Over 40% of adults in the United States are considered obese. Obesity is known to cause abnormal metabolic effects and lead to other negative health consequences. Interestingly, differences in metabolism and contractile performance between obese and healthy weight individuals are associated with differences in skeletal muscle fiber type composition between these groups. Each fiber type is characterized by unique metabolic and contractile properties, which are largely determined by the myosin heavy chain isoform (MHC) or isoform combination that the fiber expresses. In previous studies, SDS-PAGE single fiber analysis has been utilized as a method to determine MHC isoform distribution and single fiber type distribution in skeletal muscle. Herein, a methodological approach to analyze MHC isoform and fiber type distribution in skeletal muscle was fine-tuned for use in human and rodent studies. In the future, this revised methodology will be implemented to evaluate the effects of obesity and exercise on the phenotypic fiber type composition of skeletal muscle.

Since 1975, the prevalence of obesity has nearly tripled around the world. In 2016, 39% of adults, or 1.9 billion people, were considered overweight, and 13% of adults, or 650 million people, were considered obese. Furthermore, Cardiovascular disease remains to be the leading cause of death for adults in the United States, with 655,000 people dying from related conditions and consequences each year. Including fiber in one’s dietary regimen has been shown to greatly improve health outcomes in regards to these two areas of health. However, not much literature is available on the effects of corn-based fiber, especially detailing the individual components of the grain itself. The purpose of this preliminary study was to test the differences in influence on both LDL-cholesterol and triglycerides between treatments based on whole-grain corn flour, refined corn flour, and 50% refined corn flour + 50% corn bran derived from whole grain cornmeal (excellent fiber) in healthy overweight (BMI ≥ 25.0 kg/m2) adults (ages 18 - 70) with high LDL cholesterol (LDL ≥ 120mg/dL). 20 participants, ages 18 - 64 (10 males, 10 females) were involved. Data was derived from blood draws taken before and after each of the three treatments as well as before and after each treatment’s wash out periods. A general linear model was used to assess the effect of corn products on circulating concentrations of LDL-cholesterol and triglycerides. From the model, it was found that the whole-grain corn flour and the 50% refined corn flour + 50% corn bran drive from whole grain cornmeal treatments produced a higher, similar benefit in reductions in LDL-cholesterol. However, the whole grain flour, refined flour, and bran-based fiber treatments did not influence the triglyceride levels of the participants throughout this study. Further research is needed to elucidate the effects of these fiber items on cardiometabolic disease markers in the long-term as well as with a larger sample size.

Lyme disease is a common tick-borne illness caused by the Gram-negative bacterium Borrelia burgdorferi. An outer membrane protein of Borrelia burgdorferi, P66, has been suggested as a possible target for Lyme disease treatments. However, a lack of structural information available for P66 has hindered attempts to design medications to target the protein. Therefore, this study attempted to find methods for expressing and purifying P66 in quantities that can be used for structural studies. It was found that by using the PelB signal sequence, His-tagged P66 could be directed to the outer membrane of Escherichia coli, as confirmed by an anti-His Western blot. Further attempts to optimize P66 expression in the outer membrane were made, pending verification via Western blotting. The ability to direct P66 to the outer membrane using the PelB signal sequence is a promising first step in determining the overall structure of P66, but further work is needed before P66 is ready for large-scale purification for structural studies.

Seven human subjects with body mass indices (BMIs) ranging from 19.4 kg/ m2 to 26.7 kg/ m2 and six human subjects with BMIs ranging from 32.1 kg/ m2 to 37.6 kg/ m2 were recruited and subjected to 45-minute bouts of acute exercise to look at the changes in the plasma concentration of the dopamine metabolite homovanillic acid (HVA) in response to acute physical activity. Plasma HVA concentration was measured before exercise and during the last 10 minutes of the exercise bout via competitive ELISA. On average the optical density (OD) of the samples taken from lean subjects decreased from 0.203 before exercise to 0.192 during exercise, indicating increased plasma HVA concentration. In subjects with obesity OD increased from 0.210 before exercise to 0.219 during exercise, indicating reduced plasma HVA concentration. These differences in OD were not statistically significant. Between the lean group and the group with obesity no significant difference was observed between the OD of the plasma samples taken before exercise, but a significant difference (p = 0.0209) was observed between the ODs of the samples taken after exercise. This indicated that there was a significant difference between the percent changes in OD between the lean group and the group with obesity, which suggested that there may be a body weight-dependent difference in the amount of dopamine released in response to exercise. Because of the lack of significance in the changes in OD within the lean group and the group with obesity the results of this study were insufficient to conclude that this difference is not due to chance, but further investigation is warranted.

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.

Apolipoprotein (ApoE) plays an important role in the transport of lipids in the brain for normal functioning. There are three different isoforms of ApoE which are coded for by three alleles (E2, E3, E4). Patients carrying at least one copy of ApoE E4 are known to be at higher risk for developing Alzheimer’s disease (AD) and earlier onset of symptoms. This is due to the buildup of amyloid plaques and neurofibrillary tangles of the brain from the accumulation of tau proteins, which are associated with the progression of Alzheimer’s disease. However, findings on ApoE E2 have shown that it may be a protective allele since it is linked to a decreased risk of formation of amyloid plaques and neurofibrillary tangles. To study this phenomenon within the context of a local population group, polymerase chain reaction and gel electrophoresis were conducted on extracted DNA samples. The principal goal in this research study was to genotype ApoE variants using single nucleotide polymorphism (SNP) specific primers, and polymerase chain reaction to analyze the frequency in the Tempe population to determine future healthcare needs.