules within a sample. Two spectroscopic techniques that are especially useful in identifying properties of molecules are Fluorescence Spectroscopy and Raman Spectroscopy. While both techniques analyze light interactions, each provides different types of information. Fluorescence Spectroscopy is useful in identifying structural changes in a molecule due to any perturbations in the surrounding area. Raman Spectroscopy identifies the molecules present within a sample, generating a spectral fingerprint of the sample and allowing us to view how a sample changes in composition over time. We are currently using Fluorescence Spectroscopy to analyze how molecular crowding impacts protein function. Furthermore, we are using Raman Spectroscopy to analyze the difference between saliva samples of healthy individuals and saliva samples of individuals affected by breast or lung cancer.

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.