Jaclyn Bermudez, a senior biology major, won in the Biochemistry Scientific Discipline; Tracy Gonzalez, a senior biology and math major, won in the Interdisciplinary Sciences Discipline, and Tony Vega, a junior physics major, won in both Cancer Biology and Interdisciplinary Sciences Disciplines. A total of 225 awards were given out of a more than 1,500 abstracts that were submitted.
ABRCMS is the largest professional conference for students interested in the fields of biomedical and behavioral research. One of the main goals of the conference is to encourage underrepresented minority students to pursue advanced training in the biomedical and behavioral sciences.
Below are summations of the students’ research:
Title: Binding of PH Domains to Phosphatidylinositols and Small GTPases
Researchers: Jaclyn Bermudez; Paul Sternweis, Ph.D.; and Stephen Gutowski, University of Texas Southwestern Medical Center, Dallas
Abstract: Pleckstrin homology (PH) domains are found in many cell signaling proteins that are important for cell survival and function. They are often found in tandem with the Dbl homology (DH) domain in proteins such as Dbl’s Big Sister (Dbs). Several PH domains have been shown to bind phosphatidylnositides (PI); the first identified interaction was with phosphatidylinositol 4,5 bisphosphate (PIP2). More recently, some of these PH domains have been shown to also associate with small monomeric GTPases bound to GTP. It is not known if the PH domains can bind to phosphatidylinositols and small GTPases simultaneously or what effect such binding would have on the downstream events. We hypothesize that PH domains can interact with phosphatidylinositols and small GTPases at the same time in a synergistic manner. We demonstrate an approach to test this question directly through association of PH domains with small GTPases and phosphatidylinositols in artificial phospholipid vesicles. In our study we were able to make artificial phospholipid vesicles containing phosphatidylnositides (which allowed the binding of the Dbs DH-PH region) and nickel head groups (which allowed the binding of the small GTPase Rac1 with a histidine tag). We found that artificial vesicles made with nickel head groups and no phosphatidylnositides can bind small GTPase Rac1 and artificial vesicles with phosphatidylnositides and no nickel can bind the Dbs DH-PH region. Though the Dbs DH-PH region and Rac1 were present in our formed vesicles with both nickel and phosphatidylnositides, it has yet to be determined if the Dbs DH-PH region was binding to both the phosphatidylnositides and the small GTPases at the same time.
Title: My BIG Fat Math Model: Beta-Cell Compensation and Type 2 Diabetes.
Researchers: Tracy Gonzalez; Rosalia Zarate, Univeristy of California, Santa Barbara; Javier Baez; Anarina Murillo, Arizona State University; Danielle Toupo, Cornell University
Abstract: Type 2 diabetes (T2D) is characterized by the progressive decline of beta-cell function and terminal insulin resistance. T2D is understood to be incurable although a change in diet and regular physical activity can reduce symptoms and prevent the onset of T2D in pre-diabetic patients. The initiation of insulin resistance and beta-cell compensation defines the pre-diabetic stage of T2D. Progression of beta-cell dysfunction and failure mark the clinical onset of T2D. The possibility of recovering from T2D in the pre-diabetic and diabetic stages through diet interventions is of current interest to many researchers today. We mathematically explore the biological consequences of the effect of over-nutrition, fat accumulation and degradation, and beta-cell function in T2D. We focus on the effects of fat (adipose tissue on the liver and free fatty acids in the blood) and the mechanism underlying the initiation and progression of beta-cell failure. This work is based on a previous model that considers glucose, insulin, and beta-cell mass dynamics. We extended the model to incorporate fat and its explicit effect on insulin sensitivity and on beta-cell sensitivity. More specifically, we modeled fat as having an inverse effect on insulin sensitivity with a Hill function and beta-cell sensitivity with a logistic function. We assumed that beta-cell sensitivity embodies a logistic response by initially increasing as fat accumulates due to the compensatory response triggered by increased glucose levels. Then as fat continues to accumulate, beta-cell sensitivity decreases due to beta-cell dysfunction and death. The threshold at which beta-cell compensation fails marks the clinical onset of T2D, which with time can progress to the stage where it is no longer reversible due to severe loss of beta-cell mass. Using stability analysis and bifurcation theory, we analyzed the various stages of T2D, investigated whether weight loss and/or diet in the pre-diabetic and diabetic stages would reverse T2D, and studied when certain treatment strategy are no longer effective. The bifurcation diagrams produced by our model were able to show that when the initial beta-cell mass is beneath an unstable manifold beta-cell mass will decrease and overtime cause an individual to converge to a pathological state. This means that if beta-cell mass is already low, then beta-cell mass will decrease even if fat is not accumulated in the liver. Because this model gives more accurate fixed points while incorporating physiologically relevant fat dynamics at the pathological equilibrium point, our results indicate that the most important aspect in possibly reversing an individual’s T2D depends upon beta-cell adaptation driven by both insulin resistance and beta-cell sensitivity, which in turn are driven by fat accumulation in the liver.
Title: Ineffectiveness of Correlation Coefficient in Determination of Image Registration Accuracy
Researchers: Anthony Vega; Thomas Guerrero, M.D./Ph.D.; Edward Castillo, Ph.D.; and Richard Castillo, The University of Texas M.D Anderson Cancer Center, Houston
Abstract: A method of image manipulation to demonstrate the ineffectiveness of the correlation coefficient in image registration has been developed. The aforementioned method uses a sorting pattern to manipulate pixels and reconstruct large three-dimensional images. The reconstructed image would appear as an identical replication of the previous image to the correlation coefficient, but would register a grossly significant error to a spatial accuracy test. The purpose of the experiment was to prove that the method of a correlation coefficient is inadequate to exclusively uphold or diminish the validity of any image registration method. The following study sought to implement this method in the image registration area of four dimensional computed tomography (4DCT) lung images, which are used for disease diagnosis. The sorting function relies primarily on two focal points in the method. The first is a simple algorithm, which reads each pixel in an image and creates a mapping matrix similar to optical flow, based on the equal weighting of pixel intensity and distance to “move” a pixel to the furthest and most similar position. The second is the portion, which creates the estimated image by reading in the mapping matrix and carrying out the transformation. After the latest version of the program had been developed, five two dimensional trials were recorded to have an average correlation of 0.997, within 1% of the targeted result, and a high spatial accuracy error equal to half the size of the image. The average correlation and the spatial accuracy error yielded by the method met the optimum criteria initially expected for the experiment. Further work is currently being done to build a faster version of the method so that a relatively inexpensive and fast three-dimensional test can be implemented.