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.

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.