Alterations in redox and energy metabolism in Ras-transformed cells: Mechanisms and therapeutic implications

Increasing attention has been given to the connection between metabolism and cancer. Under aerobic conditions, normal cells predominantly use oxidative phosphorylation for ATP generation. In contrast, increase of glycolytic activity has been observed in various tumor cells, which is known as Warburg effect. Cancer cells, compared to normal cells, produce high levels of Reactive Oxygen Species (ROS) and hence are constantly under oxidative stress. Increase of oxidative stress and glycolytic activity in cancer cells represent major biochemical alterations associated with malignant transformation. Despite prevalent upregulation of ROS production and glycolytic activity observed in various cancer cells, underlying mechanisms still remain to be defined. Oncogenic signals including Ras has been linked to regulation of energy metabolism and ROS production. Current study was initiated to investigate the mechanism by which Ras oncogenic signal regulates cellular metabolism and redox status. A doxycycline inducible gene expression system with oncogenic K-ras transfection was constructed to assess the role played by Ras activation in any given studied parameters. Data obtained here reveals that K-ras activation directly caused mitochondrial dysfunction and ROS generation, which appeared to be mechanistically associated with translocation of K-ras to mitochondria and the opening of the mitochondrial permeability transition pore. K-ras induced mitochondrial dysfunction led to upregulation of glycolysis and constitutive activation of ROS-generating NAD(P)H Oxidase (NOX). Increased oxidative stress, upregulation of glycolytic activity, and constitutive activated NOX were also observed in the pancreatic K-ras transformed cancer cells compared to their normal counterparts. Compared to non-transformed cells, the pancreatic K-ras transformed cancer cells with activated NOX exhibited higher sensitivity to capsaicin, a natural compound that appeared to target NOX and cause preferential accumulation of oxidative stress in K-ras transformed cells. Taken together, these findings shed new light on the role played by Ras in the road to cancer in the context of oxidative stress and metabolic alteration. The mechanistic relationship between K-ras oncogenic signals and metabolic alteration in cancer will help to identify potential molecular targets such as NAD(P)H Oxidase and glycolytic pathway for therapeutic intervention of cancer development. ^

Genetic variability and the hypothalamic control of energy balance

Background. Obesity is a major health problem throughout the industrialized world. Despite numerous attempts to curtail the rapid growth of obesity, its incidence continues to rise. Therefore, it is crucial to better understand the etiology of obesity beyond the concept of energy balance.^ Aims. The first aim of this study was to first investigate the relationship between eating behaviors and body size. The second goal was to identify genetic variation associated with eating behaviors. Thirdly, this study aimed to examine the joint relationships between eating behavior, body size and genetic variation.^ Methods. This study utilized baseline data ascertained in young adults from the Training Interventions and Genetics of Exercise (TIGER) Study. Variables assessed included eating behavior (Emotional Eating Scale, Eating Attitudes Test-26, and the Block98 Food Frequency Questionnaire), body size (body mass index, waist and hip circumference, waist/hip ratio, and percent body fat), genetic variation in genes implicated related to the hypothalamic control of energy balance, and appropriate covariates (age, gender, race/ethnicity, smoking status, and physical activity. For the genetic association analyses, genotypes were collapsed by minor allele frequency, and haplotypes were estimated for each gene. Additionally, Bayesian networks were constructed in order to determine the relationships between genetic variation, eating behavior and body size.^ Results. We report that the EAT-26 score, Caloric intake, percent fat, fiber intake, HEAT index, and daily servings of vegetables, meats, grains, and fats were significantly associated with at least one body size measure. Multiple SNPs in 17 genes and haplotypes from 12 genes were tested for their association with body size. Variation within both DRD4 and HTR2A was found to be associated with EAT-26 score. In addition, variation in the ghrelin gene (GHRL) was significantly associated with daily Caloric intake. A significant interaction between daily servings of grains and the HEAT index and variation within the leptin receptor gene (LEPR) was shown to influence body size.^ Conclusion. This study has shown that there is a substantial genetic component to eating behavior and that genetic variation interacts with eating behavior to influence body size.^

EVALUATION OF THE EFFECTIVENESS OF ANISOTROPIC ANALYTICAL ALGORITHM IN FLATTENED AND FLATTENING-FILTER-FREE BEAMS FOR HIGH ENERGY LUNG DOSE DELIVERY USING THE RADIOLOGICAL PHYSICS CENTER LUNG PHANTOM

The effectiveness of the Anisotropic Analytical Algorithm (AAA) implemented in the Eclipse treatment planning system (TPS) was evaluated using theRadiologicalPhysicsCenteranthropomorphic lung phantom using both flattened and flattening-filter-free high energy beams. Radiation treatment plans were developed following the Radiation Therapy Oncology Group and theRadiologicalPhysicsCenterguidelines for lung treatment using Stereotactic Radiation Body Therapy. The tumor was covered such that at least 95% of Planning Target Volume (PTV) received 100% of the prescribed dose while ensuring that normal tissue constraints were followed as well. Calculated doses were exported from the Eclipse TPS and compared with the experimental data as measured using thermoluminescence detectors (TLD) and radiochromic films that were placed inside the phantom. The results demonstrate that the AAA superposition-convolution algorithm is able to calculate SBRT treatment plans with all clinically used photon beams in the range from 6 MV to 18 MV. The measured dose distribution showed a good agreement with the calculated distribution using clinically acceptable criteria of ±5% dose or 3mm distance to agreement. These results show that in a heterogeneous environment a 3D pencil beam superposition-convolution algorithms with Monte Carlo pre-calculated scatter kernels, such as AAA, are able to reliably calculate dose, accounting for increased lateral scattering due to the loss of electronic equilibrium in low density medium. The data for high energy plans (15 MV and 18 MV) showed very good tumor coverage in contrast to findings by other investigators for less sophisticated dose calculation algorithms, which demonstrated less than expected tumor doses and generally worse tumor coverage for high energy plans compared to 6MV plans. This demonstrates that the modern superposition-convolution AAA algorithm is a significant improvement over previous algorithms and is able to calculate doses accurately for SBRT treatment plans in the highly heterogeneous environment of the thorax for both lower (≤12 MV) and higher (greater than 12 MV) beam energies.